Update OpenVINO INT8 export (ultralytics#7515)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
puzhiyuan · Jan 11, 2024 · b5cf67c · b5cf67c
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diff --git a/docs/en/datasets/explorer/index.md b/docs/en/datasets/explorer/index.md
@@ -25,7 +25,6 @@ pip install ultralytics[explorer]
 
     Explorer works on embedding/semantic search & SQL querying and is powered by [LanceDB](https://lancedb.com/) serverless vector database. Unlike traditional in-memory DBs, it is persisted on disk without sacrificing performance, so you can scale locally to large datasets like COCO without running out of memory.
 
-
 ### Explorer API
 
 This is a Python API for Exploring your datasets. It also powers the GUI Explorer. You can use this to create your own exploratory notebooks or scripts to get insights into your datasets.
@@ -41,6 +40,7 @@ yolo explorer
 ```
 
 !!! note "Note"
+
     Ask AI feature works using OpenAI, so you'll be prompted to set the api key for OpenAI when you first run the GUI.
     You can set it like this - `yolo settings openai_api_key="..."`
 

diff --git a/docs/en/datasets/index.md b/docs/en/datasets/index.md
@@ -16,7 +16,6 @@ Create embeddings for your dataset, search for similar images, run SQL queries,
 <img width="1709" alt="Screenshot 2024-01-08 at 7 19 48 PM (1)" src="https://github.com/AyushExel/assets/assets/15766192/e536b0eb-6bce-43fe-b800-3e79510d2e5b">
 </p>
 
-
 - Try the [GUI Demo](explorer/index.md)
 - Learn more about the [Explorer API](explorer/index.md)
 

diff --git a/docs/en/modes/benchmark.md b/docs/en/modes/benchmark.md
@@ -91,7 +91,7 @@ Benchmarks will attempt to run automatically on all possible export formats belo
 | [PyTorch](https://pytorch.org/)                                    | -                 | `yolov8n.pt`              | ✅        | -                                                   |
 | [TorchScript](https://pytorch.org/docs/stable/jit.html)            | `torchscript`     | `yolov8n.torchscript`     | ✅        | `imgsz`, `optimize`                                 |
 | [ONNX](https://onnx.ai/)                                           | `onnx`            | `yolov8n.onnx`            | ✅        | `imgsz`, `half`, `dynamic`, `simplify`, `opset`     |
-| [OpenVINO](https://docs.openvino.ai/latest/index.html)             | `openvino`        | `yolov8n_openvino_model/` | ✅        | `imgsz`, `half`, `int8`                             |
+| [OpenVINO](../integrations/openvino.md)                            | `openvino`        | `yolov8n_openvino_model/` | ✅        | `imgsz`, `half`, `int8`                             |
 | [TensorRT](https://developer.nvidia.com/tensorrt)                  | `engine`          | `yolov8n.engine`          | ✅        | `imgsz`, `half`, `dynamic`, `simplify`, `workspace` |
 | [CoreML](https://github.com/apple/coremltools)                     | `coreml`          | `yolov8n.mlpackage`       | ✅        | `imgsz`, `half`, `int8`, `nms`                      |
 | [TF SavedModel](https://www.tensorflow.org/guide/saved_model)      | `saved_model`     | `yolov8n_saved_model/`    | ✅        | `imgsz`, `keras`, `int8`                            |

diff --git a/docs/en/modes/export.md b/docs/en/modes/export.md
@@ -96,7 +96,7 @@ Available YOLOv8 export formats are in the table below. You can export to any fo
 | [PyTorch](https://pytorch.org/)                                    | -                 | `yolov8n.pt`              | ✅        | -                                                   |
 | [TorchScript](https://pytorch.org/docs/stable/jit.html)            | `torchscript`     | `yolov8n.torchscript`     | ✅        | `imgsz`, `optimize`                                 |
 | [ONNX](https://onnx.ai/)                                           | `onnx`            | `yolov8n.onnx`            | ✅        | `imgsz`, `half`, `dynamic`, `simplify`, `opset`     |
-| [OpenVINO](https://docs.openvino.ai/latest/index.html)             | `openvino`        | `yolov8n_openvino_model/` | ✅        | `imgsz`, `half`, `int8`                             |
+| [OpenVINO](../integrations/openvino.md)                            | `openvino`        | `yolov8n_openvino_model/` | ✅        | `imgsz`, `half`, `int8`                             |
 | [TensorRT](https://developer.nvidia.com/tensorrt)                  | `engine`          | `yolov8n.engine`          | ✅        | `imgsz`, `half`, `dynamic`, `simplify`, `workspace` |
 | [CoreML](https://github.com/apple/coremltools)                     | `coreml`          | `yolov8n.mlpackage`       | ✅        | `imgsz`, `half`, `int8`, `nms`                      |
 | [TF SavedModel](https://www.tensorflow.org/guide/saved_model)      | `saved_model`     | `yolov8n_saved_model/`    | ✅        | `imgsz`, `keras`, `int8`                            |

diff --git a/docs/en/modes/predict.md b/docs/en/modes/predict.md
@@ -761,5 +761,7 @@ Here's a Python script using OpenCV (`cv2`) and YOLOv8 to run inference on video
 This script will run predictions on each frame of the video, visualize the results, and display them in a window. The loop can be exited by pressing 'q'.
 
 [car spare parts]: https://github.com/RizwanMunawar/ultralytics/assets/62513924/a0f802a8-0776-44cf-8f17-93974a4a28a1
+
 [football player detect]: https://github.com/RizwanMunawar/ultralytics/assets/62513924/7d320e1f-fc57-4d7f-a691-78ee579c3442
+
 [human fall detect]: https://github.com/RizwanMunawar/ultralytics/assets/62513924/86437c4a-3227-4eee-90ef-9efb697bdb43
diff --git a/docs/en/modes/track.md b/docs/en/modes/track.md
@@ -354,5 +354,7 @@ To initiate your contribution, please refer to our [Contributing Guide](https://
 Together, let's enhance the tracking capabilities of the Ultralytics YOLO ecosystem 🙏!
 
 [fish track]: https://github.com/RizwanMunawar/ultralytics/assets/62513924/a5146d0f-bfa8-4e0a-b7df-3c1446cd8142
+
 [people track]: https://github.com/RizwanMunawar/ultralytics/assets/62513924/93bb4ee2-77a0-4e4e-8eb6-eb8f527f0527
+
 [vehicle track]: https://github.com/RizwanMunawar/ultralytics/assets/62513924/ee6e6038-383b-4f21-ac29-b2a1c7d386ab
diff --git a/docs/en/reference/data/explorer/gui/dash.md b/docs/en/reference/data/explorer/gui/dash.md
@@ -4,7 +4,6 @@ description: Detailed reference for the Explorer GUI. Includes brief description
 keywords: Ultralytics, data explorer, gui, function reference, documentation, AI queries, image similarity, SQL queries, streamlit, semantic search
 ---
 
-
 # Reference for `ultralytics/data/explorer/gui/dash.py`
 
 !!! Note

diff --git a/docs/en/tasks/classify.md b/docs/en/tasks/classify.md
@@ -167,7 +167,7 @@ Available YOLOv8-cls export formats are in the table below. You can predict or v
 | [PyTorch](https://pytorch.org/)                                    | -                 | `yolov8n-cls.pt`              | ✅        | -                                                   |
 | [TorchScript](https://pytorch.org/docs/stable/jit.html)            | `torchscript`     | `yolov8n-cls.torchscript`     | ✅        | `imgsz`, `optimize`                                 |
 | [ONNX](https://onnx.ai/)                                           | `onnx`            | `yolov8n-cls.onnx`            | ✅        | `imgsz`, `half`, `dynamic`, `simplify`, `opset`     |
-| [OpenVINO](https://docs.openvino.ai/latest/index.html)             | `openvino`        | `yolov8n-cls_openvino_model/` | ✅        | `imgsz`, `half`                                     |
+| [OpenVINO](../integrations/openvino.md)                            | `openvino`        | `yolov8n-cls_openvino_model/` | ✅        | `imgsz`, `half`, `int8`                             |
 | [TensorRT](https://developer.nvidia.com/tensorrt)                  | `engine`          | `yolov8n-cls.engine`          | ✅        | `imgsz`, `half`, `dynamic`, `simplify`, `workspace` |
 | [CoreML](https://github.com/apple/coremltools)                     | `coreml`          | `yolov8n-cls.mlpackage`       | ✅        | `imgsz`, `half`, `int8`, `nms`                      |
 | [TF SavedModel](https://www.tensorflow.org/guide/saved_model)      | `saved_model`     | `yolov8n-cls_saved_model/`    | ✅        | `imgsz`, `keras`                                    |

diff --git a/docs/en/tasks/detect.md b/docs/en/tasks/detect.md
@@ -168,7 +168,7 @@ Available YOLOv8 export formats are in the table below. You can predict or valid
 | [PyTorch](https://pytorch.org/)                                    | -                 | `yolov8n.pt`              | ✅        | -                                                   |
 | [TorchScript](https://pytorch.org/docs/stable/jit.html)            | `torchscript`     | `yolov8n.torchscript`     | ✅        | `imgsz`, `optimize`                                 |
 | [ONNX](https://onnx.ai/)                                           | `onnx`            | `yolov8n.onnx`            | ✅        | `imgsz`, `half`, `dynamic`, `simplify`, `opset`     |
-| [OpenVINO](https://docs.openvino.ai/latest/index.html)             | `openvino`        | `yolov8n_openvino_model/` | ✅        | `imgsz`, `half`, `int8`                             |
+| [OpenVINO](../integrations/openvino.md)                            | `openvino`        | `yolov8n_openvino_model/` | ✅        | `imgsz`, `half`, `int8`                             |
 | [TensorRT](https://developer.nvidia.com/tensorrt)                  | `engine`          | `yolov8n.engine`          | ✅        | `imgsz`, `half`, `dynamic`, `simplify`, `workspace` |
 | [CoreML](https://github.com/apple/coremltools)                     | `coreml`          | `yolov8n.mlpackage`       | ✅        | `imgsz`, `half`, `int8`, `nms`                      |
 | [TF SavedModel](https://www.tensorflow.org/guide/saved_model)      | `saved_model`     | `yolov8n_saved_model/`    | ✅        | `imgsz`, `keras`, `int8`                            |

diff --git a/docs/en/tasks/obb.md b/docs/en/tasks/obb.md
@@ -29,13 +29,13 @@ YOLOv8 pretrained OBB models are shown here, which are pretrained on the [DOTAv1
 
 [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models) download automatically from the latest Ultralytics [release](https://github.com/ultralytics/assets/releases) on first use.
 
-| Model                                                                                        | size<br><sup>(pixels) | mAP<sup>test<br>50   | Speed<br><sup>CPU ONNX<br>(ms)   | Speed<br><sup>A100 TensorRT<br>(ms)   | params<br><sup>(M)   | FLOPs<br><sup>(B) |
-|----------------------------------------------------------------------------------------------|-----------------------| -------------------- | -------------------------------- | ------------------------------------- | -------------------- | ----------------- |
-| [YOLOv8n-obb](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8n-obb.pt) | 1024                  | 76.9                 | 204.77                           | 3.57                                  | 3.1                  | 23.3              |
-| [YOLOv8s-obb](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8s-obb.pt) | 1024                  | 78.0                 | 424.88                           | 4.07                                  | 11.4                 | 76.3              |
-| [YOLOv8m-obb](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8m-obb.pt) | 1024                  | 80.5                 | 763.48                           | 7.61                                  | 26.4                 | 208.6             |
-| [YOLOv8l-obb](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8l-obb.pt) | 1024                  | 80.7                 | 1278.42                          | 11.83                                 | 44.5                 | 433.8             |
-| [YOLOv8x-obb](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8x-obb.pt) | 1024                  | 81.36                | 1759.10                          | 13.23                                 | 69.5                 | 676.7             |
+| Model                                                                                        | size<br><sup>(pixels) | mAP<sup>test<br>50 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
+|----------------------------------------------------------------------------------------------|-----------------------|--------------------|--------------------------------|-------------------------------------|--------------------|-------------------|
+| [YOLOv8n-obb](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8n-obb.pt) | 1024                  | 76.9               | 204.77                         | 3.57                                | 3.1                | 23.3              |
+| [YOLOv8s-obb](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8s-obb.pt) | 1024                  | 78.0               | 424.88                         | 4.07                                | 11.4               | 76.3              |
+| [YOLOv8m-obb](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8m-obb.pt) | 1024                  | 80.5               | 763.48                         | 7.61                                | 26.4               | 208.6             |
+| [YOLOv8l-obb](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8l-obb.pt) | 1024                  | 80.7               | 1278.42                        | 11.83                               | 44.5               | 433.8             |
+| [YOLOv8x-obb](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8x-obb.pt) | 1024                  | 81.36              | 1759.10                        | 13.23                               | 69.5               | 676.7             |
 
 - **mAP<sup>test</sup>** values are for single-model multi-scale on [DOTAv1 test](https://captain-whu.github.io/DOTA/index.html) dataset. <br>Reproduce by `yolo val obb data=DOTAv1.yaml device=0 split=test` and submit merged results to [DOTA evaluation](https://captain-whu.github.io/DOTA/evaluation.html).
 - **Speed** averaged over DOTAv1 val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val obb data=DOTAv1.yaml batch=1 device=0|cpu`
@@ -165,7 +165,7 @@ Available YOLOv8-obb export formats are in the table below. You can predict or v
 | [PyTorch](https://pytorch.org/)                                    | -                 | `yolov8n-obb.pt`              | ✅        | -                                                   |
 | [TorchScript](https://pytorch.org/docs/stable/jit.html)            | `torchscript`     | `yolov8n-obb.torchscript`     | ✅        | `imgsz`, `optimize`                                 |
 | [ONNX](https://onnx.ai/)                                           | `onnx`            | `yolov8n-obb.onnx`            | ✅        | `imgsz`, `half`, `dynamic`, `simplify`, `opset`     |
-| [OpenVINO](https://docs.openvino.ai/latest/index.html)             | `openvino`        | `yolov8n-obb_openvino_model/` | ✅        | `imgsz`, `half`                                     |
+| [OpenVINO](../integrations/openvino.md)                            | `openvino`        | `yolov8n-obb_openvino_model/` | ✅        | `imgsz`, `half`, `int8`                             |
 | [TensorRT](https://developer.nvidia.com/tensorrt)                  | `engine`          | `yolov8n-obb.engine`          | ✅        | `imgsz`, `half`, `dynamic`, `simplify`, `workspace` |
 | [CoreML](https://github.com/apple/coremltools)                     | `coreml`          | `yolov8n-obb.mlpackage`       | ✅        | `imgsz`, `half`, `int8`, `nms`                      |
 | [TF SavedModel](https://www.tensorflow.org/guide/saved_model)      | `saved_model`     | `yolov8n-obb_saved_model/`    | ✅        | `imgsz`, `keras`                                    |

diff --git a/docs/en/tasks/pose.md b/docs/en/tasks/pose.md
@@ -170,7 +170,7 @@ Available YOLOv8-pose export formats are in the table below. You can predict or
 | [PyTorch](https://pytorch.org/)                                    | -                 | `yolov8n-pose.pt`              | ✅        | -                                                   |
 | [TorchScript](https://pytorch.org/docs/stable/jit.html)            | `torchscript`     | `yolov8n-pose.torchscript`     | ✅        | `imgsz`, `optimize`                                 |
 | [ONNX](https://onnx.ai/)                                           | `onnx`            | `yolov8n-pose.onnx`            | ✅        | `imgsz`, `half`, `dynamic`, `simplify`, `opset`     |
-| [OpenVINO](https://docs.openvino.ai/latest/index.html)             | `openvino`        | `yolov8n-pose_openvino_model/` | ✅        | `imgsz`, `half`                                     |
+| [OpenVINO](../integrations/openvino.md)                            | `openvino`        | `yolov8n-pose_openvino_model/` | ✅        | `imgsz`, `half`, `int8`                             |
 | [TensorRT](https://developer.nvidia.com/tensorrt)                  | `engine`          | `yolov8n-pose.engine`          | ✅        | `imgsz`, `half`, `dynamic`, `simplify`, `workspace` |
 | [CoreML](https://github.com/apple/coremltools)                     | `coreml`          | `yolov8n-pose.mlpackage`       | ✅        | `imgsz`, `half`, `int8`, `nms`                      |
 | [TF SavedModel](https://www.tensorflow.org/guide/saved_model)      | `saved_model`     | `yolov8n-pose_saved_model/`    | ✅        | `imgsz`, `keras`                                    |

diff --git a/docs/en/tasks/segment.md b/docs/en/tasks/segment.md
@@ -173,7 +173,7 @@ Available YOLOv8-seg export formats are in the table below. You can predict or v
 | [PyTorch](https://pytorch.org/)                                    | -                 | `yolov8n-seg.pt`              | ✅        | -                                                   |
 | [TorchScript](https://pytorch.org/docs/stable/jit.html)            | `torchscript`     | `yolov8n-seg.torchscript`     | ✅        | `imgsz`, `optimize`                                 |
 | [ONNX](https://onnx.ai/)                                           | `onnx`            | `yolov8n-seg.onnx`            | ✅        | `imgsz`, `half`, `dynamic`, `simplify`, `opset`     |
-| [OpenVINO](https://docs.openvino.ai/latest/index.html)             | `openvino`        | `yolov8n-seg_openvino_model/` | ✅        | `imgsz`, `half`                                     |
+| [OpenVINO](../integrations/openvino.md)                            | `openvino`        | `yolov8n-seg_openvino_model/` | ✅        | `imgsz`, `half`, `int8`                             |
 | [TensorRT](https://developer.nvidia.com/tensorrt)                  | `engine`          | `yolov8n-seg.engine`          | ✅        | `imgsz`, `half`, `dynamic`, `simplify`, `workspace` |
 | [CoreML](https://github.com/apple/coremltools)                     | `coreml`          | `yolov8n-seg.mlpackage`       | ✅        | `imgsz`, `half`, `int8`, `nms`                      |
 | [TF SavedModel](https://www.tensorflow.org/guide/saved_model)      | `saved_model`     | `yolov8n-seg_saved_model/`    | ✅        | `imgsz`, `keras`                                    |

diff --git a/docs/en/usage/cli.md b/docs/en/usage/cli.md
@@ -175,7 +175,7 @@ Available YOLOv8 export formats are in the table below. You can export to any fo
 | [PyTorch](https://pytorch.org/)                                    | -                 | `yolov8n.pt`              | ✅        | -                                                   |
 | [TorchScript](https://pytorch.org/docs/stable/jit.html)            | `torchscript`     | `yolov8n.torchscript`     | ✅        | `imgsz`, `optimize`                                 |
 | [ONNX](https://onnx.ai/)                                           | `onnx`            | `yolov8n.onnx`            | ✅        | `imgsz`, `half`, `dynamic`, `simplify`, `opset`     |
-| [OpenVINO](https://docs.openvino.ai/latest/index.html)             | `openvino`        | `yolov8n_openvino_model/` | ✅        | `imgsz`, `half`, `int8`                             |
+| [OpenVINO](../integrations/openvino.md)                            | `openvino`        | `yolov8n_openvino_model/` | ✅        | `imgsz`, `half`, `int8`                             |
 | [TensorRT](https://developer.nvidia.com/tensorrt)                  | `engine`          | `yolov8n.engine`          | ✅        | `imgsz`, `half`, `dynamic`, `simplify`, `workspace` |
 | [CoreML](https://github.com/apple/coremltools)                     | `coreml`          | `yolov8n.mlpackage`       | ✅        | `imgsz`, `half`, `int8`, `nms`                      |
 | [TF SavedModel](https://www.tensorflow.org/guide/saved_model)      | `saved_model`     | `yolov8n_saved_model/`    | ✅        | `imgsz`, `keras`, `int8`                            |

diff --git a/docs/en/usage/python.md b/docs/en/usage/python.md
@@ -286,7 +286,6 @@ Explorer API can be used to explore datasets with advanced semantic, vector-simi
 
 [Explorer](../datasets/explorer/index.md){ .md-button }
 
-
 ## Using Trainers
 
 `YOLO` model class is a high-level wrapper on the Trainer classes. Each YOLO task has its own trainer that inherits from `BaseTrainer`.
-Original file line number
+Diff line change
@@ Expand Up @@
     <img width="1709" alt="Screenshot 2024-01-08 at 7 19 48 PM (1)" src="https://github.com/AyushExel/assets/assets/15766192/e536b0eb-6bce-43fe-b800-3e79510d2e5b">
     </p>
     - Try the [GUI Demo](explorer/index.md)
     - Learn more about the [Explorer API](explorer/index.md)
@@ Expand Down @@
Original file line number	Diff line number	Diff line change
Expand Up		@@ -286,7 +286,6 @@ Explorer API can be used to explore datasets with advanced semantic, vector-simi

		[Explorer](../datasets/explorer/index.md){ .md-button }


		## Using Trainers

		`YOLO` model class is a high-level wrapper on the Trainer classes. Each YOLO task has its own trainer that inherits from `BaseTrainer`.
Expand Down