ball_detector_training.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#get dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "%pip install roboflow\n",
    "%pip install -U ultralytics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "from roboflow import Roboflow\n",
    "rf = Roboflow(api_key=\"dl7jnrQZ5l8ouBCKk8pC\")\n",
    "project = rf.workspace(\"table-tennis-ball-detecting\").project(\"pingpong-2\")\n",
    "version = project.version(3)\n",
    "dataset = version.download(\"yolov5\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import shutil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# shutil.move('pingpong-2-3/train', 'pingpong-2-3/pingpong-2-3/train',)\n",
    "\n",
    "# shutil.move('pingpong-2-3/test', 'pingpong-2-3/pingpong-2-3/test',)\n",
    "\n",
    "# shutil.move('pingpong-2-3/valid', 'pingpong-2-3/pingpong-2-3/valid',)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ultralytics YOLOv8.1.43 🚀 Python-3.12.2 torch-2.2.1+cu118 CUDA:0 (NVIDIA GeForce RTX 3070, 8191MiB)\n",
      "\u001b[34m\u001b[1mengine\\trainer: \u001b[0mtask=detect, mode=train, model=yolov5l6u.pt, data={dataset.location}/data.yaml, epochs=10, time=None, patience=100, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train2, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, bgr=0.0, mosaic=1.0, mixup=0.0, copy_paste=0.0, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs\\detect\\train2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Traceback (most recent call last):\n",
      "  File \"C:\\Users\\Ivan\\Lib\\site-packages\\ultralytics\\engine\\trainer.py\", line 513, in get_dataset\n",
      "    data = check_det_dataset(self.args.data)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"C:\\Users\\Ivan\\Lib\\site-packages\\ultralytics\\data\\utils.py\", line 269, in check_det_dataset\n",
      "    file = check_file(dataset)\n",
      "           ^^^^^^^^^^^^^^^^^^^\n",
      "  File \"C:\\Users\\Ivan\\Lib\\site-packages\\ultralytics\\utils\\checks.py\", line 499, in check_file\n",
      "    raise FileNotFoundError(f\"'{file}' does not exist\")\n",
      "FileNotFoundError: '{dataset.location}/data.yaml' does not exist\n",
      "\n",
      "The above exception was the direct cause of the following exception:\n",
      "\n",
      "Traceback (most recent call last):\n",
      "  File \"<frozen runpy>\", line 198, in _run_module_as_main\n",
      "  File \"<frozen runpy>\", line 88, in _run_code\n",
      "  File \"C:\\Users\\Ivan\\Scripts\\yolo.exe\\__main__.py\", line 7, in <module>\n",
      "  File \"C:\\Users\\Ivan\\Lib\\site-packages\\ultralytics\\cfg\\__init__.py\", line 582, in entrypoint\n",
      "    getattr(model, mode)(**overrides)  # default args from model\n",
      "    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"C:\\Users\\Ivan\\Lib\\site-packages\\ultralytics\\engine\\model.py\", line 648, in train\n",
      "    self.trainer = (trainer or self._smart_load(\"trainer\"))(overrides=args, _callbacks=self.callbacks)\n",
      "                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"C:\\Users\\Ivan\\Lib\\site-packages\\ultralytics\\engine\\trainer.py\", line 129, in __init__\n",
      "    self.trainset, self.testset = self.get_dataset()\n",
      "                                  ^^^^^^^^^^^^^^^^^^\n",
      "  File \"C:\\Users\\Ivan\\Lib\\site-packages\\ultralytics\\engine\\trainer.py\", line 517, in get_dataset\n",
      "    raise RuntimeError(emojis(f\"Dataset '{clean_url(self.args.data)}' error ❌ {e}\")) from e\n",
      "RuntimeError: Dataset '{dataset.location}/data.yaml' error  '{dataset.location}/data.yaml' does not exist\n"
     ]
    }
   ],
   "source": [
    "!yolo task=detect mode=train model=yolov5l6u.pt data={dataset.location}/data.yaml epochs=10 imgsz=640"
   ]
  }
 ],
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