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Browse files- tutorial.ipynb +9 -10
tutorial.ipynb
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"id": "eyTZYGgRjnMc"
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"source": [
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"## COCO
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"Download [COCO val 2017](https://github.com/ultralytics/yolov5/blob/74b34872fdf41941cddcf243951cdb090fbac17b/data/coco.yaml#L14) dataset (1GB - 5000 images), and test model accuracy."
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"outputId": "7e6f5c96-c819-43e1-cd03-d3b9878cf8de"
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"source": [
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"# Download COCO
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"torch.hub.download_url_to_file('https://
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"!unzip -q tmp.zip -d ../datasets && rm tmp.zip"
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],
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"execution_count": null,
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"outputId": "3dd0e2fc-aecf-4108-91b1-6392da1863cb"
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"source": [
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"# Run YOLOv5x on COCO
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"!python val.py --weights yolov5x.pt --data coco.yaml --img 640 --iou 0.65 --half"
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],
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"execution_count": null,
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"id": "rc_KbFk0juX2"
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"source": [
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"## COCO test
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"Download [COCO test2017](https://github.com/ultralytics/yolov5/blob/74b34872fdf41941cddcf243951cdb090fbac17b/data/coco.yaml#L15) dataset (7GB - 40,000 images), to test model accuracy on test-dev set (**20,000 images, no labels**). Results are saved to a `*.json` file which should be **zipped** and submitted to the evaluation server at https://competitions.codalab.org/competitions/20794."
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"source": [
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"# Download COCO test-dev2017\n",
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"torch.hub.download_url_to_file('https://
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"!unzip -q tmp.zip -d ../ && rm tmp.zip # unzip labels\n",
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"!f=\"test2017.zip\" && curl http://images.cocodataset.org/zips/$f -o $f && unzip -q $f
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"%mv ./test2017 ../coco/images # move to /coco"
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],
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"execution_count": null,
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"outputs": []
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"id": "29GJXAP_lPrt"
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"source": [
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"# Run YOLOv5s on COCO test
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"!python val.py --weights yolov5s.pt --data coco.yaml --task test"
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],
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"execution_count": null,
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"id": "eyTZYGgRjnMc"
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"source": [
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"## COCO val\n",
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"Download [COCO val 2017](https://github.com/ultralytics/yolov5/blob/74b34872fdf41941cddcf243951cdb090fbac17b/data/coco.yaml#L14) dataset (1GB - 5000 images), and test model accuracy."
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]
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},
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"outputId": "7e6f5c96-c819-43e1-cd03-d3b9878cf8de"
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"source": [
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"# Download COCO val\n",
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"torch.hub.download_url_to_file('https://ultralytics.com/assets/coco2017val.zip', 'tmp.zip')\n",
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"!unzip -q tmp.zip -d ../datasets && rm tmp.zip"
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],
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"execution_count": null,
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"outputId": "3dd0e2fc-aecf-4108-91b1-6392da1863cb"
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},
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"source": [
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"# Run YOLOv5x on COCO val\n",
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"!python val.py --weights yolov5x.pt --data coco.yaml --img 640 --iou 0.65 --half"
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],
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"execution_count": null,
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"id": "rc_KbFk0juX2"
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},
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"source": [
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"## COCO test\n",
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"Download [COCO test2017](https://github.com/ultralytics/yolov5/blob/74b34872fdf41941cddcf243951cdb090fbac17b/data/coco.yaml#L15) dataset (7GB - 40,000 images), to test model accuracy on test-dev set (**20,000 images, no labels**). Results are saved to a `*.json` file which should be **zipped** and submitted to the evaluation server at https://competitions.codalab.org/competitions/20794."
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]
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},
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},
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"source": [
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"# Download COCO test-dev2017\n",
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"torch.hub.download_url_to_file('https://ultralytics.com/assets/coco2017labels.zip', 'tmp.zip')\n",
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"!unzip -q tmp.zip -d ../datasets && rm tmp.zip # unzip labels\n",
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"!f=\"test2017.zip\" && curl http://images.cocodataset.org/zips/$f -o $f && unzip -q $f -d ../datasets/coco/images # 7GB 41k images"
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],
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"execution_count": null,
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"outputs": []
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"id": "29GJXAP_lPrt"
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},
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"source": [
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"# Run YOLOv5s on COCO test\n",
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"!python val.py --weights yolov5s.pt --data coco.yaml --task test"
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],
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"execution_count": null,
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