From acb01c2ca24c8b424d90cea669e895d5e7fabfda Mon Sep 17 00:00:00 2001 From: Harim Kang Date: Tue, 13 Feb 2024 10:18:34 +0900 Subject: [PATCH 1/5] Add Auto-Configuration for OVModel (#2897) * Add initial commit for auto-config ov model * Add auto-config with OV data pipeline * Add intg test api export & test * Add api example * Add CLI link between checkpoint and model.model_name --- for_developers/add_custom_model.ipynb | 215 ++----- for_developers/cli_guide.md | 18 + for_developers/engine_api_example.ipynb | 541 ++++++++++++++++++ src/otx/cli/cli.py | 3 + src/otx/core/model/entity/classification.py | 24 +- src/otx/core/model/entity/detection.py | 20 + .../model/entity/instance_segmentation.py | 20 + src/otx/core/model/entity/segmentation.py | 20 + src/otx/engine/engine.py | 19 +- src/otx/engine/utils/auto_configurator.py | 60 +- tests/integration/api/test_engine_api.py | 23 +- tests/integration/cli/test_cli.py | 10 +- .../integration/cli/test_export_inference.py | 4 +- 13 files changed, 803 insertions(+), 174 deletions(-) create mode 100644 for_developers/engine_api_example.ipynb diff --git a/for_developers/add_custom_model.ipynb b/for_developers/add_custom_model.ipynb index 5110fbae48e..4a6bb1d79be 100644 --- a/for_developers/add_custom_model.ipynb +++ b/for_developers/add_custom_model.ipynb @@ -12,26 +12,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ - "from typing import Any\n", - "\n", - "import torch\n", - "from torch import nn\n", - "from torchvision.models.resnet import resnet50, ResNet50_Weights\n", + "from __future__ import annotations\n", "\n", - "from hydra import compose, initialize\n", + "from typing import TYPE_CHECKING, Any\n", "\n", - "from otx.core.data.entity.base import OTXBatchLossEntity\n", + "import torch\n", "from otx.core.data.entity.classification import (\n", " MulticlassClsBatchDataEntity,\n", " MulticlassClsBatchPredEntity,\n", ")\n", - "from otx.core.model.entity.classification import OTXClassificationModel\n", - "from otx.core.engine.train import train\n", - "from otx.cli.utils.hydra import configure_hydra_outputs\n" + "from otx.core.model.entity.classification import OTXMulticlassClsModel\n", + "from torch import nn\n", + "from torchvision.models.resnet import ResNet50_Weights, resnet50\n", + "\n", + "if TYPE_CHECKING:\n", + " from otx.core.data.entity.base import OTXBatchLossEntity\n" ] }, { @@ -50,7 +49,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -96,14 +95,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ - "class OTXResNet50(OTXClassificationModel):\n", + "class OTXResNet50(OTXMulticlassClsModel):\n", " def __init__(self, num_classes: int) -> None:\n", - " self.num_classes = num_classes\n", - " super().__init__()\n", + " super().__init__(num_classes=num_classes)\n", " self.register_buffer(\n", " \"mean\",\n", " torch.FloatTensor([123.675, 116.28, 103.53]).view(-1, 1, 1),\n", @@ -121,7 +119,7 @@ " return ResNet50WithLossComputation(num_classes=self.num_classes)\n", "\n", " def _customize_inputs(self, inputs: MulticlassClsBatchDataEntity) -> dict[str, Any]:\n", - " images = torch.stack(inputs.images, dim=0).to(dtype=torch.float32)\n", + " images = inputs.images.to(dtype=torch.float32)\n", " images = (images - self.mean) / self.std\n", " return {\n", " \"images\": images,\n", @@ -165,184 +163,95 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:root:The corresponding keys in config are not used.: ['verbose', 'data_root', 'task', 'seed', 'callback_monitor', 'resume', 'disable_infer_num_classes']\n", + "WARNING:root:Set Default Optimizer: {'class_path': 'torch.optim.SGD', 'init_args': {'lr': 0.0049, 'momentum': 0.9, 'dampening': 0, 'weight_decay': 0.0001, 'nesterov': False, 'maximize': False, 'foreach': None, 'differentiable': False}}\n", + "WARNING:root:Set Default Scheduler: {'class_path': 'lightning.pytorch.cli.ReduceLROnPlateau', 'init_args': {'monitor': 'train/loss', 'mode': 'min', 'factor': 0.5, 'patience': 1, 'threshold': 0.0001, 'threshold_mode': 'rel', 'cooldown': 0, 'min_lr': 0, 'eps': 1e-08, 'verbose': False}}\n", + "INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (cuda), used: True\n", + "INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n", + "INFO:pytorch_lightning.utilities.rank_zero:IPU available: False, using: 0 IPUs\n", + "INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n", + "INFO:pytorch_lightning.utilities.rank_zero:You are using a CUDA device ('NVIDIA GeForce RTX 3090') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n", + "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n", + "\n", + " | Name | Type | Params\n", + "---------------------------------------------------\n", + "0 | model | OTXResNet50 | 23.5 M\n", + "1 | val_metric | MulticlassAccuracy | 0 \n", + "2 | test_metric | MulticlassAccuracy | 0 \n", + "---------------------------------------------------\n", + "23.5 M Trainable params\n", + "0 Non-trainable params\n", + "23.5 M Total params\n", + "94.049 Total estimated model params size (MB)\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "[2023-12-08 14:44:09,539][root][INFO] - Instantiating datamodule <{'data_format': 'imagenet_with_subset_dirs', 'data_root': '${base.data_dir}', 'train_subset': {'batch_size': 64, 'subset_name': 'train', 'transform_lib_type': , 'transforms': [{'type': 'LoadImageFromFile'}, {'backend': 'cv2', 'scale': 224, 'type': 'RandomResizedCrop'}, {'type': 'PackInputs'}], 'num_workers': 2}, 'val_subset': {'batch_size': 64, 'subset_name': 'val', 'transform_lib_type': , 'transforms': [{'type': 'LoadImageFromFile'}, {'backend': 'cv2', 'edge': 'short', 'scale': 256, 'type': 'ResizeEdge'}, {'crop_size': 224, 'type': 'CenterCrop'}, {'type': 'PackInputs'}], 'num_workers': 2}, 'test_subset': {'batch_size': 64, 'subset_name': 'test', 'transform_lib_type': , 'transforms': [{'type': 'LoadImageFromFile'}, {'backend': 'cv2', 'edge': 'short', 'scale': 256, 'type': 'ResizeEdge'}, {'crop_size': 224, 'type': 'CenterCrop'}, {'type': 'PackInputs'}], 'num_workers': 2}, 'mem_cache_size': '1GB', 'mem_cache_img_max_size': '${as_int_tuple:500,500}'}>\n", - "[2023-12-08 14:44:09,951][root][INFO] - Add name: val, self.subsets: {'val': }\n", - "[2023-12-08 14:44:09,952][root][INFO] - Add name: test, self.subsets: {'val': , 'test': }\n", - "[2023-12-08 14:44:09,954][root][INFO] - Add name: train, self.subsets: {'val': , 'test': , 'train': }\n", - "[2023-12-08 14:44:09,954][root][INFO] - Try to create a 1000000000 size memory pool.\n", - "[2023-12-08 14:44:10,356][root][INFO] - Instantiating model <{'_target_': 'otx.core.model.module.classification.OTXClassificationLitModule', 'optimizer': {'_target_': 'torch.optim.SGD', '_partial_': True, 'lr': 0.0049, 'weight_decay': 0.0001, 'momentum': 0.9}, 'scheduler': {'_target_': 'torch.optim.lr_scheduler.ReduceLROnPlateau', '_partial_': True, 'mode': 'min', 'factor': 0.5, 'patience': 1}, 'otx_model': {'_target_': 'otx.core.model.entity.classification.MMPretrainCompatibleModel', 'config': {'backbone': {'version': 'b0', 'pretrained': True, 'type': 'OTXEfficientNet'}, 'head': {'act_cfg': {'type': 'HSwish'}, 'dropout_rate': 0.2, 'in_channels': 1280, 'init_cfg': {'bias': 0.0, 'layer': 'Linear', 'mean': 0.0, 'std': 0.01, 'type': 'Normal'}, 'loss': {'loss_weight': 1.0, 'type': 'CrossEntropyLoss'}, 'mid_channels': [1280], 'num_classes': 1000, 'topk': [1, 5], 'type': 'StackedLinearClsHead'}, 'neck': {'type': 'GlobalAveragePooling'}, 'data_preprocessor': {'mean': [123.675, 116.28, 103.53], 'num_classes': 1000, 'std': [58.395, 57.12, 57.375], 'to_rgb': True, 'type': 'ClsDataPreprocessor'}, 'type': 'ImageClassifier'}}, 'torch_compile': False}>\n", - "init weight - https://github.com/osmr/imgclsmob/releases/download/v0.0.364/efficientnet_b0-0752-0e386130.pth.zip\n", - "[2023-12-08 14:44:11,335][root][INFO] - Overriding to this OTX model \n", - "[2023-12-08 14:44:11,335][root][INFO] - Instantiating callbacks...\n", - "[2023-12-08 14:44:11,335][otx.core.engine.utils.instantiators][INFO] - Instantiating callback \n", - "[2023-12-08 14:44:11,337][otx.core.engine.utils.instantiators][INFO] - Instantiating callback \n", - "[2023-12-08 14:44:11,339][otx.core.engine.utils.instantiators][INFO] - Instantiating callback \n", - "[2023-12-08 14:44:11,341][otx.core.engine.utils.instantiators][INFO] - Instantiating callback \n", - "[2023-12-08 14:44:11,341][otx.core.engine.utils.instantiators][INFO] - Instantiating callback \n", - "[2023-12-08 14:44:11,342][root][INFO] - Instantiating loggers...\n", - "[2023-12-08 14:44:11,342][otx.core.engine.utils.instantiators][INFO] - Instantiating logger \n", - "[2023-12-08 14:44:11,343][root][INFO] - Instantiating trainer <{'default_root_dir': '${base.output_dir}', 'accelerator': 'gpu', 'precision': 32, 'max_epochs': 10, 'min_epochs': 1, 'devices': 1, 'check_val_every_n_epoch': 1, 'deterministic': False, '_target_': 'lightning.pytorch.trainer.Trainer'}>\n" + " \r" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Trainer already configured with model summary callbacks: []. Skipping setting a default `ModelSummary` callback.\n", - "GPU available: True (cuda), used: True\n", - "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", - "HPU available: False, using: 0 HPUs\n" + "/home/harimkan/workspace/repo/otx-regression/venv/lib/python3.10/site-packages/lightning/pytorch/loops/fit_loop.py:293: The number of training batches (1) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "[2023-12-08 14:44:11,479][root][INFO] - Logging hyperparameters!\n", - "[2023-12-08 14:44:11,483][root][INFO] - Starting training!\n" + "Epoch 9: 100%|██████████| 1/1 [00:00<00:00, 8.83it/s, v_num=9, train/loss=0.563, val/accuracy=1.000]" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "You are using a CUDA device ('NVIDIA GeForce RTX 3090') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n", - "/home/vinnamki/miniconda3/envs/otx-v2/lib/python3.11/site-packages/lightning/pytorch/callbacks/model_checkpoint.py:639: Checkpoint directory outputs/checkpoints exists and is not empty.\n", - "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n" + "INFO:pytorch_lightning.utilities.rank_zero:`Trainer.fit` stopped: `max_epochs=10` reached.\n" ] }, { - "data": { - "text/html": [ - "
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-       "┃   ┃ Name        ┃ Type               ┃ Params ┃\n",
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-       "│ 0 │ model       │ OTXResNet50        │ 23.5 M │\n",
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Trainable params: 23.5 M                                                                                           \n",
-       "Non-trainable params: 0                                                                                            \n",
-       "Total params: 23.5 M                                                                                               \n",
-       "Total estimated model params size (MB): 94                                                                         \n",
-       "
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/home/vinnamki/miniconda3/envs/otx-v2/lib/python3.11/site-packages/lightning/pytorch/loops/fit_loop.py:293: The \n",
-       "number of training batches (1) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower \n",
-       "value for log_every_n_steps if you want to see logs for the training epoch.\n",
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-      ],
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-     },
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-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "
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-       "
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-      ],
       "text/plain": [
-       "\n"
+       "{'train/loss': tensor(0.5628), 'val/accuracy': tensor(1.)}"
       ]
      },
+     "execution_count": 4,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "num_classes = 2\n",
+    "from otx.engine import Engine\n",
+    "\n",
     "data_dir = \"../tests/assets/classification_dataset\"\n",
+    "otx_model = OTXResNet50(num_classes=2)\n",
     "\n",
-    "with initialize(\n",
-    "    config_path=\"../src/otx/config\", version_base=\"1.3\", job_name=\"otx_train\"\n",
-    "):\n",
-    "    overrides = [\n",
-    "        \"+recipe=classification/otx_efficientnet_b0\",\n",
-    "        \"base.output_dir=outputs\",\n",
-    "        \"trainer.accelerator=gpu\",\n",
-    "        f\"base.data_dir={data_dir}\",\n",
-    "    ]\n",
-    "    cfg = compose(config_name=\"train\", overrides=overrides, return_hydra_config=True)\n",
-    "    configure_hydra_outputs(cfg)\n",
+    "engine = Engine(\n",
+    "    data_root=data_dir,\n",
+    "    model=otx_model,\n",
+    "    device=\"gpu\",\n",
+    "    work_dir=\"otx-workspace\",\n",
+    ")\n",
     "\n",
-    "    otx_model = OTXResNet50(num_classes=num_classes)\n",
-    "    train(cfg, otx_model=otx_model)\n"
+    "engine.train(max_epochs=10)"
    ]
   },
   {
@@ -369,7 +278,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.5"
+   "version": "3.10.13"
   }
  },
  "nbformat": 4,
diff --git a/for_developers/cli_guide.md b/for_developers/cli_guide.md
index 717a1d91afb..63465367d7f 100644
--- a/for_developers/cli_guide.md
+++ b/for_developers/cli_guide.md
@@ -203,6 +203,24 @@ Override Parameters
 otx train ... --model.num_classes  --max_epochs 
 ```
 
+Testing with checkpoint
+
+```console
+otx test ... --checkpoint 
+```
+
+Export to OpenVINO IR model or ONNX (Default="OPENVINO")
+
+```console
+otx export ... --checkpoint  --export_format 
+```
+
+Testing with Exported model output
+
+```console
+otx test ... --checkpoint 
+```
+
 ## How to write OTX Configuration (recipe)
 
 ### Configuration
diff --git a/for_developers/engine_api_example.ipynb b/for_developers/engine_api_example.ipynb
new file mode 100644
index 00000000000..e4bb524fe69
--- /dev/null
+++ b/for_developers/engine_api_example.ipynb
@@ -0,0 +1,541 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# How to use OTX Engine\n",
+    "\n",
+    "## Installation\n",
+    "\n",
+    "Please see [setup_guide.md](setup_guide.md).\n",
+    "\n",
+    "## Engine Usage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/harimkan/workspace/repo/otx-regression/venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
+   "source": [
+    "from otx.engine import Engine"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Training with dataset path (Auto-Configuration)\n",
+    "\n",
+    "- Auto-Configurator detect task from data_root\n",
+    "- Auto-Configurator select default model, data-transform, optimizer, scheduler"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:The corresponding keys in config are not used.: ['verbose', 'data_root', 'task', 'seed', 'callback_monitor', 'resume', 'disable_infer_num_classes']\n",
+      "WARNING:root:Set Default Model: {'class_path': 'otx.algo.classification.efficientnet_b0.EfficientNetB0ForMulticlassCls', 'init_args': {'num_classes': 2, 'light': False}}\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "init weight - https://github.com/osmr/imgclsmob/releases/download/v0.0.364/efficientnet_b0-0752-0e386130.pth.zip\n",
+      "init weight - https://github.com/osmr/imgclsmob/releases/download/v0.0.364/efficientnet_b0-0752-0e386130.pth.zip\n",
+      "init weight - https://github.com/osmr/imgclsmob/releases/download/v0.0.364/efficientnet_b0-0752-0e386130.pth.zip\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:Set Default Optimizer: {'class_path': 'torch.optim.SGD', 'init_args': {'lr': 0.0049, 'momentum': 0.9, 'dampening': 0, 'weight_decay': 0.0001, 'nesterov': False, 'maximize': False, 'foreach': None, 'differentiable': False}}\n",
+      "WARNING:root:Set Default Scheduler: {'class_path': 'lightning.pytorch.cli.ReduceLROnPlateau', 'init_args': {'monitor': 'train/loss', 'mode': 'min', 'factor': 0.5, 'patience': 1, 'threshold': 0.0001, 'threshold_mode': 'rel', 'cooldown': 0, 'min_lr': 0, 'eps': 1e-08, 'verbose': False}}\n",
+      "INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (cuda), used: True\n",
+      "INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
+      "INFO:pytorch_lightning.utilities.rank_zero:IPU available: False, using: 0 IPUs\n",
+      "INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
+      "INFO:pytorch_lightning.utilities.rank_zero:You are using a CUDA device ('NVIDIA GeForce RTX 3090') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n",
+      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n",
+      "\n",
+      "  | Name        | Type                           | Params\n",
+      "---------------------------------------------------------------\n",
+      "0 | model       | EfficientNetB0ForMulticlassCls | 5.6 M \n",
+      "1 | val_metric  | MulticlassAccuracy             | 0     \n",
+      "2 | test_metric | MulticlassAccuracy             | 0     \n",
+      "---------------------------------------------------------------\n",
+      "5.6 M     Trainable params\n",
+      "0         Non-trainable params\n",
+      "5.6 M     Total params\n",
+      "22.599    Total estimated model params size (MB)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                                                                           \r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/harimkan/workspace/repo/otx-regression/venv/lib/python3.10/site-packages/lightning/pytorch/loops/fit_loop.py:293: The number of training batches (1) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1: 100%|██████████| 1/1 [00:00<00:00,  9.97it/s, v_num=10, train/loss=0.692, val/accuracy=0.680]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:pytorch_lightning.utilities.rank_zero:`Trainer.fit` stopped: `max_epochs=2` reached.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1: 100%|██████████| 1/1 [00:00<00:00,  5.66it/s, v_num=10, train/loss=0.692, val/accuracy=0.680]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'train/loss': tensor(0.6917), 'val/accuracy': tensor(0.6800)}"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_root = \"../tests/assets/classification_dataset\"\n",
+    "\n",
+    "engine = Engine(data_root=data_root)\n",
+    "\n",
+    "engine.train(max_epochs=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Training with Custom OTXModel\n",
+    "\n",
+    "Please see [add_custom_model.ipynb](add_custom_model.ipynb).\n",
+    "\n",
+    "```python\n",
+    "# Inherited Class from otx.core.model.entity.base.OTXModel\n",
+    "custom_model = CustomOTXModel(...)\n",
+    "\n",
+    "engine = Engine(data_root=data_root, model=custom_model)\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Training with OTX Model (model_name: str)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['otx_efficientnet_v2',\n",
+       " 'openvino_model',\n",
+       " 'otx_dino_v2_linear_probe',\n",
+       " 'mobilenet_v3_large_light',\n",
+       " 'efficientnet_b0_light',\n",
+       " 'otx_mobilenet_v3_large',\n",
+       " 'efficientnet_v2_light',\n",
+       " 'otx_dino_v2',\n",
+       " 'otx_efficientnet_b0',\n",
+       " 'otx_deit_tiny']"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from otx.engine.utils.api import list_models\n",
+    "\n",
+    "list_models(task=\"MULTI_CLASS_CLS\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:The corresponding keys in config are not used.: ['verbose', 'data_root', 'task', 'seed', 'callback_monitor', 'resume', 'disable_infer_num_classes']\n",
+      "WARNING:root:Set Default Model: {'class_path': 'otx.algo.classification.mobilenet_v3_large.MobileNetV3ForMulticlassCls', 'init_args': {'num_classes': 2, 'light': True}}\n",
+      "WARNING:root:Set Default Optimizer: {'class_path': 'torch.optim.SGD', 'init_args': {'lr': 0.0058, 'momentum': 0.9, 'dampening': 0, 'weight_decay': 0.0001, 'nesterov': False, 'maximize': False, 'foreach': None, 'differentiable': False}}\n",
+      "WARNING:root:Set Default Scheduler: {'class_path': 'otx.algo.schedulers.WarmupReduceLROnPlateau', 'init_args': {'warmup_steps': 10, 'monitor': 'val/accuracy', 'mode': 'max', 'factor': 0.5, 'patience': 1, 'threshold': 0.0001, 'threshold_mode': 'rel', 'cooldown': 0, 'min_lr': 0, 'eps': 1e-08, 'verbose': False}}\n",
+      "INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (cuda), used: True\n",
+      "INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
+      "INFO:pytorch_lightning.utilities.rank_zero:IPU available: False, using: 0 IPUs\n",
+      "INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
+      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n",
+      "\n",
+      "  | Name        | Type                        | Params\n",
+      "------------------------------------------------------------\n",
+      "0 | model       | MobileNetV3ForMulticlassCls | 3.0 M \n",
+      "1 | val_metric  | MulticlassAccuracy          | 0     \n",
+      "2 | test_metric | MulticlassAccuracy          | 0     \n",
+      "------------------------------------------------------------\n",
+      "3.0 M     Trainable params\n",
+      "0         Non-trainable params\n",
+      "3.0 M     Total params\n",
+      "11.895    Total estimated model params size (MB)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loads checkpoint by http backend from path: https://github.com/d-li14/mobilenetv3.pytorch/blob/master/pretrained/mobilenetv3-large-1cd25616.pth?raw=true\n",
+      "The model and loaded state dict do not match exactly\n",
+      "\n",
+      "unexpected key in source state_dict: classifier.0.weight, classifier.0.bias, classifier.3.weight, classifier.3.bias\n",
+      "\n",
+      "init weight - https://github.com/d-li14/mobilenetv3.pytorch/blob/master/pretrained/mobilenetv3-large-1cd25616.pth?raw=true\n",
+      "Loads checkpoint by http backend from path: https://github.com/d-li14/mobilenetv3.pytorch/blob/master/pretrained/mobilenetv3-large-1cd25616.pth?raw=true\n",
+      "The model and loaded state dict do not match exactly\n",
+      "\n",
+      "unexpected key in source state_dict: classifier.0.weight, classifier.0.bias, classifier.3.weight, classifier.3.bias\n",
+      "\n",
+      "init weight - https://github.com/d-li14/mobilenetv3.pytorch/blob/master/pretrained/mobilenetv3-large-1cd25616.pth?raw=true\n",
+      "Loads checkpoint by http backend from path: https://github.com/d-li14/mobilenetv3.pytorch/blob/master/pretrained/mobilenetv3-large-1cd25616.pth?raw=true\n",
+      "The model and loaded state dict do not match exactly\n",
+      "\n",
+      "unexpected key in source state_dict: classifier.0.weight, classifier.0.bias, classifier.3.weight, classifier.3.bias\n",
+      "\n",
+      "init weight - https://github.com/d-li14/mobilenetv3.pytorch/blob/master/pretrained/mobilenetv3-large-1cd25616.pth?raw=true\n",
+      "Epoch 1: 100%|██████████| 1/1 [00:00<00:00, 12.68it/s, v_num=11, train/loss=0.809, val/accuracy=0.520]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:pytorch_lightning.utilities.rank_zero:`Trainer.fit` stopped: `max_epochs=2` reached.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1: 100%|██████████| 1/1 [00:00<00:00,  8.37it/s, v_num=11, train/loss=0.809, val/accuracy=0.520]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'train/loss': tensor(0.8091), 'val/accuracy': tensor(0.5200)}"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "engine = Engine(data_root=data_root, model=\"mobilenet_v3_large_light\")\n",
+    "\n",
+    "engine.train(max_epochs=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Training with OTX configuration file\n",
+    "- Users can override configuration values when creating an Engin.from_config.\n",
+    "- Or Users can also modify the configuration file directly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:The corresponding keys in config are not used.: ['verbose', 'data_root', 'task', 'seed', 'callback_monitor', 'resume', 'disable_infer_num_classes']\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "init weight - https://github.com/osmr/imgclsmob/releases/download/v0.0.364/efficientnet_b0-0752-0e386130.pth.zip\n",
+      "init weight - https://github.com/osmr/imgclsmob/releases/download/v0.0.364/efficientnet_b0-0752-0e386130.pth.zip\n",
+      "init weight - https://github.com/osmr/imgclsmob/releases/download/v0.0.364/efficientnet_b0-0752-0e386130.pth.zip\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (cuda), used: True\n",
+      "INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
+      "INFO:pytorch_lightning.utilities.rank_zero:IPU available: False, using: 0 IPUs\n",
+      "INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
+      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n",
+      "\n",
+      "  | Name        | Type                           | Params\n",
+      "---------------------------------------------------------------\n",
+      "0 | model       | EfficientNetB0ForMulticlassCls | 5.6 M \n",
+      "1 | val_metric  | MulticlassAccuracy             | 0     \n",
+      "2 | test_metric | MulticlassAccuracy             | 0     \n",
+      "---------------------------------------------------------------\n",
+      "5.6 M     Trainable params\n",
+      "0         Non-trainable params\n",
+      "5.6 M     Total params\n",
+      "22.599    Total estimated model params size (MB)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1: 100%|██████████| 1/1 [00:00<00:00,  9.87it/s, v_num=12, train/loss=0.697, val/accuracy=0.440]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:pytorch_lightning.utilities.rank_zero:`Trainer.fit` stopped: `max_epochs=2` reached.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1: 100%|██████████| 1/1 [00:00<00:00,  5.55it/s, v_num=12, train/loss=0.697, val/accuracy=0.440]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'train/loss': tensor(0.6972), 'val/accuracy': tensor(0.4400)}"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from otx.engine import Engine\n",
+    "\n",
+    "config = \"../src/otx/recipe/classification/multi_class_cls/otx_efficientnet_b0.yaml\"\n",
+    "\n",
+    "engine = Engine.from_config(\n",
+    "    config_path=config,\n",
+    "    data_root=data_root,\n",
+    ")\n",
+    "\n",
+    "engine.train(max_epochs=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Testing DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 61.30it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
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+       "┃\u001b[1m \u001b[0m\u001b[1m       Test metric       \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      DataLoader 0       \u001b[0m\u001b[1m \u001b[0m┃\n",
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+       "│\u001b[36m \u001b[0m\u001b[36m      test/accuracy      \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.4000000059604645    \u001b[0m\u001b[35m \u001b[0m│\n",
+       "└───────────────────────────┴───────────────────────────┘\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'test/accuracy': tensor(0.4000)}"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "engine.test()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PosixPath('otx-workspace/exported_model.xml')"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "exported_model_path = engine.export() # export_format=\"OPENVINO\" is default\n",
+    "exported_model_path"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:The corresponding keys in config are not used.: ['verbose', 'data_root', 'task', 'seed', 'callback_monitor', 'resume', 'disable_infer_num_classes']\n",
+      "/home/harimkan/workspace/repo/otx-regression/src/otx/core/utils/build.py:52: UserWarning: Set the default number of OpenVINO inference requests to 8.\n",
+      "            You can specify the value in config.\n",
+      "  warnings.warn(msg, stacklevel=1)\n",
+      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Testing DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 11.92it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
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+       "┃        Test metric        ┃       DataLoader 0        ┃\n",
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+       "│       test/accuracy       │    0.4000000059604645     │\n",
+       "└───────────────────────────┴───────────────────────────┘\n",
+       "
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+      "text/plain": [
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+       "┃\u001b[1m \u001b[0m\u001b[1m       Test metric       \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      DataLoader 0       \u001b[0m\u001b[1m \u001b[0m┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
+       "│\u001b[36m \u001b[0m\u001b[36m      test/accuracy      \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.4000000059604645    \u001b[0m\u001b[35m \u001b[0m│\n",
+       "└───────────────────────────┴───────────────────────────┘\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'test/accuracy': tensor(0.4000)}"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Using Exsting Engine for OV Testing\n",
+    "engine.test(checkpoint=exported_model_path)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/src/otx/cli/cli.py b/src/otx/cli/cli.py
index 7b3f8579035..2c7159f990a 100644
--- a/src/otx/cli/cli.py
+++ b/src/otx/cli/cli.py
@@ -231,6 +231,9 @@ def add_subcommands(self) -> None:
             if "callbacks" in added_arguments:
                 sub_parser.link_arguments("callback_monitor", "callbacks.init_args.monitor")
                 sub_parser.link_arguments("engine.work_dir", "callbacks.init_args.dirpath")
+            if "checkpoint" in added_arguments and "--checkpoint" in sys.argv:
+                # This is code for an OVModel that uses checkpoint in model.model_name.
+                sub_parser.link_arguments("checkpoint", "model.init_args.model_name")
 
             # Load default subcommand config file
             default_config_file = get_otx_root_path() / "recipe" / "_base_" / f"{subcommand}.yaml"
diff --git a/src/otx/core/model/entity/classification.py b/src/otx/core/model/entity/classification.py
index 9361f35926f..d07fb96ed34 100644
--- a/src/otx/core/model/entity/classification.py
+++ b/src/otx/core/model/entity/classification.py
@@ -498,6 +498,26 @@ class OVMulticlassClassificationModel(
     and create the OTX classification model compatible for OTX testing pipeline.
     """
 
+    def __init__(
+        self,
+        num_classes: int,
+        model_name: str,
+        model_type: str = "Classification",
+        async_inference: bool = True,
+        max_num_requests: int | None = None,
+        use_throughput_mode: bool = False,
+        model_api_configuration: dict[str, Any] | None = None,
+    ) -> None:
+        super().__init__(
+            num_classes,
+            model_name,
+            model_type,
+            async_inference,
+            max_num_requests,
+            use_throughput_mode,
+            model_api_configuration,
+        )
+
     def _customize_outputs(
         self,
         outputs: list[ClassificationResult],
@@ -528,7 +548,7 @@ def __init__(
         self,
         num_classes: int,
         model_name: str,
-        model_type: str,
+        model_type: str = "Classification",
         async_inference: bool = True,
         max_num_requests: int | None = None,
         use_throughput_mode: bool = True,
@@ -590,7 +610,7 @@ def __init__(
         self,
         num_classes: int,
         model_name: str,
-        model_type: str,
+        model_type: str = "Classification",
         async_inference: bool = True,
         max_num_requests: int | None = None,
         use_throughput_mode: bool = True,
diff --git a/src/otx/core/model/entity/detection.py b/src/otx/core/model/entity/detection.py
index 2fb8a7fcb00..1dbfd24e2c9 100644
--- a/src/otx/core/model/entity/detection.py
+++ b/src/otx/core/model/entity/detection.py
@@ -283,6 +283,26 @@ class OVDetectionModel(OVModel[DetBatchDataEntity, DetBatchPredEntity]):
     and create the OTX detection model compatible for OTX testing pipeline.
     """
 
+    def __init__(
+        self,
+        num_classes: int,
+        model_name: str,
+        model_type: str = "SSD",
+        async_inference: bool = True,
+        max_num_requests: int | None = None,
+        use_throughput_mode: bool = True,
+        model_api_configuration: dict[str, Any] | None = None,
+    ) -> None:
+        super().__init__(
+            num_classes,
+            model_name,
+            model_type,
+            async_inference,
+            max_num_requests,
+            use_throughput_mode,
+            model_api_configuration,
+        )
+
     def _customize_outputs(
         self,
         outputs: list[DetectionResult],
diff --git a/src/otx/core/model/entity/instance_segmentation.py b/src/otx/core/model/entity/instance_segmentation.py
index a75668d4d30..124046ed82b 100644
--- a/src/otx/core/model/entity/instance_segmentation.py
+++ b/src/otx/core/model/entity/instance_segmentation.py
@@ -280,6 +280,26 @@ class OVInstanceSegmentationModel(
     and create the OTX detection model compatible for OTX testing pipeline.
     """
 
+    def __init__(
+        self,
+        num_classes: int,
+        model_name: str,
+        model_type: str = "MaskRCNN",
+        async_inference: bool = True,
+        max_num_requests: int | None = None,
+        use_throughput_mode: bool = True,
+        model_api_configuration: dict[str, Any] | None = None,
+    ) -> None:
+        super().__init__(
+            num_classes,
+            model_name,
+            model_type,
+            async_inference,
+            max_num_requests,
+            use_throughput_mode,
+            model_api_configuration,
+        )
+
     def _customize_outputs(
         self,
         outputs: list[InstanceSegmentationResult],
diff --git a/src/otx/core/model/entity/segmentation.py b/src/otx/core/model/entity/segmentation.py
index f49c7648018..79c2d5d63e1 100644
--- a/src/otx/core/model/entity/segmentation.py
+++ b/src/otx/core/model/entity/segmentation.py
@@ -159,6 +159,26 @@ class OVSegmentationModel(OVModel[SegBatchDataEntity, SegBatchPredEntity]):
     and create the OTX segmentation model compatible for OTX testing pipeline.
     """
 
+    def __init__(
+        self,
+        num_classes: int,
+        model_name: str,
+        model_type: str = "Segmentation",
+        async_inference: bool = True,
+        max_num_requests: int | None = None,
+        use_throughput_mode: bool = True,
+        model_api_configuration: dict[str, Any] | None = None,
+    ) -> None:
+        super().__init__(
+            num_classes,
+            model_name,
+            model_type,
+            async_inference,
+            max_num_requests,
+            use_throughput_mode,
+            model_api_configuration,
+        )
+
     def _customize_outputs(
         self,
         outputs: list[ImageResultWithSoftPrediction],
diff --git a/src/otx/engine/engine.py b/src/otx/engine/engine.py
index 8e246db18b8..fffe06a1f23 100644
--- a/src/otx/engine/engine.py
+++ b/src/otx/engine/engine.py
@@ -15,7 +15,7 @@
 from otx.core.config.device import DeviceConfig
 from otx.core.config.explain import ExplainConfig
 from otx.core.data.module import OTXDataModule
-from otx.core.model.entity.base import OTXModel
+from otx.core.model.entity.base import OTXModel, OVModel
 from otx.core.model.module.base import OTXLitModule
 from otx.core.types.device import DeviceType
 from otx.core.types.export import OTXExportFormatType
@@ -276,19 +276,25 @@ def test(
                 otx test --config  --checkpoint 
                 ```
         """
+        model = self.model
+        checkpoint = checkpoint if checkpoint is not None else self.checkpoint
+        datamodule = datamodule if datamodule is not None else self.datamodule
+
+        is_ir_ckpt = Path(str(checkpoint)).suffix in [".xml", ".onnx"]
+        if is_ir_ckpt and not isinstance(model, OVModel):
+            datamodule = self._auto_configurator.get_ov_datamodule()
+            model = self._auto_configurator.get_ov_model(model_name=str(checkpoint), meta_info=datamodule.meta_info)
+
         lit_module = self._build_lightning_module(
-            model=self.model,
+            model=model,
             optimizer=self.optimizer,
             scheduler=self.scheduler,
         )
-        if datamodule is None:
-            datamodule = self.datamodule
         lit_module.meta_info = datamodule.meta_info
 
         # NOTE, trainer.test takes only lightning based checkpoint.
         # So, it can't take the OTX1.x checkpoint.
-        checkpoint = checkpoint if checkpoint is not None else self.checkpoint
-        if checkpoint is not None:
+        if checkpoint is not None and not is_ir_ckpt:
             loaded_checkpoint = torch.load(checkpoint)
             lit_module.load_state_dict(loaded_checkpoint)
 
@@ -580,6 +586,7 @@ def from_config(cls, config_path: PathLike, data_root: PathLike | None = None, *
 
         engine_config = {**config.pop("engine"), **config}
         engine_config.update(kwargs)
+        engine_config["data_root"] = data_root
         return cls(
             datamodule=datamodule,
             model=model,
diff --git a/src/otx/engine/utils/auto_configurator.py b/src/otx/engine/utils/auto_configurator.py
index 02304493544..98f7f3506e1 100644
--- a/src/otx/engine/utils/auto_configurator.py
+++ b/src/otx/engine/utils/auto_configurator.py
@@ -16,6 +16,7 @@
 from otx.core.config.data import DataModuleConfig, SubsetConfig, TilerConfig
 from otx.core.data.dataset.base import LabelInfo
 from otx.core.data.module import OTXDataModule
+from otx.core.model.entity.base import OVModel
 from otx.core.types.task import OTXTaskType
 from otx.core.utils.imports import get_otx_root_path
 from otx.core.utils.instantiators import partial_instantiate_class
@@ -64,6 +65,16 @@
     "ava": [OTXTaskType.ACTION_DETECTION],
 }
 
+OVMODEL_PER_TASK = {
+    OTXTaskType.MULTI_CLASS_CLS: "otx.core.model.entity.classification.OVMulticlassClassificationModel",
+    OTXTaskType.MULTI_LABEL_CLS: "otx.core.model.entity.classification.OVMultilabelClassificationModel",
+    OTXTaskType.H_LABEL_CLS: "otx.core.model.entity.classification.OVHlabelClassificationModel",
+    OTXTaskType.DETECTION: "otx.core.model.entity.detection.OVDetectionModel",
+    OTXTaskType.ROTATED_DETECTION: "otx.core.model.entity.rotated_detection.OVRotatedDetectionModel",
+    OTXTaskType.INSTANCE_SEGMENTATION: "otx.core.model.entity.instance_segmentation.OVInstanceSegmentationModel",
+    OTXTaskType.SEMANTIC_SEGMENTATION: "otx.core.model.entity.segmentation.OVSegmentationModel",
+}
+
 
 def configure_task(data_root: PathLike) -> OTXTaskType:
     """Configures the task based on the given data root.
@@ -170,11 +181,11 @@ def _load_default_config(self, model_name: str | None = None) -> dict:
             dict: The loaded configuration.
 
         Raises:
-            ValueError: If the task is not supported for auto-configuration.
+            ValueError: If the task doesn't supported for auto-configuration.
         """
         config_file = DEFAULT_CONFIG_PER_TASK.get(self.task, None)
         if config_file is None:
-            msg = f"{self.task} is not support Auto-Configuration."
+            msg = f"{self.task} doesn't support Auto-Configuration."
             raise ValueError(msg)
         if model_name is not None:
             model_path = str(config_file).split("/")
@@ -257,3 +268,48 @@ def get_scheduler(self) -> LRSchedulerCallable | None:
         scheduler_config = self.config.get("scheduler", None)
         logger.warning(f"Set Default Scheduler: {scheduler_config}")
         return partial_instantiate_class(init=scheduler_config)
+
+    def get_ov_model(self, model_name: str, meta_info: LabelInfo) -> OVModel:
+        """Retrieves the OVModel instance based on the given model name and label information.
+
+        Args:
+            model_name (str): The name of the model.
+            meta_info (LabelInfo): The label information.
+
+        Returns:
+            OVModel: The OVModel instance.
+
+        Raises:
+            NotImplementedError: If the OVModel for the given task is not supported.
+        """
+        class_path = OVMODEL_PER_TASK.get(self.task, None)
+        if class_path is None:
+            msg = f"{self.task} is not support OVModel."
+            raise NotImplementedError(msg)
+        class_module, class_name = class_path.rsplit(".", 1)
+        module = __import__(class_module, fromlist=[class_name])
+        ov_model = getattr(module, class_name)
+        return ov_model(
+            model_name=model_name,
+            num_classes=meta_info.num_classes,
+        )
+
+    def get_ov_datamodule(self) -> OTXDataModule:
+        """Returns an instance of OTXDataModule configured with the specified data root and data module configuration.
+
+        Returns:
+            OTXDataModule: An instance of OTXDataModule.
+        """
+        config = self._load_default_config(model_name="openvino_model")
+        config["data"]["config"]["data_root"] = self.data_root
+        data_config = config["data"]["config"].copy()
+        return OTXDataModule(
+            task=config["data"]["task"],
+            config=DataModuleConfig(
+                train_subset=SubsetConfig(**data_config.pop("train_subset")),
+                val_subset=SubsetConfig(**data_config.pop("val_subset")),
+                test_subset=SubsetConfig(**data_config.pop("test_subset")),
+                tile_config=TilerConfig(**data_config.pop("tile_config", {})),
+                **data_config,
+            ),
+        )
diff --git a/tests/integration/api/test_engine_api.py b/tests/integration/api/test_engine_api.py
index 39042b1665d..51570d69c29 100644
--- a/tests/integration/api/test_engine_api.py
+++ b/tests/integration/api/test_engine_api.py
@@ -8,7 +8,7 @@
 from otx.core.model.entity.base import OTXModel
 from otx.core.types.task import OTXTaskType
 from otx.engine import Engine
-from otx.engine.utils.auto_configurator import DEFAULT_CONFIG_PER_TASK
+from otx.engine.utils.auto_configurator import DEFAULT_CONFIG_PER_TASK, OVMODEL_PER_TASK
 
 
 @pytest.mark.parametrize("task", list(DEFAULT_CONFIG_PER_TASK))
@@ -47,3 +47,24 @@ def test_engine_from_config(
 
     test_metric = engine.test()
     assert len(test_metric) > 0
+
+    # A Task that doesn't have Export implemented yet.
+    # [TODO]: Enable should progress for all Tasks.
+    if task in [
+        OTXTaskType.ACTION_CLASSIFICATION,
+        OTXTaskType.ACTION_DETECTION,
+        OTXTaskType.H_LABEL_CLS,
+        OTXTaskType.ROTATED_DETECTION,
+        OTXTaskType.VISUAL_PROMPTING,
+        OTXTaskType.ZERO_SHOT_VISUAL_PROMPTING,
+    ]:
+        return
+
+    # Export IR Model
+    exported_model_path = engine.export()
+    assert exported_model_path.exists()
+
+    # Test with IR Model
+    if task in OVMODEL_PER_TASK:
+        test_metric_from_ov_model = engine.test(checkpoint=exported_model_path, accelerator="cpu")
+        assert len(test_metric_from_ov_model) > 0
diff --git a/tests/integration/cli/test_cli.py b/tests/integration/cli/test_cli.py
index e2ea19952f9..4b0d53957bc 100644
--- a/tests/integration/cli/test_cli.py
+++ b/tests/integration/cli/test_cli.py
@@ -154,19 +154,13 @@ def test_otx_e2e(
         assert (tmp_path_test / "outputs" / f"{format_to_file[fmt]}").exists()
 
     # 4) infer of the exported models
-    task = recipe.split("/")[-2]
-    tmp_path_test = tmp_path / f"otx_test_{model_name}"
-    if "_cls" in recipe:
-        export_test_recipe = f"src/otx/recipe/classification/{task}/openvino_model.yaml"
-    else:
-        export_test_recipe = f"src/otx/recipe/{task}/openvino_model.yaml"
     exported_model_path = str(tmp_path_test / "outputs" / "exported_model.xml")
 
     command_cfg = [
         "otx",
         "test",
         "--config",
-        export_test_recipe,
+        recipe,
         "--data_root",
         fxt_target_dataset_per_task[task],
         "--engine.work_dir",
@@ -174,7 +168,7 @@ def test_otx_e2e(
         "--engine.device",
         "cpu",
         *fxt_cli_override_command_per_task[task],
-        "--model.model_name",
+        "--checkpoint",
         exported_model_path,
     ]
 
diff --git a/tests/integration/cli/test_export_inference.py b/tests/integration/cli/test_export_inference.py
index 2c1b571cd83..e4ad87fe97d 100644
--- a/tests/integration/cli/test_export_inference.py
+++ b/tests/integration/cli/test_export_inference.py
@@ -189,7 +189,7 @@ def test_otx_export_infer(
         "--engine.device",
         "cpu",
         *fxt_cli_override_command_per_task[task],
-        "--model.model_name",
+        "--checkpoint",
         exported_model_path,
     ]
 
@@ -232,7 +232,7 @@ def test_otx_export_infer(
         "--engine.device",
         "cpu",
         *fxt_cli_override_command_per_task[task],
-        "--model.model_name",
+        "--checkpoint",
         exported_model_path,
     ]
 

From 48e667674e90914cb6d25aacaf24b95f86d8f1fe Mon Sep 17 00:00:00 2001
From: Prokofiev Kirill 
Date: Tue, 13 Feb 2024 09:29:31 +0100
Subject: [PATCH 2/5] Add Dice metric for semantic segmentation (#2877)

* add raising an error when metric is None

* added Dice. Need to debug

* updated other models

* align metric

* change mIoU to Dice in tests

* fix monitor

* minor fix

* fix monitor callback
---
 src/otx/core/data/dataset/segmentation.py     | 10 +++++++---
 src/otx/core/model/module/segmentation.py     | 20 +++++++++----------
 src/otx/engine/engine.py                      |  1 -
 .../recipe/semantic_segmentation/dino_v2.yaml |  2 +-
 .../semantic_segmentation/litehrnet_18.yaml   |  4 ++--
 .../semantic_segmentation/litehrnet_s.yaml    |  4 ++--
 .../semantic_segmentation/litehrnet_x.yaml    |  4 ++--
 .../semantic_segmentation/openvino_model.yaml |  2 +-
 .../semantic_segmentation/segnext_b.yaml      |  2 +-
 .../semantic_segmentation/segnext_s.yaml      |  2 +-
 .../semantic_segmentation/segnext_t.yaml      |  2 +-
 .../integration/cli/test_export_inference.py  |  2 +-
 12 files changed, 29 insertions(+), 26 deletions(-)

diff --git a/src/otx/core/data/dataset/segmentation.py b/src/otx/core/data/dataset/segmentation.py
index a9b47957c33..f95e7f6ffdc 100644
--- a/src/otx/core/data/dataset/segmentation.py
+++ b/src/otx/core/data/dataset/segmentation.py
@@ -72,7 +72,8 @@ def __init__(
     def _get_item_impl(self, index: int) -> SegDataEntity | None:
         item = self.dm_subset.get(id=self.ids[index], subset=self.dm_subset.name)
         img = item.media_as(Image)
-        ignored_labels: list[int] = []  # This should be assigned form item
+        num_classes = self.meta_info.num_classes
+        ignored_labels: list[int] = []
         img_data, img_shape = self._get_img_data_and_shape(img)
 
         # create 2D class mask. We use np.sum() since Datumaro returns 3D masks (one for each class)
@@ -81,7 +82,10 @@ def _get_item_impl(self, index: int) -> SegDataEntity | None:
             axis=0,
             dtype=np.uint8,
         )
-
+        mask = torch.as_tensor(mask_anns, dtype=torch.long)
+        # assign possible ignored labels from dataset to max label class + 1.
+        # it is needed to compute mDice metric.
+        mask[mask == 255] = num_classes
         entity = SegDataEntity(
             image=img_data,
             img_info=ImageInfo(
@@ -92,7 +96,7 @@ def _get_item_impl(self, index: int) -> SegDataEntity | None:
                 ignored_labels=ignored_labels,
             ),
             gt_seg_map=tv_tensors.Mask(
-                torch.as_tensor(mask_anns, dtype=torch.long),
+                mask,
             ),
         )
         return self._apply_transforms(entity)
diff --git a/src/otx/core/model/module/segmentation.py b/src/otx/core/model/module/segmentation.py
index e294a09126f..de028c5c40a 100644
--- a/src/otx/core/model/module/segmentation.py
+++ b/src/otx/core/model/module/segmentation.py
@@ -9,7 +9,7 @@
 
 import torch
 from torch import Tensor
-from torchmetrics import JaccardIndex
+from torchmetrics import Dice
 
 from otx.core.data.entity.segmentation import (
     SegBatchDataEntity,
@@ -40,18 +40,18 @@ def __init__(
         )
         num_classes = otx_model.num_classes
         if num_classes is None:
-            msg = """JaccardIndex metric cannot be used with num_classes = None.
+            msg = """Dice metric cannot be used with num_classes = None.
             Please, specify number of classes in config."""
             raise RuntimeError(msg)
 
         metric_params = {
-            "task": "multiclass",
-            "num_classes": num_classes,
-            "ignore_index": 255,
+            # a hack to use ignore_index in Dice metric
+            "num_classes": num_classes + 1,
+            "ignore_index": num_classes,
         }
 
-        self.val_metric = JaccardIndex(**metric_params)
-        self.test_metric = JaccardIndex(**metric_params)
+        self.val_metric = Dice(**metric_params)
+        self.test_metric = Dice(**metric_params)
 
     def on_validation_epoch_start(self) -> None:
         """Callback triggered when the validation epoch starts."""
@@ -69,7 +69,7 @@ def on_test_epoch_end(self) -> None:
         """Callback triggered when the test epoch ends."""
         self._log_metrics(self.test_metric, "test")
 
-    def _log_metrics(self, meter: JaccardIndex, key: str) -> None:
+    def _log_metrics(self, meter: Dice, key: str) -> None:
         results = meter.compute()
         if results is None:
             msg = f"{meter} has no data to compute metric or there is an error computing metric"
@@ -80,7 +80,7 @@ def _log_metrics(self, meter: JaccardIndex, key: str) -> None:
                 log.debug("Cannot log Tensor which is not scalar")
                 return
             self.log(
-                f"{key}/mIoU",
+                f"{key}/{type(meter).__name__}",
                 results,
                 sync_dist=True,
                 prog_bar=True,
@@ -134,4 +134,4 @@ def test_step(self, inputs: SegBatchDataEntity, batch_idx: int) -> None:
     @property
     def lr_scheduler_monitor_key(self) -> str:
         """Metric name that the learning rate scheduler monitor."""
-        return "val/mIoU"
+        return "val/Dice"
diff --git a/src/otx/engine/engine.py b/src/otx/engine/engine.py
index fffe06a1f23..64e63fe2137 100644
--- a/src/otx/engine/engine.py
+++ b/src/otx/engine/engine.py
@@ -238,7 +238,6 @@ def train(
             **fit_kwargs,
         )
         self.checkpoint = self.trainer.checkpoint_callback.best_model_path
-
         return self.trainer.callback_metrics
 
     def test(
diff --git a/src/otx/recipe/semantic_segmentation/dino_v2.yaml b/src/otx/recipe/semantic_segmentation/dino_v2.yaml
index 0941d47edc6..d5435a5d34a 100644
--- a/src/otx/recipe/semantic_segmentation/dino_v2.yaml
+++ b/src/otx/recipe/semantic_segmentation/dino_v2.yaml
@@ -23,7 +23,7 @@ engine:
   task: SEMANTIC_SEGMENTATION
   device: auto
 
-callback_monitor: val/mIoU
+callback_monitor: val/Dice
 
 data: ../_base_/data/mmseg_base.yaml
 overrides:
diff --git a/src/otx/recipe/semantic_segmentation/litehrnet_18.yaml b/src/otx/recipe/semantic_segmentation/litehrnet_18.yaml
index b4215984175..8956be79ecd 100644
--- a/src/otx/recipe/semantic_segmentation/litehrnet_18.yaml
+++ b/src/otx/recipe/semantic_segmentation/litehrnet_18.yaml
@@ -20,12 +20,12 @@ scheduler:
     mode: max
     factor: 0.5
     patience: 5
-    monitor: val/mIoU
+    monitor: val/Dice
 
 engine:
   task: SEMANTIC_SEGMENTATION
   device: auto
 
-callback_monitor: val/mIoU
+callback_monitor: val/Dice
 
 data: ../_base_/data/mmseg_base.yaml
diff --git a/src/otx/recipe/semantic_segmentation/litehrnet_s.yaml b/src/otx/recipe/semantic_segmentation/litehrnet_s.yaml
index f0b575b4a0d..aae8197a084 100644
--- a/src/otx/recipe/semantic_segmentation/litehrnet_s.yaml
+++ b/src/otx/recipe/semantic_segmentation/litehrnet_s.yaml
@@ -20,12 +20,12 @@ scheduler:
     mode: max
     factor: 0.5
     patience: 5
-    monitor: val/mIoU
+    monitor: val/Dice
 
 engine:
   task: SEMANTIC_SEGMENTATION
   device: auto
 
-callback_monitor: val/mIoU
+callback_monitor: val/Dice
 
 data: ../_base_/data/mmseg_base.yaml
diff --git a/src/otx/recipe/semantic_segmentation/litehrnet_x.yaml b/src/otx/recipe/semantic_segmentation/litehrnet_x.yaml
index 115c655df0e..ec6fa516248 100644
--- a/src/otx/recipe/semantic_segmentation/litehrnet_x.yaml
+++ b/src/otx/recipe/semantic_segmentation/litehrnet_x.yaml
@@ -20,12 +20,12 @@ scheduler:
     mode: max
     factor: 0.5
     patience: 5
-    monitor: val/mIoU
+    monitor: val/Dice
 
 engine:
   task: SEMANTIC_SEGMENTATION
   device: auto
 
-callback_monitor: val/mIoU
+callback_monitor: val/Dice
 
 data: ../_base_/data/mmseg_base.yaml
diff --git a/src/otx/recipe/semantic_segmentation/openvino_model.yaml b/src/otx/recipe/semantic_segmentation/openvino_model.yaml
index 98021fefee7..90004e3c9b9 100644
--- a/src/otx/recipe/semantic_segmentation/openvino_model.yaml
+++ b/src/otx/recipe/semantic_segmentation/openvino_model.yaml
@@ -20,7 +20,7 @@ engine:
   task: SEMANTIC_SEGMENTATION
   device: cpu
 
-callback_monitor: val/mIoU
+callback_monitor: val/Dice
 
 data: ../_base_/data/torchvision_base.yaml
 overrides:
diff --git a/src/otx/recipe/semantic_segmentation/segnext_b.yaml b/src/otx/recipe/semantic_segmentation/segnext_b.yaml
index 8655921665f..2501234f6b3 100644
--- a/src/otx/recipe/semantic_segmentation/segnext_b.yaml
+++ b/src/otx/recipe/semantic_segmentation/segnext_b.yaml
@@ -25,7 +25,7 @@ engine:
   task: SEMANTIC_SEGMENTATION
   device: auto
 
-callback_monitor: val/mIoU
+callback_monitor: val/Dice
 
 data: ../_base_/data/mmseg_base.yaml
 overrides:
diff --git a/src/otx/recipe/semantic_segmentation/segnext_s.yaml b/src/otx/recipe/semantic_segmentation/segnext_s.yaml
index 7c1faa86567..e319bc5ada7 100644
--- a/src/otx/recipe/semantic_segmentation/segnext_s.yaml
+++ b/src/otx/recipe/semantic_segmentation/segnext_s.yaml
@@ -25,7 +25,7 @@ engine:
   task: SEMANTIC_SEGMENTATION
   device: auto
 
-callback_monitor: val/mIoU
+callback_monitor: val/Dice
 
 data: ../_base_/data/mmseg_base.yaml
 overrides:
diff --git a/src/otx/recipe/semantic_segmentation/segnext_t.yaml b/src/otx/recipe/semantic_segmentation/segnext_t.yaml
index 11c78833275..e8f657de00d 100644
--- a/src/otx/recipe/semantic_segmentation/segnext_t.yaml
+++ b/src/otx/recipe/semantic_segmentation/segnext_t.yaml
@@ -25,7 +25,7 @@ engine:
   task: SEMANTIC_SEGMENTATION
   device: auto
 
-callback_monitor: val/mIoU
+callback_monitor: val/Dice
 
 data: ../_base_/data/mmseg_base.yaml
 overrides:
diff --git a/tests/integration/cli/test_export_inference.py b/tests/integration/cli/test_export_inference.py
index e4ad87fe97d..b31073bd1a5 100644
--- a/tests/integration/cli/test_export_inference.py
+++ b/tests/integration/cli/test_export_inference.py
@@ -46,7 +46,7 @@ def fxt_local_seed() -> int:
 
 
 TASK_NAME_TO_MAIN_METRIC_NAME = {
-    "semantic_segmentation": "test/mIoU",
+    "semantic_segmentation": "test/Dice",
     "multi_label_cls": "test/accuracy",
     "multi_class_cls": "test/accuracy",
     "detection": "test/map_50",

From 8096c32514be4e1503c0f2f92f19bfde75a486d2 Mon Sep 17 00:00:00 2001
From: Vladislav Sovrasov 
Date: Tue, 13 Feb 2024 10:01:34 +0100
Subject: [PATCH 3/5] Add workaround for h-cls inference (#2904)

* Fix h-cls inference

* Add h_cls to inference testing
---
 src/otx/core/model/entity/classification.py   | 38 ++++++++++++++++---
 .../integration/cli/test_export_inference.py  |  4 ++
 2 files changed, 36 insertions(+), 6 deletions(-)

diff --git a/src/otx/core/model/entity/classification.py b/src/otx/core/model/entity/classification.py
index d07fb96ed34..f3d5f80c98b 100644
--- a/src/otx/core/model/entity/classification.py
+++ b/src/otx/core/model/entity/classification.py
@@ -8,6 +8,7 @@
 import json
 from typing import TYPE_CHECKING, Any
 
+import numpy as np
 import torch
 
 from otx.core.data.dataset.classification import HLabelMetaInfo
@@ -559,7 +560,7 @@ def __init__(
         self.num_multiclass_heads = num_multiclass_heads
         self.num_multilabel_classes = num_multilabel_classes
         model_api_configuration = model_api_configuration if model_api_configuration else {}
-        model_api_configuration.update({"hierarchical": True, "confidence_threshold": 0.0})
+        model_api_configuration.update({"hierarchical": True, "output_raw_scores": True})
         super().__init__(
             num_classes,
             model_name,
@@ -574,7 +575,7 @@ def set_hlabel_info(self, hierarchical_info: HLabelInfo) -> None:
         """Set hierarchical information in model head.
 
         Since OV IR model consist of all required hierarchy information,
-        this method serves as placehloder
+        this method serves as placeholder
         """
         if not hasattr(self.model, "hierarchical_info") or not self.model.hierarchical_info:
             msg = "OpenVINO IR model should have hierarchical config embedded in rt_info of the model"
@@ -585,15 +586,40 @@ def _customize_outputs(
         outputs: list[ClassificationResult],
         inputs: HlabelClsBatchDataEntity,
     ) -> HlabelClsBatchPredEntity:
-        pred_labels = [torch.tensor([label[0] for label in out.top_labels], dtype=torch.long) for out in outputs]
-        pred_scores = [torch.tensor([label[2] for label in out.top_labels]) for out in outputs]
+        all_pred_labels = []
+        all_pred_scores = []
+        for output in outputs:
+            logits = output.raw_scores
+            predicted_labels = []
+            predicted_scores = []
+            cls_heads_info = self.model.hierarchical_info["cls_heads_info"]
+            for i in range(cls_heads_info["num_multiclass_heads"]):
+                logits_begin, logits_end = cls_heads_info["head_idx_to_logits_range"][str(i)]
+                head_logits = logits[logits_begin:logits_end]
+                j = np.argmax(head_logits)
+                predicted_labels.append(j)
+                predicted_scores.append(head_logits[j])
+
+            if cls_heads_info["num_multilabel_classes"]:
+                logits_begin = cls_heads_info["num_single_label_classes"]
+                head_logits = logits[logits_begin:]
+
+                for i in range(head_logits.shape[0]):
+                    predicted_scores.append(head_logits[i])
+                    if head_logits[i] > self.model.confidence_threshold:
+                        predicted_labels.append(1)
+                    else:
+                        predicted_labels.append(0)
+
+            all_pred_labels.append(torch.tensor(predicted_labels, dtype=torch.long))
+            all_pred_scores.append(torch.tensor(predicted_scores))
 
         return HlabelClsBatchPredEntity(
             batch_size=len(outputs),
             images=inputs.images,
             imgs_info=inputs.imgs_info,
-            scores=pred_scores,
-            labels=pred_labels,
+            scores=all_pred_scores,
+            labels=all_pred_labels,
         )
 
 
diff --git a/tests/integration/cli/test_export_inference.py b/tests/integration/cli/test_export_inference.py
index b31073bd1a5..a7c53fe0e0d 100644
--- a/tests/integration/cli/test_export_inference.py
+++ b/tests/integration/cli/test_export_inference.py
@@ -49,6 +49,7 @@ def fxt_local_seed() -> int:
     "semantic_segmentation": "test/Dice",
     "multi_label_cls": "test/accuracy",
     "multi_class_cls": "test/accuracy",
+    "h_label_cls": "test/accuracy",
     "detection": "test/map_50",
     "instance_segmentation": "test/map_50",
 }
@@ -264,5 +265,8 @@ def test_otx_export_infer(
     if "multi_label_cls/mobilenet_v3_large_light" in request.node.name:
         msg = "multi_label_cls/mobilenet_v3_large_light exceeds the following threshold = 0.1"
         pytest.xfail(msg)
+    if "h_label_cls/efficientnet_v2_light" in request.node.name:
+        msg = "h_label_cls/efficientnet_v2_light exceeds the following threshold = 0.1"
+        pytest.xfail(msg)
 
     _check_relative_metric_diff(torch_acc, ov_acc, 0.1)

From 4cd268b0d757047cdb7965c84d40df6a49e6e49a Mon Sep 17 00:00:00 2001
From: Vladislav Sovrasov 
Date: Tue, 13 Feb 2024 10:03:54 +0100
Subject: [PATCH 4/5] Update data pipeline for DinoV2 cls (#2898)

* Update data pipeline for DinoV2 cls

* Fix typos in native converter

* Fix typos in mmdeploy converter
---
 src/otx/core/exporter/base.py                              | 2 +-
 src/otx/core/exporter/mmdeploy.py                          | 6 +++---
 src/otx/core/exporter/native.py                            | 4 ++--
 .../multi_class_cls/otx_dino_v2_linear_probe.yaml          | 7 ++-----
 tests/integration/cli/test_export_inference.py             | 4 +---
 5 files changed, 9 insertions(+), 14 deletions(-)

diff --git a/src/otx/core/exporter/base.py b/src/otx/core/exporter/base.py
index 845a06a9d59..e9838d5d16e 100644
--- a/src/otx/core/exporter/base.py
+++ b/src/otx/core/exporter/base.py
@@ -31,7 +31,7 @@ class OTXModelExporter:
         mean (tuple[float, float, float], optional): Mean values of 3 channels. Defaults to (0.0, 0.0, 0.0).
         std (tuple[float, float, float], optional): Std values of 3 channels. Defaults to (1.0, 1.0, 1.0).
         resize_mode (Literal["crop", "standard", "fit_to_window", "fit_to_window_letterbox"], optional):
-            A resize type for model preprocess. "standard" resizes iamges without keeping ratio.
+            A resize type for model preprocess. "standard" resizes images without keeping ratio.
             "fit_to_window" resizes images while keeping ratio.
             "fit_to_window_letterbox" resizes images and pads images to fit the size. Defaults to "standard".
         pad_value (int, optional): Padding value. Defaults to 0.
diff --git a/src/otx/core/exporter/mmdeploy.py b/src/otx/core/exporter/mmdeploy.py
index 158cf924596..6e378199d1b 100644
--- a/src/otx/core/exporter/mmdeploy.py
+++ b/src/otx/core/exporter/mmdeploy.py
@@ -41,7 +41,7 @@ class MMdeployExporter(OTXModelExporter):
         mean (tuple[float, float, float], optional): Mean values of 3 channels. Defaults to (0.0, 0.0, 0.0).
         std (tuple[float, float, float], optional): Std values of 3 channels. Defaults to (1.0, 1.0, 1.0).
         resize_mode (Literal["crop", "standard", "fit_to_window", "fit_to_window_letterbox"], optional):
-            A resize type for model preprocess. "standard" resizes iamges without keeping ratio.
+            A resize type for model preprocess. "standard" resizes images without keeping ratio.
             "fit_to_window" resizes images while keeping ratio.
             "fit_to_window_letterbox" resizes images and pads images to fit the size. Defaults to "standard".
         pad_value (int, optional): Padding value. Defaults to 0.
@@ -110,7 +110,7 @@ def to_openvino(
         save_path = output_dir / (base_model_name + ".xml")
         openvino.save_model(exported_model, save_path, compress_to_fp16=(precision == OTXPrecisionType.FP16))
         onnx_path.unlink()
-        log.info("Coverting to OpenVINO is done.")
+        log.info("Converting to OpenVINO is done.")
 
         return Path(save_path)
 
@@ -141,7 +141,7 @@ def to_onnx(
         onnx_model = self._postprocess_onnx_model(onnx_model, embed_metadata, precision)
 
         onnx.save(onnx_model, str(save_path))
-        log.info("Coverting to ONNX is done.")
+        log.info("Converting to ONNX is done.")
 
         return save_path
 
diff --git a/src/otx/core/exporter/native.py b/src/otx/core/exporter/native.py
index ee0a8748a03..0a4257a6b31 100644
--- a/src/otx/core/exporter/native.py
+++ b/src/otx/core/exporter/native.py
@@ -75,7 +75,7 @@ def to_openvino(
 
         save_path = output_dir / (base_model_name + ".xml")
         openvino.save_model(exported_model, save_path, compress_to_fp16=(precision == OTXPrecisionType.FP16))
-        log.info("Coverting to OpenVINO is done.")
+        log.info("Converting to OpenVINO is done.")
 
         return Path(save_path)
 
@@ -109,6 +109,6 @@ def to_onnx(
         onnx_model = self._postprocess_onnx_model(onnx_model, embed_metadata, precision)
 
         onnx.save(onnx_model, save_path)
-        log.info("Coverting to ONNX is done.")
+        log.info("Converting to ONNX is done.")
 
         return Path(save_path)
diff --git a/src/otx/recipe/classification/multi_class_cls/otx_dino_v2_linear_probe.yaml b/src/otx/recipe/classification/multi_class_cls/otx_dino_v2_linear_probe.yaml
index 4ea1f2d67bf..dc5e4ccde51 100644
--- a/src/otx/recipe/classification/multi_class_cls/otx_dino_v2_linear_probe.yaml
+++ b/src/otx/recipe/classification/multi_class_cls/otx_dino_v2_linear_probe.yaml
@@ -92,9 +92,6 @@ overrides:
             to_rgb: true
             type: Normalize
           - backend: cv2
-            edge: short
-            scale: 256
-            type: ResizeEdge
-          - crop_size: 224
-            type: CenterCrop
+            scale: 224
+            type: Resize
           - type: PackInputs
diff --git a/tests/integration/cli/test_export_inference.py b/tests/integration/cli/test_export_inference.py
index a7c53fe0e0d..552e96aad7c 100644
--- a/tests/integration/cli/test_export_inference.py
+++ b/tests/integration/cli/test_export_inference.py
@@ -91,9 +91,7 @@ def test_otx_export_infer(
     ):
         pytest.skip("To prevent memory bug from aborting integration test, test single model per task.")
     elif "tile" in recipe:
-        pytest.skip("Exporting tiling model isn't suppored yet.")
-    elif "otx_dino_v2_linear_probe" in recipe:
-        pytest.skip("Test pipeline is different between torch and ov model.")  # NOTE Enable test after making them same
+        pytest.skip("Exporting models with tiling isn't supported yet.")
 
     model_name = recipe.split("/")[-1].split(".")[0]
     # 1) otx train

From c5ad4258a29376b3f9952c9b5441b675dd6f5c8c Mon Sep 17 00:00:00 2001
From: Harim Kang 
Date: Wed, 14 Feb 2024 09:52:36 +0900
Subject: [PATCH 5/5] Fix to allow Scheduler to be configured as a list and fix
 a bug in the AdaptiveTrainingCallback (#2910)

* Address Adaptive Setting issue

* Fix to receive optimizers and schedulers as a list

* Remove warmup 0 iter settings

* Fix partial_instantiate_function

* Fix minor issue

* Fix for other python version

* Fix minor empty issue

* Fix docstring in Engine

* Fix CLI type

* Fix configs.callbacks is None
---
 .../callbacks/adaptive_train_scheduling.py    |  2 +-
 src/otx/cli/cli.py                            | 23 +++++---
 src/otx/cli/utils/jsonargparse.py             |  2 +-
 .../model/module/action_classification.py     |  4 +-
 src/otx/core/model/module/action_detection.py |  4 +-
 src/otx/core/model/module/base.py             | 57 ++++++++-----------
 src/otx/core/model/module/classification.py   | 12 ++--
 src/otx/core/model/module/detection.py        |  4 +-
 .../model/module/instance_segmentation.py     |  4 +-
 .../core/model/module/rotated_detection.py    |  4 +-
 src/otx/core/model/module/segmentation.py     |  4 +-
 src/otx/core/model/module/visual_prompting.py |  4 +-
 src/otx/core/utils/instantiators.py           | 23 +++++---
 src/otx/engine/engine.py                      | 21 +++----
 src/otx/engine/utils/auto_configurator.py     |  8 +--
 .../action/action_classification/x3d.yaml     | 16 +++---
 .../action/action_detection/x3d_fastrcnn.yaml | 16 +++---
 .../h_label_cls/efficientnet_b0_light.yaml    |  8 ++-
 .../h_label_cls/efficientnet_v2_light.yaml    |  8 ++-
 .../h_label_cls/mobilenet_v3_large_light.yaml | 21 ++++---
 .../h_label_cls/otx_deit_tiny.yaml            | 21 ++++---
 .../efficientnet_b0_light.yaml                | 10 +++-
 .../efficientnet_v2_light.yaml                |  8 ++-
 .../mobilenet_v3_large_light.yaml             | 21 ++++---
 .../multi_class_cls/otx_deit_tiny.yaml        | 23 +++++---
 .../multi_class_cls/otx_dino_v2.yaml          |  5 ++
 .../otx_dino_v2_linear_probe.yaml             |  5 ++
 .../multi_class_cls/otx_efficientnet_b0.yaml  | 11 +++-
 .../multi_class_cls/otx_efficientnet_v2.yaml  |  9 ++-
 .../otx_mobilenet_v3_large.yaml               | 20 +++++--
 .../efficientnet_b0_light.yaml                |  8 ++-
 .../efficientnet_v2_light.yaml                |  8 ++-
 .../mobilenet_v3_large_light.yaml             | 21 ++++---
 .../multi_label_cls/otx_deit_tiny.yaml        | 21 ++++---
 .../recipe/detection/atss_mobilenetv2.yaml    | 16 +++---
 src/otx/recipe/detection/atss_r50_fpn.yaml    | 16 +++---
 src/otx/recipe/detection/atss_resnext101.yaml | 16 +++---
 src/otx/recipe/detection/ssd_mobilenetv2.yaml | 16 +++---
 src/otx/recipe/detection/yolox_l.yaml         | 16 +++---
 src/otx/recipe/detection/yolox_l_tile.yaml    | 16 +++---
 src/otx/recipe/detection/yolox_s.yaml         | 16 +++---
 src/otx/recipe/detection/yolox_s_tile.yaml    | 16 +++---
 src/otx/recipe/detection/yolox_tiny.yaml      | 16 +++---
 src/otx/recipe/detection/yolox_tiny_tile.yaml | 16 +++---
 src/otx/recipe/detection/yolox_x.yaml         | 16 +++---
 src/otx/recipe/detection/yolox_x_tile.yaml    | 16 +++---
 .../maskrcnn_efficientnetb2b.yaml             | 17 +++---
 .../maskrcnn_efficientnetb2b_tile.yaml        | 17 +++---
 .../instance_segmentation/maskrcnn_r50.yaml   | 17 +++---
 .../maskrcnn_r50_tile.yaml                    | 17 +++---
 .../instance_segmentation/maskrcnn_swint.yaml | 17 +++---
 .../maskrcnn_efficientnetb2b.yaml             | 16 ++++--
 .../rotated_detection/maskrcnn_r50.yaml       | 16 ++++--
 .../semantic_segmentation/litehrnet_18.yaml   | 19 ++++---
 .../semantic_segmentation/litehrnet_s.yaml    | 19 ++++---
 .../semantic_segmentation/litehrnet_x.yaml    | 19 ++++---
 .../semantic_segmentation/segnext_b.yaml      | 15 +++--
 .../semantic_segmentation/segnext_s.yaml      | 15 +++--
 .../semantic_segmentation/segnext_t.yaml      | 15 +++--
 .../test_adaptive_train_scheduling.py         |  1 +
 tests/unit/core/model/module/test_base.py     | 23 ++++----
 .../engine/utils/test_auto_configurator.py    | 14 ++++-
 62 files changed, 540 insertions(+), 345 deletions(-)

diff --git a/src/otx/algo/callbacks/adaptive_train_scheduling.py b/src/otx/algo/callbacks/adaptive_train_scheduling.py
index ae63040f056..afeeaa0bb35 100644
--- a/src/otx/algo/callbacks/adaptive_train_scheduling.py
+++ b/src/otx/algo/callbacks/adaptive_train_scheduling.py
@@ -104,7 +104,7 @@ def _revert_func(config: LRSchedulerConfig, saved_frequency: int) -> None:
             config.frequency = saved_frequency
 
         for config in lr_configs:
-            if hasattr(config, "frequency"):
+            if hasattr(config, "frequency") and hasattr(config, "interval") and config.interval == "epoch":
                 msg = (
                     "The frequency of LRscheduler will be changed due to the effect of adaptive interval: "
                     f"{config.frequency} --> {adaptive_interval}."
diff --git a/src/otx/cli/cli.py b/src/otx/cli/cli.py
index 2c7159f990a..764e84794a8 100644
--- a/src/otx/cli/cli.py
+++ b/src/otx/cli/cli.py
@@ -156,18 +156,19 @@ def engine_subcommand_parser(**kwargs) -> ArgumentParser:
             sub_configs=True,
         )
         # Optimizer & Scheduler Settings
-        from lightning.pytorch.cli import LRSchedulerTypeTuple
+        from lightning.pytorch.cli import ReduceLROnPlateau
         from torch.optim import Optimizer
+        from torch.optim.lr_scheduler import LRScheduler
 
         optim_kwargs = {"instantiate": False, "fail_untyped": False, "skip": {"params"}}
         scheduler_kwargs = {"instantiate": False, "fail_untyped": False, "skip": {"optimizer"}}
         parser.add_subclass_arguments(
-            baseclass=(Optimizer,),
+            baseclass=(Optimizer, list),
             nested_key="optimizer",
             **optim_kwargs,
         )
         parser.add_subclass_arguments(
-            baseclass=LRSchedulerTypeTuple,
+            baseclass=(LRScheduler, ReduceLROnPlateau, list),
             nested_key="scheduler",
             **scheduler_kwargs,
         )
@@ -341,11 +342,17 @@ def instantiate_model(self, model_config: Namespace) -> tuple:
         # Update self.config with model
         self.config[self.subcommand].update(Namespace(model=model_config))
 
-        optimizer_kwargs = namespace_to_dict(self.get_config_value(self.config_init, "optimizer", Namespace()))
-        scheduler_kwargs = namespace_to_dict(self.get_config_value(self.config_init, "scheduler", Namespace()))
         from otx.core.utils.instantiators import partial_instantiate_class
 
-        return model, partial_instantiate_class(optimizer_kwargs), partial_instantiate_class(scheduler_kwargs)
+        optimizer_kwargs = self.get_config_value(self.config_init, "optimizer", {})
+        optimizer_kwargs = optimizer_kwargs if isinstance(optimizer_kwargs, list) else [optimizer_kwargs]
+        optimizers = partial_instantiate_class([_opt for _opt in optimizer_kwargs if _opt])
+
+        scheduler_kwargs = self.get_config_value(self.config_init, "scheduler", {})
+        scheduler_kwargs = scheduler_kwargs if isinstance(scheduler_kwargs, list) else [scheduler_kwargs]
+        schedulers = partial_instantiate_class([_sch for _sch in scheduler_kwargs if _sch])
+
+        return model, optimizers, schedulers
 
     def get_config_value(self, config: Namespace, key: str, default: Any = None) -> Any:  # noqa: ANN401
         """Retrieves the value of a configuration key from the given config object.
@@ -357,8 +364,10 @@ def get_config_value(self, config: Namespace, key: str, default: Any = None) ->
 
         Returns:
             Any: The value of the configuration key, or the default value if the key is not found.
+                if the value is a Namespace, it is converted to a dictionary.
         """
-        return config.get(str(self.subcommand), config).get(key, default)
+        result = config.get(str(self.subcommand), config).get(key, default)
+        return namespace_to_dict(result) if isinstance(result, Namespace) else result
 
     def get_subcommand_parser(self, subcommand: str | None) -> ArgumentParser:
         """Returns the argument parser for the specified subcommand.
diff --git a/src/otx/cli/utils/jsonargparse.py b/src/otx/cli/utils/jsonargparse.py
index d16a1a238c6..8ea735e9d07 100644
--- a/src/otx/cli/utils/jsonargparse.py
+++ b/src/otx/cli/utils/jsonargparse.py
@@ -178,7 +178,7 @@ def list_override(configs: Namespace, key: str, overrides: list) -> None:
         ...     ...
         ... ]
     """
-    if key not in configs:
+    if key not in configs or configs[key] is None:
         return
     for target in overrides:
         class_path = target.get("class_path", None)
diff --git a/src/otx/core/model/module/action_classification.py b/src/otx/core/model/module/action_classification.py
index 867f7378283..cdc4d065982 100644
--- a/src/otx/core/model/module/action_classification.py
+++ b/src/otx/core/model/module/action_classification.py
@@ -28,8 +28,8 @@ def __init__(
         self,
         otx_model: OTXActionClsModel,
         torch_compile: bool,
-        optimizer: OptimizerCallable = lambda p: torch.optim.SGD(p, lr=0.01),
-        scheduler: LRSchedulerCallable = torch.optim.lr_scheduler.ConstantLR,
+        optimizer: list[OptimizerCallable] | OptimizerCallable = lambda p: torch.optim.SGD(p, lr=0.01),
+        scheduler: list[LRSchedulerCallable] | LRSchedulerCallable = torch.optim.lr_scheduler.ConstantLR,
     ):
         super().__init__(
             otx_model=otx_model,
diff --git a/src/otx/core/model/module/action_detection.py b/src/otx/core/model/module/action_detection.py
index cf9ff35baaf..3e5f0ba7d46 100644
--- a/src/otx/core/model/module/action_detection.py
+++ b/src/otx/core/model/module/action_detection.py
@@ -29,8 +29,8 @@ def __init__(
         self,
         otx_model: OTXActionDetModel,
         torch_compile: bool,
-        optimizer: OptimizerCallable = lambda p: torch.optim.SGD(p, lr=0.01),
-        scheduler: LRSchedulerCallable = torch.optim.lr_scheduler.ConstantLR,
+        optimizer: list[OptimizerCallable] | OptimizerCallable = lambda p: torch.optim.SGD(p, lr=0.01),
+        scheduler: list[LRSchedulerCallable] | LRSchedulerCallable = torch.optim.lr_scheduler.ConstantLR,
     ):
         super().__init__(
             otx_model=otx_model,
diff --git a/src/otx/core/model/module/base.py b/src/otx/core/model/module/base.py
index b0e90ef16c9..8dd4f0e5618 100644
--- a/src/otx/core/model/module/base.py
+++ b/src/otx/core/model/module/base.py
@@ -12,7 +12,6 @@
 from lightning import LightningModule
 from torch import Tensor
 
-from otx.algo.schedulers.warmup_schedulers import BaseWarmupScheduler
 from otx.core.data.entity.base import (
     OTXBatchDataEntity,
     OTXBatchLossEntity,
@@ -34,11 +33,13 @@ def __init__(
         self,
         optimizer: torch.optim.Optimizer,
         num_warmup_steps: int = 1000,
+        interval: str = "step",
     ):
-        if num_warmup_steps > 0:
+        if not num_warmup_steps > 0:
             msg = f"num_warmup_steps should be > 0, got {num_warmup_steps}"
-            ValueError(msg)
+            raise ValueError(msg)
         self.num_warmup_steps = num_warmup_steps
+        self.interval = interval
         super().__init__(optimizer, lambda step: min(step / num_warmup_steps, 1.0))
 
 
@@ -50,8 +51,8 @@ def __init__(
         *,
         otx_model: OTXModel,
         torch_compile: bool,
-        optimizer: OptimizerCallable = lambda p: torch.optim.SGD(p, lr=0.01),
-        scheduler: LRSchedulerCallable = torch.optim.lr_scheduler.ConstantLR,
+        optimizer: list[OptimizerCallable] | OptimizerCallable = lambda p: torch.optim.SGD(p, lr=0.01),
+        scheduler: list[LRSchedulerCallable] | LRSchedulerCallable = torch.optim.lr_scheduler.ConstantLR,
     ):
         super().__init__()
 
@@ -110,7 +111,7 @@ def setup(self, stage: str) -> None:
         if self.torch_compile and stage == "fit":
             self.model = torch.compile(self.model)
 
-    def configure_optimizers(self) -> tuple[list[torch.optim.Optimizer], list[torch.optim.Optimizer]]:
+    def configure_optimizers(self) -> tuple[list[torch.optim.Optimizer], list[dict]]:
         """Choose what optimizers and learning-rate schedulers to use in your optimization.
 
         Normally you'd need one. But in the case of GANs or similar you might have multiple.
@@ -120,34 +121,26 @@ def configure_optimizers(self) -> tuple[list[torch.optim.Optimizer], list[torch.
 
         :return: A dict containing the configured optimizers and learning-rate schedulers to be used for training.
         """
-        optimizer = (
-            self.hparams.optimizer(params=self.parameters())
-            if callable(self.hparams.optimizer)
-            else self.hparams.optimizer
-        )
-
-        scheduler = (
-            self.hparams.scheduler(optimizer=optimizer) if callable(self.hparams.scheduler) else self.hparams.scheduler
-        )
-
-        lr_scheduler_configs = []
-        if isinstance(scheduler, BaseWarmupScheduler) and scheduler.warmup_steps > 0:
-            lr_scheduler_configs += [
-                {
-                    "scheduler": LinearWarmupScheduler(optimizer, num_warmup_steps=scheduler.warmup_steps),
-                    "interval": "step",
-                },
-            ]
-        lr_scheduler_configs += [
-            {
-                "scheduler": scheduler,
-                "monitor": self.lr_scheduler_monitor_key,
-                "interval": "epoch",
-                "frequency": self.trainer.check_val_every_n_epoch,
-            },
+
+        def ensure_list(item: Any) -> list:  # noqa: ANN401
+            return item if isinstance(item, list) else [item]
+
+        optimizers = [
+            optimizer(params=self.parameters()) if callable(optimizer) else optimizer
+            for optimizer in ensure_list(self.hparams.optimizer)
         ]
 
-        return [optimizer], lr_scheduler_configs
+        lr_schedulers = []
+        for scheduler_config in ensure_list(self.hparams.scheduler):
+            scheduler = scheduler_config(optimizers[0]) if callable(scheduler_config) else scheduler_config
+            lr_scheduler_config = {"scheduler": scheduler}
+            if hasattr(scheduler, "interval"):
+                lr_scheduler_config["interval"] = scheduler.interval
+            if hasattr(scheduler, "monitor"):
+                lr_scheduler_config["monitor"] = scheduler.monitor
+            lr_schedulers.append(lr_scheduler_config)
+
+        return optimizers, lr_schedulers
 
     def register_load_state_dict_pre_hook(self, model_classes: list[str], ckpt_classes: list[str]) -> None:
         """Register self.model's load_state_dict_pre_hook.
diff --git a/src/otx/core/model/module/classification.py b/src/otx/core/model/module/classification.py
index 7505824b48c..fd1f9cf5431 100644
--- a/src/otx/core/model/module/classification.py
+++ b/src/otx/core/model/module/classification.py
@@ -37,8 +37,8 @@ def __init__(
         self,
         otx_model: OTXMulticlassClsModel,
         torch_compile: bool,
-        optimizer: OptimizerCallable = lambda p: torch.optim.SGD(p, lr=0.01),
-        scheduler: LRSchedulerCallable = torch.optim.lr_scheduler.ConstantLR,
+        optimizer: list[OptimizerCallable] | OptimizerCallable = lambda p: torch.optim.SGD(p, lr=0.01),
+        scheduler: list[LRSchedulerCallable] | LRSchedulerCallable = torch.optim.lr_scheduler.ConstantLR,
     ):
         super().__init__(
             otx_model=otx_model,
@@ -130,8 +130,8 @@ def __init__(
         self,
         otx_model: OTXMultilabelClsModel,
         torch_compile: bool,
-        optimizer: OptimizerCallable = lambda p: torch.optim.SGD(p, lr=0.01),
-        scheduler: LRSchedulerCallable = torch.optim.lr_scheduler.ConstantLR,
+        optimizer: list[OptimizerCallable] | OptimizerCallable = lambda p: torch.optim.SGD(p, lr=0.01),
+        scheduler: list[LRSchedulerCallable] | LRSchedulerCallable = torch.optim.lr_scheduler.ConstantLR,
     ):
         super().__init__(
             otx_model=otx_model,
@@ -218,8 +218,8 @@ def __init__(
         self,
         otx_model: OTXHlabelClsModel,
         torch_compile: bool,
-        optimizer: OptimizerCallable = lambda p: torch.optim.SGD(p, lr=0.01),
-        scheduler: LRSchedulerCallable = torch.optim.lr_scheduler.ConstantLR,
+        optimizer: list[OptimizerCallable] | OptimizerCallable = lambda p: torch.optim.SGD(p, lr=0.01),
+        scheduler: list[LRSchedulerCallable] | LRSchedulerCallable = torch.optim.lr_scheduler.ConstantLR,
     ):
         super().__init__(
             otx_model=otx_model,
diff --git a/src/otx/core/model/module/detection.py b/src/otx/core/model/module/detection.py
index 3869a5a798f..f2d9938874a 100644
--- a/src/otx/core/model/module/detection.py
+++ b/src/otx/core/model/module/detection.py
@@ -29,8 +29,8 @@ def __init__(
         self,
         otx_model: ExplainableOTXDetModel,
         torch_compile: bool,
-        optimizer: OptimizerCallable = lambda p: torch.optim.SGD(p, lr=0.01),
-        scheduler: LRSchedulerCallable = torch.optim.lr_scheduler.ConstantLR,
+        optimizer: list[OptimizerCallable] | OptimizerCallable = lambda p: torch.optim.SGD(p, lr=0.01),
+        scheduler: list[LRSchedulerCallable] | LRSchedulerCallable = torch.optim.lr_scheduler.ConstantLR,
     ):
         super().__init__(
             otx_model=otx_model,
diff --git a/src/otx/core/model/module/instance_segmentation.py b/src/otx/core/model/module/instance_segmentation.py
index 4d27ece3a4c..40bf4fb4fb3 100644
--- a/src/otx/core/model/module/instance_segmentation.py
+++ b/src/otx/core/model/module/instance_segmentation.py
@@ -32,8 +32,8 @@ def __init__(
         self,
         otx_model: ExplainableOTXInstanceSegModel,
         torch_compile: bool,
-        optimizer: OptimizerCallable = lambda p: torch.optim.SGD(p, lr=0.01),
-        scheduler: LRSchedulerCallable = torch.optim.lr_scheduler.ConstantLR,
+        optimizer: list[OptimizerCallable] | OptimizerCallable = lambda p: torch.optim.SGD(p, lr=0.01),
+        scheduler: list[LRSchedulerCallable] | LRSchedulerCallable = torch.optim.lr_scheduler.ConstantLR,
     ):
         super().__init__(
             otx_model=otx_model,
diff --git a/src/otx/core/model/module/rotated_detection.py b/src/otx/core/model/module/rotated_detection.py
index 84275563b2e..12bfd84d5a7 100644
--- a/src/otx/core/model/module/rotated_detection.py
+++ b/src/otx/core/model/module/rotated_detection.py
@@ -25,8 +25,8 @@ def __init__(
         self,
         otx_model: OTXRotatedDetModel,
         torch_compile: bool,
-        optimizer: OptimizerCallable = lambda p: torch.optim.SGD(p, lr=0.01),
-        scheduler: LRSchedulerCallable = torch.optim.lr_scheduler.ConstantLR,
+        optimizer: list[OptimizerCallable] | OptimizerCallable = lambda p: torch.optim.SGD(p, lr=0.01),
+        scheduler: list[LRSchedulerCallable] | LRSchedulerCallable = torch.optim.lr_scheduler.ConstantLR,
     ):
         super().__init__(
             otx_model=otx_model,
diff --git a/src/otx/core/model/module/segmentation.py b/src/otx/core/model/module/segmentation.py
index de028c5c40a..000b2cdea3d 100644
--- a/src/otx/core/model/module/segmentation.py
+++ b/src/otx/core/model/module/segmentation.py
@@ -29,8 +29,8 @@ def __init__(
         self,
         otx_model: OTXSegmentationModel,
         torch_compile: bool,
-        optimizer: OptimizerCallable = lambda p: torch.optim.SGD(p, lr=0.01),
-        scheduler: LRSchedulerCallable = torch.optim.lr_scheduler.ConstantLR,
+        optimizer: list[OptimizerCallable] | OptimizerCallable = lambda p: torch.optim.SGD(p, lr=0.01),
+        scheduler: list[LRSchedulerCallable] | LRSchedulerCallable = torch.optim.lr_scheduler.ConstantLR,
     ):
         super().__init__(
             otx_model=otx_model,
diff --git a/src/otx/core/model/module/visual_prompting.py b/src/otx/core/model/module/visual_prompting.py
index 599f2a06c79..68a62ef8398 100644
--- a/src/otx/core/model/module/visual_prompting.py
+++ b/src/otx/core/model/module/visual_prompting.py
@@ -36,8 +36,8 @@ def __init__(
         self,
         otx_model: OTXVisualPromptingModel,
         torch_compile: bool,
-        optimizer: OptimizerCallable = lambda p: torch.optim.SGD(p, lr=0.01),
-        scheduler: LRSchedulerCallable = torch.optim.lr_scheduler.ConstantLR,
+        optimizer: list[OptimizerCallable] | OptimizerCallable = lambda p: torch.optim.SGD(p, lr=0.01),
+        scheduler: list[LRSchedulerCallable] | LRSchedulerCallable = torch.optim.lr_scheduler.ConstantLR,
     ):
         super().__init__(
             otx_model=otx_model,
diff --git a/src/otx/core/utils/instantiators.py b/src/otx/core/utils/instantiators.py
index b19f0105cea..5ca301b1163 100644
--- a/src/otx/core/utils/instantiators.py
+++ b/src/otx/core/utils/instantiators.py
@@ -66,24 +66,29 @@ def instantiate_loggers(logger_cfg: list | None) -> list[Logger]:
     return logger
 
 
-def partial_instantiate_class(init: dict | None) -> partial | None:
+def partial_instantiate_class(init: list | dict | None) -> list[partial] | None:
     """Partially instantiates a class with the given initialization arguments.
 
     Copy from lightning.pytorch.cli.instantiate_class and modify it to use partial.
 
     Args:
-        init (dict): A dictionary containing the initialization arguments.
-            It should have the following keys:
+        init (list | dict | None): A dictionary containing the initialization arguments.
+            It should have the following each keys:
             - "init_args" (dict): A dictionary of keyword arguments to be passed to the class constructor.
             - "class_path" (str): The fully qualified path of the class to be instantiated.
 
     Returns:
-        partial: A partial object representing the partially instantiated class.
+        list[partial] | None: A partial object representing the partially instantiated class.
     """
     if not init:
         return None
-    kwargs = init.get("init_args", {})
-    class_module, class_name = init["class_path"].rsplit(".", 1)
-    module = __import__(class_module, fromlist=[class_name])
-    args_class = getattr(module, class_name)
-    return partial(args_class, **kwargs)
+    if not isinstance(init, list):
+        init = [init]
+    items: list[partial] = []
+    for item in init:
+        kwargs = item.get("init_args", {})
+        class_module, class_name = item["class_path"].rsplit(".", 1)
+        module = __import__(class_module, fromlist=[class_name])
+        args_class = getattr(module, class_name)
+        items.append(partial(args_class, **kwargs))
+    return items
diff --git a/src/otx/engine/engine.py b/src/otx/engine/engine.py
index 64e63fe2137..694a29827ac 100644
--- a/src/otx/engine/engine.py
+++ b/src/otx/engine/engine.py
@@ -83,8 +83,8 @@ def __init__(
         work_dir: PathLike = "./otx-workspace",
         datamodule: OTXDataModule | None = None,
         model: OTXModel | str | None = None,
-        optimizer: OptimizerCallable | None = None,
-        scheduler: LRSchedulerCallable | None = None,
+        optimizer: list[OptimizerCallable] | OptimizerCallable | None = None,
+        scheduler: list[LRSchedulerCallable] | LRSchedulerCallable | None = None,
         checkpoint: PathLike | None = None,
         device: DeviceType = DeviceType.auto,
         **kwargs,
@@ -97,9 +97,10 @@ def __init__(
             work_dir (PathLike, optional): Working directory for the engine. Defaults to "./otx-workspace".
             datamodule (OTXDataModule | None, optional): The data module for the engine. Defaults to None.
             model (OTXModel | str | None, optional): The model for the engine. Defaults to None.
-            optimizer (OptimizerCallable | None, optional): The optimizer for the engine. Defaults to None.
-            scheduler (LRSchedulerCallable | None, optional): The learning rate scheduler for the engine.
+            optimizer (list[OptimizerCallable] | OptimizerCallable | None, optional): The optimizer for the engine.
                 Defaults to None.
+            scheduler (list[LRSchedulerCallable] | LRSchedulerCallable | None, optional):
+                The learning rate scheduler for the engine. Defaults to None.
             checkpoint (PathLike | None, optional): Path to the checkpoint file. Defaults to None.
             device (DeviceType, optional): The device type to use. Defaults to DeviceType.auto.
             **kwargs: Additional keyword arguments for pl.Trainer.
@@ -132,10 +133,10 @@ def __init__(
                 meta_info=self._datamodule.meta_info if self._datamodule is not None else None,
             )
         )
-        self.optimizer: OptimizerCallable | None = (
+        self.optimizer: list[OptimizerCallable] | OptimizerCallable | None = (
             optimizer if optimizer is not None else self._auto_configurator.get_optimizer()
         )
-        self.scheduler: LRSchedulerCallable | None = (
+        self.scheduler: list[LRSchedulerCallable] | LRSchedulerCallable | None = (
             scheduler if scheduler is not None else self._auto_configurator.get_scheduler()
         )
 
@@ -667,15 +668,15 @@ def datamodule(self) -> OTXDataModule:
     def _build_lightning_module(
         self,
         model: OTXModel,
-        optimizer: OptimizerCallable,
-        scheduler: LRSchedulerCallable,
+        optimizer: list[OptimizerCallable] | OptimizerCallable | None,
+        scheduler: list[LRSchedulerCallable] | LRSchedulerCallable | None,
     ) -> OTXLitModule:
         """Builds a LightningModule for engine workflow.
 
         Args:
             model (OTXModel): The OTXModel instance.
-            optimizer (OptimizerCallable): The optimizer callable.
-            scheduler (LRSchedulerCallable): The learning rate scheduler callable.
+            optimizer (list[OptimizerCallable] | OptimizerCallable | None): The optimizer callable.
+            scheduler (list[LRSchedulerCallable] | LRSchedulerCallable | None): The learning rate scheduler callable.
 
         Returns:
             OTXLitModule: The built LightningModule instance.
diff --git a/src/otx/engine/utils/auto_configurator.py b/src/otx/engine/utils/auto_configurator.py
index 98f7f3506e1..edeceb9ce50 100644
--- a/src/otx/engine/utils/auto_configurator.py
+++ b/src/otx/engine/utils/auto_configurator.py
@@ -249,21 +249,21 @@ def get_model(self, model_name: str | None = None, meta_info: LabelInfo | None =
         logger.warning(f"Set Default Model: {self.config['model']}")
         return instantiate_class(args=(), init=self.config["model"])
 
-    def get_optimizer(self) -> OptimizerCallable | None:
+    def get_optimizer(self) -> list[OptimizerCallable] | None:
         """Returns the optimizer callable based on the configuration.
 
         Returns:
-            OptimizerCallable | None: The optimizer callable.
+            list[OptimizerCallable] | None: The optimizer callable.
         """
         optimizer_config = self.config.get("optimizer", None)
         logger.warning(f"Set Default Optimizer: {optimizer_config}")
         return partial_instantiate_class(init=optimizer_config)
 
-    def get_scheduler(self) -> LRSchedulerCallable | None:
+    def get_scheduler(self) -> list[LRSchedulerCallable] | None:
         """Returns the instantiated scheduler based on the configuration.
 
         Returns:
-            LRSchedulerCallable | None: The instantiated scheduler.
+            list[LRSchedulerCallable] | None: The instantiated scheduler.
         """
         scheduler_config = self.config.get("scheduler", None)
         logger.warning(f"Set Default Scheduler: {scheduler_config}")
diff --git a/src/otx/recipe/action/action_classification/x3d.yaml b/src/otx/recipe/action/action_classification/x3d.yaml
index 98fa0340eae..e43d0b2c1e9 100644
--- a/src/otx/recipe/action/action_classification/x3d.yaml
+++ b/src/otx/recipe/action/action_classification/x3d.yaml
@@ -10,13 +10,15 @@ optimizer:
     weight_decay: 0.0001
 
 scheduler:
-  class_path: otx.algo.schedulers.WarmupReduceLROnPlateau
-  init_args:
-    warmup_steps: 100
-    mode: max
-    factor: 0.5
-    patience: 2
-    monitor: val/accuracy
+  - class_path: otx.core.model.module.base.LinearWarmupScheduler
+    init_args:
+      num_warmup_steps: 100
+  - class_path: lightning.pytorch.cli.ReduceLROnPlateau
+    init_args:
+      mode: max
+      factor: 0.5
+      patience: 2
+      monitor: val/accuracy
 
 engine:
   task: ACTION_CLASSIFICATION
diff --git a/src/otx/recipe/action/action_detection/x3d_fastrcnn.yaml b/src/otx/recipe/action/action_detection/x3d_fastrcnn.yaml
index f297429cce6..36c51f4247e 100644
--- a/src/otx/recipe/action/action_detection/x3d_fastrcnn.yaml
+++ b/src/otx/recipe/action/action_detection/x3d_fastrcnn.yaml
@@ -12,13 +12,15 @@ optimizer:
     weight_decay: 0.00001
 
 scheduler:
-  class_path: otx.algo.schedulers.WarmupReduceLROnPlateau
-  init_args:
-    warmup_steps: 100
-    mode: max
-    factor: 0.5
-    patience: 2
-    monitor: val/map_50
+  - class_path: otx.core.model.module.base.LinearWarmupScheduler
+    init_args:
+      num_warmup_steps: 100
+  - class_path: lightning.pytorch.cli.ReduceLROnPlateau
+    init_args:
+      mode: max
+      factor: 0.5
+      patience: 2
+      monitor: val/map_50
 
 engine:
   task: ACTION_DETECTION
diff --git a/src/otx/recipe/classification/h_label_cls/efficientnet_b0_light.yaml b/src/otx/recipe/classification/h_label_cls/efficientnet_b0_light.yaml
index 6717202c843..a6b696b1497 100644
--- a/src/otx/recipe/classification/h_label_cls/efficientnet_b0_light.yaml
+++ b/src/otx/recipe/classification/h_label_cls/efficientnet_b0_light.yaml
@@ -11,9 +11,8 @@ optimizer:
     lr: 0.0049
 
 scheduler:
-  class_path: otx.algo.schedulers.WarmupReduceLROnPlateau
+  class_path: lightning.pytorch.cli.ReduceLROnPlateau
   init_args:
-    warmup_steps: 0
     mode: max
     factor: 0.5
     patience: 1
@@ -27,6 +26,11 @@ callback_monitor: val/accuracy
 
 data: ../../_base_/data/mmpretrain_base.yaml
 overrides:
+  max_epochs: 90
+  callbacks:
+    - class_path: lightning.pytorch.callbacks.EarlyStopping
+      init_args:
+        patience: 3
   data:
     task: H_LABEL_CLS
     config:
diff --git a/src/otx/recipe/classification/h_label_cls/efficientnet_v2_light.yaml b/src/otx/recipe/classification/h_label_cls/efficientnet_v2_light.yaml
index 6f63839aee3..9a12f9005e9 100644
--- a/src/otx/recipe/classification/h_label_cls/efficientnet_v2_light.yaml
+++ b/src/otx/recipe/classification/h_label_cls/efficientnet_v2_light.yaml
@@ -13,9 +13,8 @@ optimizer:
     weight_decay: 0.0001
 
 scheduler:
-  class_path: otx.algo.schedulers.WarmupReduceLROnPlateau
+  class_path: lightning.pytorch.cli.ReduceLROnPlateau
   init_args:
-    warmup_steps: 0
     mode: max
     factor: 0.5
     patience: 1
@@ -29,6 +28,11 @@ callback_monitor: val/accuracy
 
 data: ../../_base_/data/mmpretrain_base.yaml
 overrides:
+  max_epochs: 90
+  callbacks:
+    - class_path: lightning.pytorch.callbacks.EarlyStopping
+      init_args:
+        patience: 3
   data:
     task: H_LABEL_CLS
     config:
diff --git a/src/otx/recipe/classification/h_label_cls/mobilenet_v3_large_light.yaml b/src/otx/recipe/classification/h_label_cls/mobilenet_v3_large_light.yaml
index fb4e34bc725..12f731da739 100644
--- a/src/otx/recipe/classification/h_label_cls/mobilenet_v3_large_light.yaml
+++ b/src/otx/recipe/classification/h_label_cls/mobilenet_v3_large_light.yaml
@@ -13,13 +13,15 @@ optimizer:
     weight_decay: 0.0001
 
 scheduler:
-  class_path: otx.algo.schedulers.WarmupReduceLROnPlateau
-  init_args:
-    warmup_steps: 10
-    mode: max
-    factor: 0.5
-    patience: 1
-    monitor: val/accuracy
+  - class_path: otx.core.model.module.base.LinearWarmupScheduler
+    init_args:
+      num_warmup_steps: 10
+  - class_path: lightning.pytorch.cli.ReduceLROnPlateau
+    init_args:
+      mode: max
+      factor: 0.5
+      patience: 1
+      monitor: val/accuracy
 
 engine:
   task: H_LABEL_CLS
@@ -29,6 +31,11 @@ callback_monitor: val/accuracy
 
 data: ../../_base_/data/mmpretrain_base.yaml
 overrides:
+  max_epochs: 90
+  callbacks:
+    - class_path: lightning.pytorch.callbacks.EarlyStopping
+      init_args:
+        patience: 3
   data:
     task: H_LABEL_CLS
     config:
diff --git a/src/otx/recipe/classification/h_label_cls/otx_deit_tiny.yaml b/src/otx/recipe/classification/h_label_cls/otx_deit_tiny.yaml
index d837293b0a3..a6d2e62b6a3 100644
--- a/src/otx/recipe/classification/h_label_cls/otx_deit_tiny.yaml
+++ b/src/otx/recipe/classification/h_label_cls/otx_deit_tiny.yaml
@@ -12,13 +12,15 @@ optimizer:
     weight_decay: 0.05
 
 scheduler:
-  class_path: otx.algo.schedulers.WarmupReduceLROnPlateau
-  init_args:
-    warmup_steps: 10
-    mode: max
-    factor: 0.5
-    patience: 1
-    monitor: val/accuracy
+  - class_path: otx.core.model.module.base.LinearWarmupScheduler
+    init_args:
+      num_warmup_steps: 10
+  - class_path: lightning.pytorch.cli.ReduceLROnPlateau
+    init_args:
+      mode: max
+      factor: 0.5
+      patience: 1
+      monitor: val/accuracy
 
 engine:
   task: H_LABEL_CLS
@@ -28,6 +30,11 @@ callback_monitor: val/accuracy
 
 data: ../../_base_/data/mmpretrain_base.yaml
 overrides:
+  max_epochs: 90
+  callbacks:
+    - class_path: lightning.pytorch.callbacks.EarlyStopping
+      init_args:
+        patience: 3
   data:
     task: H_LABEL_CLS
     config:
diff --git a/src/otx/recipe/classification/multi_class_cls/efficientnet_b0_light.yaml b/src/otx/recipe/classification/multi_class_cls/efficientnet_b0_light.yaml
index ec9309a707c..7c7511b134b 100644
--- a/src/otx/recipe/classification/multi_class_cls/efficientnet_b0_light.yaml
+++ b/src/otx/recipe/classification/multi_class_cls/efficientnet_b0_light.yaml
@@ -12,9 +12,8 @@ optimizer:
     weight_decay: 0.0001
 
 scheduler:
-  class_path: otx.algo.schedulers.WarmupReduceLROnPlateau
+  class_path: lightning.pytorch.cli.ReduceLROnPlateau
   init_args:
-    warmup_steps: 0
     mode: max
     factor: 0.5
     patience: 1
@@ -27,3 +26,10 @@ engine:
 callback_monitor: val/accuracy
 
 data: ../../_base_/data/mmpretrain_base.yaml
+
+overrides:
+  max_epochs: 90
+  callbacks:
+    - class_path: lightning.pytorch.callbacks.EarlyStopping
+      init_args:
+        patience: 3
diff --git a/src/otx/recipe/classification/multi_class_cls/efficientnet_v2_light.yaml b/src/otx/recipe/classification/multi_class_cls/efficientnet_v2_light.yaml
index eb50774b193..c4402ac4810 100644
--- a/src/otx/recipe/classification/multi_class_cls/efficientnet_v2_light.yaml
+++ b/src/otx/recipe/classification/multi_class_cls/efficientnet_v2_light.yaml
@@ -12,9 +12,8 @@ optimizer:
     weight_decay: 0.0001
 
 scheduler:
-  class_path: otx.algo.schedulers.WarmupReduceLROnPlateau
+  class_path: lightning.pytorch.cli.ReduceLROnPlateau
   init_args:
-    warmup_steps: 0
     mode: max
     factor: 0.5
     patience: 1
@@ -28,6 +27,11 @@ callback_monitor: val/accuracy
 
 data: ../../_base_/data/mmpretrain_base.yaml
 overrides:
+  max_epochs: 90
+  callbacks:
+    - class_path: lightning.pytorch.callbacks.EarlyStopping
+      init_args:
+        patience: 3
   data:
     config:
       train_subset:
diff --git a/src/otx/recipe/classification/multi_class_cls/mobilenet_v3_large_light.yaml b/src/otx/recipe/classification/multi_class_cls/mobilenet_v3_large_light.yaml
index 200fe8b08cc..080cc830be7 100644
--- a/src/otx/recipe/classification/multi_class_cls/mobilenet_v3_large_light.yaml
+++ b/src/otx/recipe/classification/multi_class_cls/mobilenet_v3_large_light.yaml
@@ -12,13 +12,15 @@ optimizer:
     weight_decay: 0.0001
 
 scheduler:
-  class_path: otx.algo.schedulers.WarmupReduceLROnPlateau
-  init_args:
-    warmup_steps: 10
-    mode: max
-    factor: 0.5
-    patience: 1
-    monitor: val/accuracy
+  - class_path: otx.core.model.module.base.LinearWarmupScheduler
+    init_args:
+      num_warmup_steps: 10
+  - class_path: lightning.pytorch.cli.ReduceLROnPlateau
+    init_args:
+      mode: max
+      factor: 0.5
+      patience: 1
+      monitor: val/accuracy
 
 engine:
   task: MULTI_CLASS_CLS
@@ -28,6 +30,11 @@ callback_monitor: val/accuracy
 
 data: ../../_base_/data/mmpretrain_base.yaml
 overrides:
+  max_epochs: 90
+  callbacks:
+    - class_path: lightning.pytorch.callbacks.EarlyStopping
+      init_args:
+        patience: 3
   data:
     config:
       train_subset:
diff --git a/src/otx/recipe/classification/multi_class_cls/otx_deit_tiny.yaml b/src/otx/recipe/classification/multi_class_cls/otx_deit_tiny.yaml
index 5c3373ce98f..da0c5522854 100644
--- a/src/otx/recipe/classification/multi_class_cls/otx_deit_tiny.yaml
+++ b/src/otx/recipe/classification/multi_class_cls/otx_deit_tiny.yaml
@@ -10,13 +10,15 @@ optimizer:
     weight_decay: 0.05
 
 scheduler:
-  class_path: otx.algo.schedulers.WarmupReduceLROnPlateau
-  init_args:
-    warmup_steps: 10
-    mode: max
-    factor: 0.5
-    patience: 1
-    monitor: val/accuracy
+  - class_path: otx.core.model.module.base.LinearWarmupScheduler
+    init_args:
+      num_warmup_steps: 10
+  - class_path: lightning.pytorch.cli.ReduceLROnPlateau
+    init_args:
+      mode: max
+      factor: 0.5
+      patience: 1
+      monitor: val/accuracy
 
 engine:
   task: MULTI_CLASS_CLS
@@ -25,3 +27,10 @@ engine:
 callback_monitor: val/accuracy
 
 data: ../../_base_/data/mmpretrain_base.yaml
+
+overrides:
+  max_epochs: 90
+  callbacks:
+    - class_path: lightning.pytorch.callbacks.EarlyStopping
+      init_args:
+        patience: 3
diff --git a/src/otx/recipe/classification/multi_class_cls/otx_dino_v2.yaml b/src/otx/recipe/classification/multi_class_cls/otx_dino_v2.yaml
index e911b5e06a2..e69f1e1aa8e 100644
--- a/src/otx/recipe/classification/multi_class_cls/otx_dino_v2.yaml
+++ b/src/otx/recipe/classification/multi_class_cls/otx_dino_v2.yaml
@@ -34,6 +34,11 @@ callback_monitor: val/accuracy
 
 data: ../../_base_/data/mmpretrain_base.yaml
 overrides:
+  max_epochs: 90
+  callbacks:
+    - class_path: lightning.pytorch.callbacks.EarlyStopping
+      init_args:
+        patience: 3
   data:
     config:
       train_subset:
diff --git a/src/otx/recipe/classification/multi_class_cls/otx_dino_v2_linear_probe.yaml b/src/otx/recipe/classification/multi_class_cls/otx_dino_v2_linear_probe.yaml
index dc5e4ccde51..3ef45101e92 100644
--- a/src/otx/recipe/classification/multi_class_cls/otx_dino_v2_linear_probe.yaml
+++ b/src/otx/recipe/classification/multi_class_cls/otx_dino_v2_linear_probe.yaml
@@ -36,6 +36,11 @@ callback_monitor: val/accuracy
 
 data: ../../_base_/data/mmpretrain_base.yaml
 overrides:
+  max_epochs: 90
+  callbacks:
+    - class_path: lightning.pytorch.callbacks.EarlyStopping
+      init_args:
+        patience: 3
   data:
     config:
       train_subset:
diff --git a/src/otx/recipe/classification/multi_class_cls/otx_efficientnet_b0.yaml b/src/otx/recipe/classification/multi_class_cls/otx_efficientnet_b0.yaml
index 8526dc42b0d..678cf451556 100644
--- a/src/otx/recipe/classification/multi_class_cls/otx_efficientnet_b0.yaml
+++ b/src/otx/recipe/classification/multi_class_cls/otx_efficientnet_b0.yaml
@@ -14,10 +14,10 @@ optimizer:
 scheduler:
   class_path: lightning.pytorch.cli.ReduceLROnPlateau
   init_args:
-    mode: min
+    mode: max
     factor: 0.5
     patience: 1
-    monitor: train/loss
+    monitor: val/accuracy
 
 engine:
   task: MULTI_CLASS_CLS
@@ -26,3 +26,10 @@ engine:
 callback_monitor: val/accuracy
 
 data: ../../_base_/data/mmpretrain_base.yaml
+
+overrides:
+  max_epochs: 90
+  callbacks:
+    - class_path: lightning.pytorch.callbacks.EarlyStopping
+      init_args:
+        patience: 3
diff --git a/src/otx/recipe/classification/multi_class_cls/otx_efficientnet_v2.yaml b/src/otx/recipe/classification/multi_class_cls/otx_efficientnet_v2.yaml
index 1dee8cd1331..e0a2db7f69b 100644
--- a/src/otx/recipe/classification/multi_class_cls/otx_efficientnet_v2.yaml
+++ b/src/otx/recipe/classification/multi_class_cls/otx_efficientnet_v2.yaml
@@ -14,10 +14,10 @@ optimizer:
 scheduler:
   class_path: lightning.pytorch.cli.ReduceLROnPlateau
   init_args:
-    mode: min
+    mode: max
     factor: 0.5
     patience: 1
-    monitor: train/loss
+    monitor: val/accuracy
 
 engine:
   task: MULTI_CLASS_CLS
@@ -27,6 +27,11 @@ callback_monitor: val/accuracy
 
 data: ../../_base_/data/mmpretrain_base.yaml
 overrides:
+  max_epochs: 90
+  callbacks:
+    - class_path: lightning.pytorch.callbacks.EarlyStopping
+      init_args:
+        patience: 3
   data:
     config:
       train_subset:
diff --git a/src/otx/recipe/classification/multi_class_cls/otx_mobilenet_v3_large.yaml b/src/otx/recipe/classification/multi_class_cls/otx_mobilenet_v3_large.yaml
index 5c280d6b397..7058f87da0e 100644
--- a/src/otx/recipe/classification/multi_class_cls/otx_mobilenet_v3_large.yaml
+++ b/src/otx/recipe/classification/multi_class_cls/otx_mobilenet_v3_large.yaml
@@ -12,12 +12,15 @@ optimizer:
     weight_decay: 0.0001
 
 scheduler:
-  class_path: lightning.pytorch.cli.ReduceLROnPlateau
-  init_args:
-    mode: min
-    factor: 0.5
-    patience: 1
-    monitor: train/loss
+  - class_path: otx.core.model.module.base.LinearWarmupScheduler
+    init_args:
+      num_warmup_steps: 10
+  - class_path: lightning.pytorch.cli.ReduceLROnPlateau
+    init_args:
+      mode: max
+      factor: 0.5
+      patience: 1
+      monitor: val/accuracy
 
 engine:
   task: MULTI_CLASS_CLS
@@ -27,6 +30,11 @@ callback_monitor: val/accuracy
 
 data: ../../_base_/data/mmpretrain_base.yaml
 overrides:
+  max_epochs: 90
+  callbacks:
+    - class_path: lightning.pytorch.callbacks.EarlyStopping
+      init_args:
+        patience: 3
   data:
     config:
       train_subset:
diff --git a/src/otx/recipe/classification/multi_label_cls/efficientnet_b0_light.yaml b/src/otx/recipe/classification/multi_label_cls/efficientnet_b0_light.yaml
index 7b055bb74d7..e904e4bfe12 100644
--- a/src/otx/recipe/classification/multi_label_cls/efficientnet_b0_light.yaml
+++ b/src/otx/recipe/classification/multi_label_cls/efficientnet_b0_light.yaml
@@ -9,9 +9,8 @@ optimizer:
     lr: 0.0049
 
 scheduler:
-  class_path: otx.algo.schedulers.WarmupReduceLROnPlateau
+  class_path: lightning.pytorch.cli.ReduceLROnPlateau
   init_args:
-    warmup_steps: 0
     mode: max
     factor: 0.5
     patience: 1
@@ -25,6 +24,11 @@ callback_monitor: val/accuracy
 
 data: ../../_base_/data/mmpretrain_base.yaml
 overrides:
+  max_epochs: 90
+  callbacks:
+    - class_path: lightning.pytorch.callbacks.EarlyStopping
+      init_args:
+        patience: 3
   data:
     task: MULTI_LABEL_CLS
     config:
diff --git a/src/otx/recipe/classification/multi_label_cls/efficientnet_v2_light.yaml b/src/otx/recipe/classification/multi_label_cls/efficientnet_v2_light.yaml
index 7517e625995..ab0c328d6b2 100644
--- a/src/otx/recipe/classification/multi_label_cls/efficientnet_v2_light.yaml
+++ b/src/otx/recipe/classification/multi_label_cls/efficientnet_v2_light.yaml
@@ -11,9 +11,8 @@ optimizer:
     weight_decay: 0.0001
 
 scheduler:
-  class_path: otx.algo.schedulers.WarmupReduceLROnPlateau
+  class_path: lightning.pytorch.cli.ReduceLROnPlateau
   init_args:
-    warmup_steps: 0
     mode: max
     factor: 0.5
     patience: 1
@@ -27,6 +26,11 @@ callback_monitor: val/accuracy
 
 data: ../../_base_/data/mmpretrain_base.yaml
 overrides:
+  max_epochs: 90
+  callbacks:
+    - class_path: lightning.pytorch.callbacks.EarlyStopping
+      init_args:
+        patience: 3
   data:
     task: MULTI_LABEL_CLS
     config:
diff --git a/src/otx/recipe/classification/multi_label_cls/mobilenet_v3_large_light.yaml b/src/otx/recipe/classification/multi_label_cls/mobilenet_v3_large_light.yaml
index 78f34991e0d..5f9f82ae0f8 100644
--- a/src/otx/recipe/classification/multi_label_cls/mobilenet_v3_large_light.yaml
+++ b/src/otx/recipe/classification/multi_label_cls/mobilenet_v3_large_light.yaml
@@ -11,13 +11,15 @@ optimizer:
     weight_decay: 0.0001
 
 scheduler:
-  class_path: otx.algo.schedulers.WarmupReduceLROnPlateau
-  init_args:
-    warmup_steps: 10
-    mode: max
-    factor: 0.5
-    patience: 1
-    monitor: val/accuracy
+  - class_path: otx.core.model.module.base.LinearWarmupScheduler
+    init_args:
+      num_warmup_steps: 10
+  - class_path: lightning.pytorch.cli.ReduceLROnPlateau
+    init_args:
+      mode: max
+      factor: 0.5
+      patience: 1
+      monitor: val/accuracy
 
 engine:
   task: MULTI_LABEL_CLS
@@ -27,6 +29,11 @@ callback_monitor: val/accuracy
 
 data: ../../_base_/data/mmpretrain_base.yaml
 overrides:
+  max_epochs: 90
+  callbacks:
+    - class_path: lightning.pytorch.callbacks.EarlyStopping
+      init_args:
+        patience: 3
   data:
     task: MULTI_LABEL_CLS
     config:
diff --git a/src/otx/recipe/classification/multi_label_cls/otx_deit_tiny.yaml b/src/otx/recipe/classification/multi_label_cls/otx_deit_tiny.yaml
index 2c1b64e8452..9e66ece0fbf 100644
--- a/src/otx/recipe/classification/multi_label_cls/otx_deit_tiny.yaml
+++ b/src/otx/recipe/classification/multi_label_cls/otx_deit_tiny.yaml
@@ -10,13 +10,15 @@ optimizer:
     weight_decay: 0.05
 
 scheduler:
-  class_path: otx.algo.schedulers.WarmupReduceLROnPlateau
-  init_args:
-    warmup_steps: 10
-    mode: max
-    factor: 0.5
-    patience: 1
-    monitor: val/accuracy
+  - class_path: otx.core.model.module.base.LinearWarmupScheduler
+    init_args:
+      num_warmup_steps: 10
+  - class_path: lightning.pytorch.cli.ReduceLROnPlateau
+    init_args:
+      mode: max
+      factor: 0.5
+      patience: 1
+      monitor: val/accuracy
 
 engine:
   task: MULTI_LABEL_CLS
@@ -26,6 +28,11 @@ callback_monitor: val/accuracy
 
 data: ../../_base_/data/mmpretrain_base.yaml
 overrides:
+  max_epochs: 90
+  callbacks:
+    - class_path: lightning.pytorch.callbacks.EarlyStopping
+      init_args:
+        patience: 3
   data:
     task: MULTI_LABEL_CLS
     config:
diff --git a/src/otx/recipe/detection/atss_mobilenetv2.yaml b/src/otx/recipe/detection/atss_mobilenetv2.yaml
index c35d5129bda..69d1cd52c7d 100644
--- a/src/otx/recipe/detection/atss_mobilenetv2.yaml
+++ b/src/otx/recipe/detection/atss_mobilenetv2.yaml
@@ -12,13 +12,15 @@ optimizer:
     weight_decay: 0.0001
 
 scheduler:
-  class_path: otx.algo.schedulers.WarmupReduceLROnPlateau
-  init_args:
-    warmup_steps: 3
-    mode: max
-    factor: 0.5
-    patience: 5
-    monitor: val/map_50
+  - class_path: otx.core.model.module.base.LinearWarmupScheduler
+    init_args:
+      num_warmup_steps: 3
+  - class_path: lightning.pytorch.cli.ReduceLROnPlateau
+    init_args:
+      mode: max
+      factor: 0.5
+      patience: 5
+      monitor: val/map_50
 
 engine:
   task: DETECTION
diff --git a/src/otx/recipe/detection/atss_r50_fpn.yaml b/src/otx/recipe/detection/atss_r50_fpn.yaml
index 0f005a3b18d..43a20e292f4 100644
--- a/src/otx/recipe/detection/atss_r50_fpn.yaml
+++ b/src/otx/recipe/detection/atss_r50_fpn.yaml
@@ -10,13 +10,15 @@ optimizer:
     weight_decay: 0.0
 
 scheduler:
-  class_path: otx.algo.schedulers.WarmupReduceLROnPlateau
-  init_args:
-    warmup_steps: 3
-    mode: max
-    factor: 0.5
-    patience: 5
-    monitor: val/map_50
+  - class_path: otx.core.model.module.base.LinearWarmupScheduler
+    init_args:
+      num_warmup_steps: 3
+  - class_path: lightning.pytorch.cli.ReduceLROnPlateau
+    init_args:
+      mode: max
+      factor: 0.5
+      patience: 5
+      monitor: val/map_50
 
 engine:
   task: DETECTION
diff --git a/src/otx/recipe/detection/atss_resnext101.yaml b/src/otx/recipe/detection/atss_resnext101.yaml
index f46baa1c21f..bb0a7b939f9 100644
--- a/src/otx/recipe/detection/atss_resnext101.yaml
+++ b/src/otx/recipe/detection/atss_resnext101.yaml
@@ -12,13 +12,15 @@ optimizer:
     weight_decay: 0.0001
 
 scheduler:
-  class_path: otx.algo.schedulers.WarmupReduceLROnPlateau
-  init_args:
-    warmup_steps: 3
-    mode: max
-    factor: 0.5
-    patience: 5
-    monitor: val/map_50
+  - class_path: otx.core.model.module.base.LinearWarmupScheduler
+    init_args:
+      num_warmup_steps: 3
+  - class_path: lightning.pytorch.cli.ReduceLROnPlateau
+    init_args:
+      mode: max
+      factor: 0.5
+      patience: 5
+      monitor: val/map_50
 
 engine:
   task: DETECTION
diff --git a/src/otx/recipe/detection/ssd_mobilenetv2.yaml b/src/otx/recipe/detection/ssd_mobilenetv2.yaml
index 84350f1e232..09b10bc4eea 100644
--- a/src/otx/recipe/detection/ssd_mobilenetv2.yaml
+++ b/src/otx/recipe/detection/ssd_mobilenetv2.yaml
@@ -12,13 +12,15 @@ optimizer:
     weight_decay: 0.0001
 
 scheduler:
-  class_path: otx.algo.schedulers.WarmupReduceLROnPlateau
-  init_args:
-    warmup_steps: 3
-    mode: max
-    factor: 0.5
-    patience: 5
-    monitor: val/map_50
+  - class_path: otx.core.model.module.base.LinearWarmupScheduler
+    init_args:
+      num_warmup_steps: 3
+  - class_path: lightning.pytorch.cli.ReduceLROnPlateau
+    init_args:
+      mode: max
+      factor: 0.5
+      patience: 5
+      monitor: val/map_50
 
 engine:
   task: DETECTION
diff --git a/src/otx/recipe/detection/yolox_l.yaml b/src/otx/recipe/detection/yolox_l.yaml
index 01c83b61e1d..690f7bfd4f4 100644
--- a/src/otx/recipe/detection/yolox_l.yaml
+++ b/src/otx/recipe/detection/yolox_l.yaml
@@ -12,13 +12,15 @@ optimizer:
     weight_decay: 0.0001
 
 scheduler:
-  class_path: otx.algo.schedulers.WarmupReduceLROnPlateau
-  init_args:
-    warmup_steps: 3
-    mode: max
-    factor: 0.5
-    patience: 5
-    monitor: val/map_50
+  - class_path: otx.core.model.module.base.LinearWarmupScheduler
+    init_args:
+      num_warmup_steps: 3
+  - class_path: lightning.pytorch.cli.ReduceLROnPlateau
+    init_args:
+      mode: max
+      factor: 0.5
+      patience: 5
+      monitor: val/map_50
 
 engine:
   task: DETECTION
diff --git a/src/otx/recipe/detection/yolox_l_tile.yaml b/src/otx/recipe/detection/yolox_l_tile.yaml
index e49efa21de6..af45f977f3b 100644
--- a/src/otx/recipe/detection/yolox_l_tile.yaml
+++ b/src/otx/recipe/detection/yolox_l_tile.yaml
@@ -12,13 +12,15 @@ optimizer:
     weight_decay: 0.0001
 
 scheduler:
-  class_path: otx.algo.schedulers.WarmupReduceLROnPlateau
-  init_args:
-    warmup_steps: 3
-    mode: max
-    factor: 0.5
-    patience: 5
-    monitor: val/map_50
+  - class_path: otx.core.model.module.base.LinearWarmupScheduler
+    init_args:
+      num_warmup_steps: 3
+  - class_path: lightning.pytorch.cli.ReduceLROnPlateau
+    init_args:
+      mode: max
+      factor: 0.5
+      patience: 5
+      monitor: val/map_50
 
 engine:
   task: DETECTION
diff --git a/src/otx/recipe/detection/yolox_s.yaml b/src/otx/recipe/detection/yolox_s.yaml
index c2b5878ad1d..0bf3a268446 100644
--- a/src/otx/recipe/detection/yolox_s.yaml
+++ b/src/otx/recipe/detection/yolox_s.yaml
@@ -12,13 +12,15 @@ optimizer:
     weight_decay: 0.0001
 
 scheduler:
-  class_path: otx.algo.schedulers.WarmupReduceLROnPlateau
-  init_args:
-    warmup_steps: 3
-    mode: max
-    factor: 0.5
-    patience: 5
-    monitor: val/map_50
+  - class_path: otx.core.model.module.base.LinearWarmupScheduler
+    init_args:
+      num_warmup_steps: 3
+  - class_path: lightning.pytorch.cli.ReduceLROnPlateau
+    init_args:
+      mode: max
+      factor: 0.5
+      patience: 5
+      monitor: val/map_50
 
 engine:
   task: DETECTION
diff --git a/src/otx/recipe/detection/yolox_s_tile.yaml b/src/otx/recipe/detection/yolox_s_tile.yaml
index 21da346c6b9..5f44cbe8964 100644
--- a/src/otx/recipe/detection/yolox_s_tile.yaml
+++ b/src/otx/recipe/detection/yolox_s_tile.yaml
@@ -12,13 +12,15 @@ optimizer:
     weight_decay: 0.0001
 
 scheduler:
-  class_path: otx.algo.schedulers.WarmupReduceLROnPlateau
-  init_args:
-    warmup_steps: 3
-    mode: max
-    factor: 0.5
-    patience: 5
-    monitor: val/map_50
+  - class_path: otx.core.model.module.base.LinearWarmupScheduler
+    init_args:
+      num_warmup_steps: 3
+  - class_path: lightning.pytorch.cli.ReduceLROnPlateau
+    init_args:
+      mode: max
+      factor: 0.5
+      patience: 5
+      monitor: val/map_50
 
 engine:
   task: DETECTION
diff --git a/src/otx/recipe/detection/yolox_tiny.yaml b/src/otx/recipe/detection/yolox_tiny.yaml
index fafc40e6bfc..9997a0022e8 100644
--- a/src/otx/recipe/detection/yolox_tiny.yaml
+++ b/src/otx/recipe/detection/yolox_tiny.yaml
@@ -11,13 +11,15 @@ optimizer:
     weight_decay: 0.0001
 
 scheduler:
-  class_path: otx.algo.schedulers.WarmupReduceLROnPlateau
-  init_args:
-    warmup_steps: 3
-    mode: max
-    factor: 0.5
-    patience: 5
-    monitor: val/map_50
+  - class_path: otx.core.model.module.base.LinearWarmupScheduler
+    init_args:
+      num_warmup_steps: 3
+  - class_path: lightning.pytorch.cli.ReduceLROnPlateau
+    init_args:
+      mode: max
+      factor: 0.5
+      patience: 5
+      monitor: val/map_50
 
 engine:
   task: DETECTION
diff --git a/src/otx/recipe/detection/yolox_tiny_tile.yaml b/src/otx/recipe/detection/yolox_tiny_tile.yaml
index f3633769e81..54d37914817 100644
--- a/src/otx/recipe/detection/yolox_tiny_tile.yaml
+++ b/src/otx/recipe/detection/yolox_tiny_tile.yaml
@@ -12,13 +12,15 @@ optimizer:
     weight_decay: 0.0001
 
 scheduler:
-  class_path: otx.algo.schedulers.WarmupReduceLROnPlateau
-  init_args:
-    warmup_steps: 3
-    mode: max
-    factor: 0.5
-    patience: 5
-    monitor: val/map_50
+  - class_path: otx.core.model.module.base.LinearWarmupScheduler
+    init_args:
+      num_warmup_steps: 3
+  - class_path: lightning.pytorch.cli.ReduceLROnPlateau
+    init_args:
+      mode: max
+      factor: 0.5
+      patience: 5
+      monitor: val/map_50
 
 engine:
   task: DETECTION
diff --git a/src/otx/recipe/detection/yolox_x.yaml b/src/otx/recipe/detection/yolox_x.yaml
index cb3496f9e2a..68162a0164d 100644
--- a/src/otx/recipe/detection/yolox_x.yaml
+++ b/src/otx/recipe/detection/yolox_x.yaml
@@ -12,13 +12,15 @@ optimizer:
     weight_decay: 0.0001
 
 scheduler:
-  class_path: otx.algo.schedulers.WarmupReduceLROnPlateau
-  init_args:
-    warmup_steps: 3
-    mode: max
-    factor: 0.5
-    patience: 5
-    monitor: val/map_50
+  - class_path: otx.core.model.module.base.LinearWarmupScheduler
+    init_args:
+      num_warmup_steps: 3
+  - class_path: lightning.pytorch.cli.ReduceLROnPlateau
+    init_args:
+      mode: max
+      factor: 0.5
+      patience: 5
+      monitor: val/map_50
 
 engine:
   task: DETECTION
diff --git a/src/otx/recipe/detection/yolox_x_tile.yaml b/src/otx/recipe/detection/yolox_x_tile.yaml
index d7d23780abf..f5c74413cf8 100644
--- a/src/otx/recipe/detection/yolox_x_tile.yaml
+++ b/src/otx/recipe/detection/yolox_x_tile.yaml
@@ -12,13 +12,15 @@ optimizer:
     weight_decay: 0.0001
 
 scheduler:
-  class_path: otx.algo.schedulers.WarmupReduceLROnPlateau
-  init_args:
-    warmup_steps: 3
-    mode: max
-    factor: 0.5
-    patience: 5
-    monitor: val/map_50
+  - class_path: otx.core.model.module.base.LinearWarmupScheduler
+    init_args:
+      num_warmup_steps: 3
+  - class_path: lightning.pytorch.cli.ReduceLROnPlateau
+    init_args:
+      mode: max
+      factor: 0.5
+      patience: 5
+      monitor: val/map_50
 
 engine:
   task: DETECTION
diff --git a/src/otx/recipe/instance_segmentation/maskrcnn_efficientnetb2b.yaml b/src/otx/recipe/instance_segmentation/maskrcnn_efficientnetb2b.yaml
index 4a207a8f4b6..bdba41b089f 100644
--- a/src/otx/recipe/instance_segmentation/maskrcnn_efficientnetb2b.yaml
+++ b/src/otx/recipe/instance_segmentation/maskrcnn_efficientnetb2b.yaml
@@ -12,13 +12,15 @@ optimizer:
     weight_decay: 0.001
 
 scheduler:
-  class_path: otx.algo.schedulers.WarmupReduceLROnPlateau
-  init_args:
-    warmup_steps: 100
-    mode: max
-    factor: 0.5
-    patience: 5
-    monitor: val/map_50
+  - class_path: otx.core.model.module.base.LinearWarmupScheduler
+    init_args:
+      num_warmup_steps: 100
+  - class_path: lightning.pytorch.cli.ReduceLROnPlateau
+    init_args:
+      mode: max
+      factor: 0.5
+      patience: 5
+      monitor: val/map_50
 
 engine:
   task: INSTANCE_SEGMENTATION
@@ -28,6 +30,7 @@ callback_monitor: val/map_50
 
 data: ../_base_/data/mmdet_base.yaml
 overrides:
+  max_epochs: 100
   data:
     task: INSTANCE_SEGMENTATION
     config:
diff --git a/src/otx/recipe/instance_segmentation/maskrcnn_efficientnetb2b_tile.yaml b/src/otx/recipe/instance_segmentation/maskrcnn_efficientnetb2b_tile.yaml
index aab00bcca18..c795668081c 100644
--- a/src/otx/recipe/instance_segmentation/maskrcnn_efficientnetb2b_tile.yaml
+++ b/src/otx/recipe/instance_segmentation/maskrcnn_efficientnetb2b_tile.yaml
@@ -12,13 +12,15 @@ optimizer:
     weight_decay: 0.001
 
 scheduler:
-  class_path: otx.algo.schedulers.WarmupReduceLROnPlateau
-  init_args:
-    warmup_steps: 100
-    mode: max
-    factor: 0.5
-    patience: 5
-    monitor: val/map_50
+  - class_path: otx.core.model.module.base.LinearWarmupScheduler
+    init_args:
+      num_warmup_steps: 100
+  - class_path: lightning.pytorch.cli.ReduceLROnPlateau
+    init_args:
+      mode: max
+      factor: 0.5
+      patience: 5
+      monitor: val/map_50
 
 engine:
   task: INSTANCE_SEGMENTATION
@@ -28,6 +30,7 @@ callback_monitor: val/map_50
 
 data: ../_base_/data/mmdet_base.yaml
 overrides:
+  max_epochs: 100
   gradient_clip_val: 35.0
   data:
     task: INSTANCE_SEGMENTATION
diff --git a/src/otx/recipe/instance_segmentation/maskrcnn_r50.yaml b/src/otx/recipe/instance_segmentation/maskrcnn_r50.yaml
index 56cdb8cd1d2..2ea4a57884f 100644
--- a/src/otx/recipe/instance_segmentation/maskrcnn_r50.yaml
+++ b/src/otx/recipe/instance_segmentation/maskrcnn_r50.yaml
@@ -12,13 +12,15 @@ optimizer:
     weight_decay: 0.001
 
 scheduler:
-  class_path: otx.algo.schedulers.WarmupReduceLROnPlateau
-  init_args:
-    warmup_steps: 100
-    mode: max
-    factor: 0.5
-    patience: 5
-    monitor: val/map_50
+  - class_path: otx.core.model.module.base.LinearWarmupScheduler
+    init_args:
+      num_warmup_steps: 100
+  - class_path: lightning.pytorch.cli.ReduceLROnPlateau
+    init_args:
+      mode: max
+      factor: 0.5
+      patience: 5
+      monitor: val/map_50
 
 engine:
   task: INSTANCE_SEGMENTATION
@@ -28,6 +30,7 @@ callback_monitor: val/map_50
 
 data: ../_base_/data/mmdet_base.yaml
 overrides:
+  max_epochs: 100
   gradient_clip_val: 35.0
   data:
     task: INSTANCE_SEGMENTATION
diff --git a/src/otx/recipe/instance_segmentation/maskrcnn_r50_tile.yaml b/src/otx/recipe/instance_segmentation/maskrcnn_r50_tile.yaml
index 9375bfff69e..398943e1744 100644
--- a/src/otx/recipe/instance_segmentation/maskrcnn_r50_tile.yaml
+++ b/src/otx/recipe/instance_segmentation/maskrcnn_r50_tile.yaml
@@ -12,13 +12,15 @@ optimizer:
     weight_decay: 0.001
 
 scheduler:
-  class_path: otx.algo.schedulers.WarmupReduceLROnPlateau
-  init_args:
-    warmup_steps: 100
-    mode: max
-    factor: 0.5
-    patience: 5
-    monitor: val/map_50
+  - class_path: otx.core.model.module.base.LinearWarmupScheduler
+    init_args:
+      num_warmup_steps: 100
+  - class_path: lightning.pytorch.cli.ReduceLROnPlateau
+    init_args:
+      mode: max
+      factor: 0.5
+      patience: 5
+      monitor: val/map_50
 
 engine:
   task: INSTANCE_SEGMENTATION
@@ -28,6 +30,7 @@ callback_monitor: val/map_50
 
 data: ../_base_/data/mmdet_base.yaml
 overrides:
+  max_epochs: 100
   gradient_clip_val: 35.0
   data:
     task: INSTANCE_SEGMENTATION
diff --git a/src/otx/recipe/instance_segmentation/maskrcnn_swint.yaml b/src/otx/recipe/instance_segmentation/maskrcnn_swint.yaml
index 5a8a7ddf3e8..8806f46b905 100644
--- a/src/otx/recipe/instance_segmentation/maskrcnn_swint.yaml
+++ b/src/otx/recipe/instance_segmentation/maskrcnn_swint.yaml
@@ -10,13 +10,15 @@ optimizer:
     weight_decay: 0.05
 
 scheduler:
-  class_path: otx.algo.schedulers.WarmupReduceLROnPlateau
-  init_args:
-    warmup_steps: 100
-    mode: max
-    factor: 0.5
-    patience: 5
-    monitor: val/map_50
+  - class_path: otx.core.model.module.base.LinearWarmupScheduler
+    init_args:
+      num_warmup_steps: 100
+  - class_path: lightning.pytorch.cli.ReduceLROnPlateau
+    init_args:
+      mode: max
+      factor: 0.5
+      patience: 5
+      monitor: val/map_50
 
 engine:
   task: INSTANCE_SEGMENTATION
@@ -26,6 +28,7 @@ callback_monitor: val/map_50
 
 data: ../_base_/data/mmdet_base.yaml
 overrides:
+  max_epochs: 100
   data:
     task: INSTANCE_SEGMENTATION
     config:
diff --git a/src/otx/recipe/rotated_detection/maskrcnn_efficientnetb2b.yaml b/src/otx/recipe/rotated_detection/maskrcnn_efficientnetb2b.yaml
index 64e89e0850a..b5f5a9a8cfe 100644
--- a/src/otx/recipe/rotated_detection/maskrcnn_efficientnetb2b.yaml
+++ b/src/otx/recipe/rotated_detection/maskrcnn_efficientnetb2b.yaml
@@ -12,12 +12,15 @@ optimizer:
     weight_decay: 0.001
 
 scheduler:
-  class_path: lightning.pytorch.cli.ReduceLROnPlateau
-  init_args:
-    mode: min
-    factor: 0.1
-    patience: 10
-    monitor: train/loss
+  - class_path: otx.core.model.module.base.LinearWarmupScheduler
+    init_args:
+      num_warmup_steps: 100
+  - class_path: lightning.pytorch.cli.ReduceLROnPlateau
+    init_args:
+      mode: max
+      factor: 0.1
+      patience: 10
+      monitor: val/map_50
 
 engine:
   task: ROTATED_DETECTION
@@ -27,6 +30,7 @@ callback_monitor: val/map_50
 
 data: ../_base_/data/mmdet_base.yaml
 overrides:
+  max_epochs: 100
   data:
     task: ROTATED_DETECTION
     config:
diff --git a/src/otx/recipe/rotated_detection/maskrcnn_r50.yaml b/src/otx/recipe/rotated_detection/maskrcnn_r50.yaml
index 20bdc6c4ec2..415e11b66fa 100644
--- a/src/otx/recipe/rotated_detection/maskrcnn_r50.yaml
+++ b/src/otx/recipe/rotated_detection/maskrcnn_r50.yaml
@@ -12,12 +12,15 @@ optimizer:
     weight_decay: 0.001
 
 scheduler:
-  class_path: lightning.pytorch.cli.ReduceLROnPlateau
-  init_args:
-    mode: min
-    factor: 0.1
-    patience: 10
-    monitor: train/loss
+  - class_path: otx.core.model.module.base.LinearWarmupScheduler
+    init_args:
+      num_warmup_steps: 100
+  - class_path: lightning.pytorch.cli.ReduceLROnPlateau
+    init_args:
+      mode: max
+      factor: 0.1
+      patience: 10
+      monitor: val/map_50
 
 engine:
   task: ROTATED_DETECTION
@@ -27,6 +30,7 @@ callback_monitor: val/map_50
 
 data: ../_base_/data/mmdet_base.yaml
 overrides:
+  max_epochs: 100
   data:
     task: ROTATED_DETECTION
     config:
diff --git a/src/otx/recipe/semantic_segmentation/litehrnet_18.yaml b/src/otx/recipe/semantic_segmentation/litehrnet_18.yaml
index 8956be79ecd..99995b831da 100644
--- a/src/otx/recipe/semantic_segmentation/litehrnet_18.yaml
+++ b/src/otx/recipe/semantic_segmentation/litehrnet_18.yaml
@@ -14,13 +14,15 @@ optimizer:
     weight_decay: 0.0
 
 scheduler:
-  class_path: otx.algo.schedulers.WarmupReduceLROnPlateau
-  init_args:
-    warmup_steps: 100
-    mode: max
-    factor: 0.5
-    patience: 5
-    monitor: val/Dice
+  - class_path: otx.core.model.module.base.LinearWarmupScheduler
+    init_args:
+      num_warmup_steps: 100
+  - class_path: lightning.pytorch.cli.ReduceLROnPlateau
+    init_args:
+      mode: max
+      factor: 0.5
+      patience: 5
+      monitor: val/Dice
 
 engine:
   task: SEMANTIC_SEGMENTATION
@@ -29,3 +31,6 @@ engine:
 callback_monitor: val/Dice
 
 data: ../_base_/data/mmseg_base.yaml
+
+overrides:
+  max_epochs: 300
diff --git a/src/otx/recipe/semantic_segmentation/litehrnet_s.yaml b/src/otx/recipe/semantic_segmentation/litehrnet_s.yaml
index aae8197a084..fdf93f5734c 100644
--- a/src/otx/recipe/semantic_segmentation/litehrnet_s.yaml
+++ b/src/otx/recipe/semantic_segmentation/litehrnet_s.yaml
@@ -14,13 +14,15 @@ optimizer:
     weight_decay: 0.0
 
 scheduler:
-  class_path: otx.algo.schedulers.WarmupReduceLROnPlateau
-  init_args:
-    warmup_steps: 100
-    mode: max
-    factor: 0.5
-    patience: 5
-    monitor: val/Dice
+  - class_path: otx.core.model.module.base.LinearWarmupScheduler
+    init_args:
+      num_warmup_steps: 100
+  - class_path: lightning.pytorch.cli.ReduceLROnPlateau
+    init_args:
+      mode: max
+      factor: 0.5
+      patience: 5
+      monitor: val/Dice
 
 engine:
   task: SEMANTIC_SEGMENTATION
@@ -29,3 +31,6 @@ engine:
 callback_monitor: val/Dice
 
 data: ../_base_/data/mmseg_base.yaml
+
+overrides:
+  max_epochs: 300
diff --git a/src/otx/recipe/semantic_segmentation/litehrnet_x.yaml b/src/otx/recipe/semantic_segmentation/litehrnet_x.yaml
index ec6fa516248..3df08bb4eb3 100644
--- a/src/otx/recipe/semantic_segmentation/litehrnet_x.yaml
+++ b/src/otx/recipe/semantic_segmentation/litehrnet_x.yaml
@@ -14,13 +14,15 @@ optimizer:
     weight_decay: 0.0
 
 scheduler:
-  class_path: otx.algo.schedulers.WarmupReduceLROnPlateau
-  init_args:
-    warmup_steps: 100
-    mode: max
-    factor: 0.5
-    patience: 5
-    monitor: val/Dice
+  - class_path: otx.core.model.module.base.LinearWarmupScheduler
+    init_args:
+      num_warmup_steps: 100
+  - class_path: lightning.pytorch.cli.ReduceLROnPlateau
+    init_args:
+      mode: max
+      factor: 0.5
+      patience: 5
+      monitor: val/Dice
 
 engine:
   task: SEMANTIC_SEGMENTATION
@@ -29,3 +31,6 @@ engine:
 callback_monitor: val/Dice
 
 data: ../_base_/data/mmseg_base.yaml
+
+overrides:
+  max_epochs: 300
diff --git a/src/otx/recipe/semantic_segmentation/segnext_b.yaml b/src/otx/recipe/semantic_segmentation/segnext_b.yaml
index 2501234f6b3..f2330302006 100644
--- a/src/otx/recipe/semantic_segmentation/segnext_b.yaml
+++ b/src/otx/recipe/semantic_segmentation/segnext_b.yaml
@@ -14,12 +14,14 @@ optimizer:
     weight_decay: 0.01
 
 scheduler:
-  class_path: otx.algo.schedulers.warmup_schedulers.WarmupPolynomialLR
-  init_args:
-    warmup_steps: 20
-    total_iters: 100
-    power: 0.9
-    last_epoch: -1
+  - class_path: otx.core.model.module.base.LinearWarmupScheduler
+    init_args:
+      num_warmup_steps: 20
+  - class_path: torch.optim.lr_scheduler.PolynomialLR
+    init_args:
+      total_iters: 100
+      power: 0.9
+      last_epoch: -1
 
 engine:
   task: SEMANTIC_SEGMENTATION
@@ -29,6 +31,7 @@ callback_monitor: val/Dice
 
 data: ../_base_/data/mmseg_base.yaml
 overrides:
+  max_epochs: 170
   data:
     config:
       train_subset:
diff --git a/src/otx/recipe/semantic_segmentation/segnext_s.yaml b/src/otx/recipe/semantic_segmentation/segnext_s.yaml
index e319bc5ada7..7f814f34119 100644
--- a/src/otx/recipe/semantic_segmentation/segnext_s.yaml
+++ b/src/otx/recipe/semantic_segmentation/segnext_s.yaml
@@ -14,12 +14,14 @@ optimizer:
     weight_decay: 0.01
 
 scheduler:
-  class_path: otx.algo.schedulers.warmup_schedulers.WarmupPolynomialLR
-  init_args:
-    warmup_steps: 20
-    total_iters: 100
-    power: 0.9
-    last_epoch: -1
+  - class_path: otx.core.model.module.base.LinearWarmupScheduler
+    init_args:
+      num_warmup_steps: 20
+  - class_path: torch.optim.lr_scheduler.PolynomialLR
+    init_args:
+      total_iters: 100
+      power: 0.9
+      last_epoch: -1
 
 engine:
   task: SEMANTIC_SEGMENTATION
@@ -29,6 +31,7 @@ callback_monitor: val/Dice
 
 data: ../_base_/data/mmseg_base.yaml
 overrides:
+  max_epochs: 170
   data:
     config:
       train_subset:
diff --git a/src/otx/recipe/semantic_segmentation/segnext_t.yaml b/src/otx/recipe/semantic_segmentation/segnext_t.yaml
index e8f657de00d..3de98141813 100644
--- a/src/otx/recipe/semantic_segmentation/segnext_t.yaml
+++ b/src/otx/recipe/semantic_segmentation/segnext_t.yaml
@@ -14,12 +14,14 @@ optimizer:
     weight_decay: 0.01
 
 scheduler:
-  class_path: otx.algo.schedulers.warmup_schedulers.WarmupPolynomialLR
-  init_args:
-    warmup_steps: 20
-    total_iters: 100
-    power: 0.9
-    last_epoch: -1
+  - class_path: otx.core.model.module.base.LinearWarmupScheduler
+    init_args:
+      num_warmup_steps: 20
+  - class_path: torch.optim.lr_scheduler.PolynomialLR
+    init_args:
+      total_iters: 100
+      power: 0.9
+      last_epoch: -1
 
 engine:
   task: SEMANTIC_SEGMENTATION
@@ -29,6 +31,7 @@ callback_monitor: val/Dice
 
 data: ../_base_/data/mmseg_base.yaml
 overrides:
+  max_epochs: 170
   data:
     config:
       train_subset:
diff --git a/tests/unit/algo/callbacks/test_adaptive_train_scheduling.py b/tests/unit/algo/callbacks/test_adaptive_train_scheduling.py
index 0229ea0c2fa..9d019be0f0a 100644
--- a/tests/unit/algo/callbacks/test_adaptive_train_scheduling.py
+++ b/tests/unit/algo/callbacks/test_adaptive_train_scheduling.py
@@ -32,6 +32,7 @@ def test_callback(self, caplog) -> None:
 
         mock_lr_scheduler_config = MagicMock(spec=LRSchedulerConfig)
         mock_lr_scheduler_config.frequency = 1
+        mock_lr_scheduler_config.interval = "epoch"
         mock_trainer.lr_scheduler_configs = [mock_lr_scheduler_config]
 
         with caplog.at_level(log.WARNING):
diff --git a/tests/unit/core/model/module/test_base.py b/tests/unit/core/model/module/test_base.py
index 7436a53a721..bf9961da165 100644
--- a/tests/unit/core/model/module/test_base.py
+++ b/tests/unit/core/model/module/test_base.py
@@ -8,8 +8,8 @@
 from unittest.mock import MagicMock, create_autospec
 
 import pytest
+from lightning.pytorch.cli import ReduceLROnPlateau
 from lightning.pytorch.trainer import Trainer
-from otx.algo.schedulers.warmup_schedulers import WarmupReduceLROnPlateau
 from otx.core.model.entity.base import OTXModel
 from otx.core.model.module.base import LinearWarmupScheduler, OTXLitModule
 from torch.optim import Optimizer
@@ -33,14 +33,19 @@ def optimizer_factory(*args, **kargs) -> Optimizer:  # noqa: ARG001
         return optimizer_factory
 
     @pytest.fixture()
-    def mock_scheduler(self) -> WarmupReduceLROnPlateau:
-        scheduler = MagicMock(spec=WarmupReduceLROnPlateau)
-        scheduler.warmup_steps = 10
+    def mock_scheduler(self) -> list[LinearWarmupScheduler | ReduceLROnPlateau]:
+        scheduler_object_1 = MagicMock()
+        warmup_scheduler = MagicMock(spec=LinearWarmupScheduler)
+        warmup_scheduler.num_warmup_steps = 10
+        warmup_scheduler.interval = "step"
+        scheduler_object_1.return_value = warmup_scheduler
 
-        def scheduler_factory(*args, **kargs) -> WarmupReduceLROnPlateau:  # noqa: ARG001
-            return scheduler
+        scheduler_object_2 = MagicMock()
+        lr_scheduler = MagicMock(spec=ReduceLROnPlateau)
+        lr_scheduler.monitor = "val/loss"
+        scheduler_object_2.return_value = lr_scheduler
 
-        return scheduler_factory
+        return [scheduler_object_1, scheduler_object_2]
 
     def test_configure_optimizers(self, mock_otx_model, mock_optimizer, mock_scheduler) -> None:
         module = OTXLitModule(
@@ -61,7 +66,3 @@ def test_configure_optimizers(self, mock_otx_model, mock_optimizer, mock_schedul
 
         assert "scheduler" in lr_schedulers[1]
         assert "monitor" in lr_schedulers[1]
-        assert "interval" in lr_schedulers[1]
-        assert "frequency" in lr_schedulers[1]
-
-        assert lr_schedulers[1]["frequency"] == 2
diff --git a/tests/unit/engine/utils/test_auto_configurator.py b/tests/unit/engine/utils/test_auto_configurator.py
index aa65edf0a80..0abe3a77207 100644
--- a/tests/unit/engine/utils/test_auto_configurator.py
+++ b/tests/unit/engine/utils/test_auto_configurator.py
@@ -124,9 +124,19 @@ def test_get_model(self) -> None:
     def test_get_optimizer(self) -> None:
         task = OTXTaskType.SEMANTIC_SEGMENTATION
         auto_configurator = AutoConfigurator(task=task)
-        assert callable(auto_configurator.get_optimizer())
+        optimizer = auto_configurator.get_optimizer()
+        if isinstance(optimizer, list):
+            for opt in optimizer:
+                assert callable(opt)
+        else:
+            assert callable(optimizer)
 
     def test_get_scheduler(self) -> None:
         task = OTXTaskType.INSTANCE_SEGMENTATION
         auto_configurator = AutoConfigurator(task=task)
-        assert callable(auto_configurator.get_scheduler())
+        scheduler = auto_configurator.get_scheduler()
+        if isinstance(scheduler, list):
+            for sch in scheduler:
+                assert callable(sch)
+        else:
+            assert callable(scheduler)