Update tracker docs (#4044)
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Burhan <62214284+Burhan-Q@users.noreply.github.com>
This commit is contained in:
Glenn Jocher
GitHub
@ -59,21 +59,21 @@
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"outputId": "2ea6e0b9-1a62-4355-c246-5e8b7b1dafff"
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"outputId": "27ca383c-0a97-4679-f1c5-ba843f033de7"
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},
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"source": [
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"%pip install ultralytics\n",
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"import ultralytics\n",
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"ultralytics.checks()"
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],
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"execution_count": null,
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"execution_count": 1,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stderr",
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"text": [
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"Ultralytics YOLOv8.0.71 🚀 Python-3.9.16 torch-2.0.0+cu118 CUDA:0 (Tesla T4, 15102MiB)\n",
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"Setup complete ✅ (2 CPUs, 12.7 GB RAM, 23.3/166.8 GB disk)\n"
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"Ultralytics YOLOv8.0.145 🚀 Python-3.10.6 torch-2.0.1+cu118 CUDA:0 (Tesla T4, 15102MiB)\n",
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"Setup complete ✅ (2 CPUs, 12.7 GB RAM, 24.2/78.2 GB disk)\n"
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]
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}
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]
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@ -96,27 +96,27 @@
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"outputId": "c578afbd-47cd-4d11-beec-8b5c31fcfba8"
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"outputId": "64489d1f-e71a-44b5-92f6-2088781ca096"
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},
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"source": [
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"# Run inference on an image with YOLOv8n\n",
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"!yolo predict model=yolov8n.pt source='https://ultralytics.com/images/zidane.jpg'"
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],
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"execution_count": null,
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"execution_count": 2,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt to yolov8n.pt...\n",
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"100% 6.23M/6.23M [00:00<00:00, 195MB/s]\n",
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"Ultralytics YOLOv8.0.71 🚀 Python-3.9.16 torch-2.0.0+cu118 CUDA:0 (Tesla T4, 15102MiB)\n",
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"YOLOv8n summary (fused): 168 layers, 3151904 parameters, 0 gradients, 8.7 GFLOPs\n",
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"Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt to 'yolov8n.pt'...\n",
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"100% 6.23M/6.23M [00:00<00:00, 77.2MB/s]\n",
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"Ultralytics YOLOv8.0.145 🚀 Python-3.10.6 torch-2.0.1+cu118 CUDA:0 (Tesla T4, 15102MiB)\n",
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"YOLOv8n summary (fused): 168 layers, 3151904 parameters, 0 gradients\n",
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"\n",
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"Downloading https://ultralytics.com/images/zidane.jpg to zidane.jpg...\n",
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"100% 165k/165k [00:00<00:00, 51.7MB/s]\n",
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"image 1/1 /content/zidane.jpg: 384x640 2 persons, 1 tie, 60.9ms\n",
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"Speed: 0.6ms preprocess, 60.9ms inference, 301.3ms postprocess per image at shape (1, 3, 640, 640)\n",
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"Downloading https://ultralytics.com/images/zidane.jpg to 'zidane.jpg'...\n",
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"100% 165k/165k [00:00<00:00, 7.46MB/s]\n",
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"image 1/1 /content/zidane.jpg: 384x640 2 persons, 1 tie, 365.8ms\n",
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"Speed: 13.7ms preprocess, 365.8ms inference, 431.7ms postprocess per image at shape (1, 3, 384, 640)\n",
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"Results saved to \u001b[1mruns/detect/predict\u001b[0m\n"
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]
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}
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@ -139,7 +139,7 @@
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},
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"source": [
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"# 2. Val\n",
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"Validate a model's accuracy on the [COCO](https://cocodataset.org/#home) dataset's `val` or `test` splits. The latest YOLOv8 [models](https://github.com/ultralytics/ultralytics#models) are downloaded automatically the first time they are used. See [YOLOv8 Val Docs](https://docs.ultralytics.com/modes/val/) for more information."
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"Validate a model's accuracy on the [COCO](https://docs.ultralytics.com/datasets/detect/coco/) dataset's `val` or `test` splits. The latest YOLOv8 [models](https://github.com/ultralytics/ultralytics#models) are downloaded automatically the first time they are used. See [YOLOv8 Val Docs](https://docs.ultralytics.com/modes/val/) for more information."
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]
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},
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{
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@ -160,108 +160,43 @@
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"cell_type": "code",
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"metadata": {
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"id": "X58w8JLpMnjH",
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"outputId": "3e5a9c48-8eba-45eb-d92f-8456cf94b60e",
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"outputId": "e3aacd98-ceca-49b7-e112-a0c25979ad6c",
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"colab": {
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"base_uri": "https://localhost:8080/"
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}
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},
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"source": [
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"# Validate YOLOv8n on COCO128 val\n",
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"!yolo val model=yolov8n.pt data=coco128.yaml"
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"# Validate YOLOv8n on COCO8 val\n",
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"!yolo val model=yolov8n.pt data=coco8.yaml"
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],
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"execution_count": null,
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"execution_count": 3,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Ultralytics YOLOv8.0.71 🚀 Python-3.9.16 torch-2.0.0+cu118 CUDA:0 (Tesla T4, 15102MiB)\n",
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"YOLOv8n summary (fused): 168 layers, 3151904 parameters, 0 gradients, 8.7 GFLOPs\n",
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"Ultralytics YOLOv8.0.145 🚀 Python-3.10.6 torch-2.0.1+cu118 CUDA:0 (Tesla T4, 15102MiB)\n",
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"YOLOv8n summary (fused): 168 layers, 3151904 parameters, 0 gradients\n",
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"\n",
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"Dataset 'coco128.yaml' images not found ⚠️, missing paths ['/content/datasets/coco128/images/train2017']\n",
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"Downloading https://ultralytics.com/assets/coco128.zip to /content/datasets/coco128.zip...\n",
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"100% 6.66M/6.66M [00:01<00:00, 6.80MB/s]\n",
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"Unzipping /content/datasets/coco128.zip to /content/datasets...\n",
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"Dataset download success ✅ (2.2s), saved to \u001b[1m/content/datasets\u001b[0m\n",
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"Dataset 'coco8.yaml' images not found ⚠️, missing path '/content/datasets/coco8/images/val'\n",
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"Downloading https://ultralytics.com/assets/coco8.zip to '/content/datasets/coco8.zip'...\n",
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"100% 433k/433k [00:00<00:00, 12.4MB/s]\n",
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"Unzipping /content/datasets/coco8.zip to /content/datasets...\n",
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"Dataset download success ✅ (0.7s), saved to \u001b[1m/content/datasets\u001b[0m\n",
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"\n",
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"Downloading https://ultralytics.com/assets/Arial.ttf to /root/.config/Ultralytics/Arial.ttf...\n",
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"100% 755k/755k [00:00<00:00, 107MB/s]\n",
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"\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/coco128/labels/train2017... 126 images, 2 backgrounds, 80 corrupt: 100% 128/128 [00:00<00:00, 1183.28it/s]\n",
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"\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/datasets/coco128/labels/train2017.cache\n",
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" Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:12<00:00, 1.54s/it]\n",
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" all 128 929 0.64 0.537 0.605 0.446\n",
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" person 128 254 0.797 0.677 0.764 0.538\n",
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" bicycle 128 6 0.514 0.333 0.315 0.264\n",
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" car 128 46 0.813 0.217 0.273 0.168\n",
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" motorcycle 128 5 0.687 0.887 0.898 0.685\n",
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" airplane 128 6 0.82 0.833 0.927 0.675\n",
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" bus 128 7 0.491 0.714 0.728 0.671\n",
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" train 128 3 0.534 0.667 0.706 0.604\n",
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" truck 128 12 1 0.332 0.473 0.297\n",
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" boat 128 6 0.226 0.167 0.316 0.134\n",
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" traffic light 128 14 0.734 0.2 0.202 0.139\n",
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" stop sign 128 2 1 0.992 0.995 0.701\n",
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" bench 128 9 0.839 0.582 0.62 0.365\n",
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" bird 128 16 0.921 0.728 0.864 0.51\n",
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" cat 128 4 0.875 1 0.995 0.791\n",
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" dog 128 9 0.603 0.889 0.785 0.585\n",
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" horse 128 2 0.597 1 0.995 0.518\n",
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" elephant 128 17 0.849 0.765 0.9 0.679\n",
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" bear 128 1 0.593 1 0.995 0.995\n",
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" zebra 128 4 0.848 1 0.995 0.965\n",
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" giraffe 128 9 0.72 1 0.951 0.722\n",
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" backpack 128 6 0.589 0.333 0.376 0.232\n",
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" umbrella 128 18 0.804 0.5 0.643 0.414\n",
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" handbag 128 19 0.424 0.0526 0.165 0.0889\n",
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" tie 128 7 0.804 0.714 0.674 0.476\n",
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" suitcase 128 4 0.635 0.883 0.745 0.534\n",
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" frisbee 128 5 0.675 0.8 0.759 0.688\n",
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" skis 128 1 0.567 1 0.995 0.497\n",
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" snowboard 128 7 0.742 0.714 0.747 0.5\n",
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" sports ball 128 6 0.716 0.433 0.485 0.278\n",
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" kite 128 10 0.817 0.45 0.569 0.184\n",
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" baseball bat 128 4 0.551 0.25 0.353 0.175\n",
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" baseball glove 128 7 0.624 0.429 0.429 0.293\n",
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" skateboard 128 5 0.846 0.6 0.6 0.41\n",
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" tennis racket 128 7 0.726 0.387 0.487 0.33\n",
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" bottle 128 18 0.448 0.389 0.376 0.208\n",
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" wine glass 128 16 0.743 0.362 0.584 0.333\n",
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" cup 128 36 0.58 0.278 0.404 0.29\n",
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" fork 128 6 0.527 0.167 0.246 0.184\n",
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" knife 128 16 0.564 0.5 0.59 0.36\n",
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" spoon 128 22 0.597 0.182 0.328 0.19\n",
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" bowl 128 28 0.648 0.643 0.618 0.491\n",
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" banana 128 1 0 0 0.124 0.0379\n",
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" sandwich 128 2 0.249 0.5 0.308 0.308\n",
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" orange 128 4 1 0.31 0.995 0.623\n",
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" broccoli 128 11 0.374 0.182 0.249 0.203\n",
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" carrot 128 24 0.648 0.458 0.572 0.362\n",
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" hot dog 128 2 0.351 0.553 0.745 0.721\n",
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" pizza 128 5 0.644 1 0.995 0.843\n",
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" donut 128 14 0.657 1 0.94 0.864\n",
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" cake 128 4 0.618 1 0.945 0.845\n",
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" chair 128 35 0.506 0.514 0.442 0.239\n",
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" couch 128 6 0.463 0.5 0.706 0.555\n",
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" potted plant 128 14 0.65 0.643 0.711 0.472\n",
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" bed 128 3 0.698 0.667 0.789 0.625\n",
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" dining table 128 13 0.432 0.615 0.485 0.366\n",
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" toilet 128 2 0.615 0.5 0.695 0.676\n",
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" tv 128 2 0.373 0.62 0.745 0.696\n",
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" laptop 128 3 1 0 0.451 0.361\n",
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" mouse 128 2 1 0 0.0625 0.00625\n",
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" remote 128 8 0.843 0.5 0.605 0.529\n",
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" cell phone 128 8 0 0 0.0549 0.0393\n",
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" microwave 128 3 0.435 0.667 0.806 0.718\n",
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" oven 128 5 0.412 0.4 0.339 0.27\n",
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" sink 128 6 0.35 0.167 0.182 0.129\n",
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" refrigerator 128 5 0.589 0.4 0.604 0.452\n",
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" book 128 29 0.629 0.103 0.346 0.178\n",
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" clock 128 9 0.788 0.83 0.875 0.74\n",
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" vase 128 2 0.376 1 0.828 0.795\n",
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" scissors 128 1 1 0 0.249 0.0746\n",
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" teddy bear 128 21 0.877 0.333 0.591 0.394\n",
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" toothbrush 128 5 0.743 0.6 0.638 0.374\n",
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"Speed: 5.3ms preprocess, 20.1ms inference, 0.0ms loss, 11.7ms postprocess per image\n",
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"Downloading https://ultralytics.com/assets/Arial.ttf to '/root/.config/Ultralytics/Arial.ttf'...\n",
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"100% 755k/755k [00:00<00:00, 17.5MB/s]\n",
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"\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/coco8/labels/val... 4 images, 0 backgrounds, 0 corrupt: 100% 4/4 [00:00<00:00, 276.04it/s]\n",
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"\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/datasets/coco8/labels/val.cache\n",
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" Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:03<00:00, 3.84s/it]\n",
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" all 4 17 0.621 0.833 0.888 0.63\n",
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" person 4 10 0.721 0.5 0.519 0.269\n",
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" dog 4 1 0.37 1 0.995 0.597\n",
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" horse 4 2 0.751 1 0.995 0.631\n",
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" elephant 4 2 0.505 0.5 0.828 0.394\n",
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" umbrella 4 1 0.564 1 0.995 0.995\n",
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" potted plant 4 1 0.814 1 0.995 0.895\n",
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"Speed: 0.3ms preprocess, 78.7ms inference, 0.0ms loss, 65.4ms postprocess per image\n",
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"Results saved to \u001b[1mruns/detect/val\u001b[0m\n"
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]
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}
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@ -284,160 +219,98 @@
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"cell_type": "code",
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"metadata": {
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"id": "1NcFxRcFdJ_O",
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"outputId": "b60a1f74-8035-4f9e-b4b0-604f9cf76231",
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"outputId": "b750f2fe-c4d9-4764-b8d5-ed7bd920697b",
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"colab": {
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"base_uri": "https://localhost:8080/"
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}
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},
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"source": [
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"# Train YOLOv8n on COCO128 for 3 epochs\n",
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"!yolo train model=yolov8n.pt data=coco128.yaml epochs=3 imgsz=640"
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"# Train YOLOv8n on COCO8 for 3 epochs\n",
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"!yolo train model=yolov8n.pt data=coco8.yaml epochs=3 imgsz=640"
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],
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"execution_count": null,
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"execution_count": 4,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Ultralytics YOLOv8.0.71 🚀 Python-3.9.16 torch-2.0.0+cu118 CUDA:0 (Tesla T4, 15102MiB)\n",
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"\u001b[34m\u001b[1mengine/trainer: \u001b[0mtask=detect, mode=train, model=yolov8n.pt, data=coco128.yaml, epochs=3, patience=50, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=None, exist_ok=False, pretrained=False, optimizer=SGD, verbose=True, seed=0, deterministic=True, single_cls=False, image_weights=False, rect=False, cos_lr=False, close_mosaic=0, resume=False, amp=True, 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, show=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, vid_stride=1, line_width=3, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, boxes=True, 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, mosaic=1.0, mixup=0.0, copy_paste=0.0, cfg=None, tracker=botsort.yaml, save_dir=runs/detect/train\n",
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"Ultralytics YOLOv8.0.145 🚀 Python-3.10.6 torch-2.0.1+cu118 CUDA:0 (Tesla T4, 15102MiB)\n",
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"\u001b[34m\u001b[1mengine/trainer: \u001b[0mtask=detect, mode=train, model=yolov8n.pt, data=coco8.yaml, epochs=3, patience=50, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=None, 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, 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, show=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, vid_stride=1, line_width=None, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, boxes=True, 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, mosaic=1.0, mixup=0.0, copy_paste=0.0, cfg=None, tracker=botsort.yaml, save_dir=runs/detect/train\n",
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"\n",
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" from n params module arguments \n",
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" 0 -1 1 464 ultralytics.nn.modules.Conv [3, 16, 3, 2] \n",
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" 1 -1 1 4672 ultralytics.nn.modules.Conv [16, 32, 3, 2] \n",
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" 2 -1 1 7360 ultralytics.nn.modules.C2f [32, 32, 1, True] \n",
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" 3 -1 1 18560 ultralytics.nn.modules.Conv [32, 64, 3, 2] \n",
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" 4 -1 2 49664 ultralytics.nn.modules.C2f [64, 64, 2, True] \n",
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" 5 -1 1 73984 ultralytics.nn.modules.Conv [64, 128, 3, 2] \n",
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" 6 -1 2 197632 ultralytics.nn.modules.C2f [128, 128, 2, True] \n",
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" 7 -1 1 295424 ultralytics.nn.modules.Conv [128, 256, 3, 2] \n",
|
||||
" 8 -1 1 460288 ultralytics.nn.modules.C2f [256, 256, 1, True] \n",
|
||||
" 9 -1 1 164608 ultralytics.nn.modules.SPPF [256, 256, 5] \n",
|
||||
" 0 -1 1 464 ultralytics.nn.modules.conv.Conv [3, 16, 3, 2] \n",
|
||||
" 1 -1 1 4672 ultralytics.nn.modules.conv.Conv [16, 32, 3, 2] \n",
|
||||
" 2 -1 1 7360 ultralytics.nn.modules.block.C2f [32, 32, 1, True] \n",
|
||||
" 3 -1 1 18560 ultralytics.nn.modules.conv.Conv [32, 64, 3, 2] \n",
|
||||
" 4 -1 2 49664 ultralytics.nn.modules.block.C2f [64, 64, 2, True] \n",
|
||||
" 5 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2] \n",
|
||||
" 6 -1 2 197632 ultralytics.nn.modules.block.C2f [128, 128, 2, True] \n",
|
||||
" 7 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2] \n",
|
||||
" 8 -1 1 460288 ultralytics.nn.modules.block.C2f [256, 256, 1, True] \n",
|
||||
" 9 -1 1 164608 ultralytics.nn.modules.block.SPPF [256, 256, 5] \n",
|
||||
" 10 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n",
|
||||
" 11 [-1, 6] 1 0 ultralytics.nn.modules.Concat [1] \n",
|
||||
" 12 -1 1 148224 ultralytics.nn.modules.C2f [384, 128, 1] \n",
|
||||
" 11 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1] \n",
|
||||
" 12 -1 1 148224 ultralytics.nn.modules.block.C2f [384, 128, 1] \n",
|
||||
" 13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n",
|
||||
" 14 [-1, 4] 1 0 ultralytics.nn.modules.Concat [1] \n",
|
||||
" 15 -1 1 37248 ultralytics.nn.modules.C2f [192, 64, 1] \n",
|
||||
" 16 -1 1 36992 ultralytics.nn.modules.Conv [64, 64, 3, 2] \n",
|
||||
" 17 [-1, 12] 1 0 ultralytics.nn.modules.Concat [1] \n",
|
||||
" 18 -1 1 123648 ultralytics.nn.modules.C2f [192, 128, 1] \n",
|
||||
" 19 -1 1 147712 ultralytics.nn.modules.Conv [128, 128, 3, 2] \n",
|
||||
" 20 [-1, 9] 1 0 ultralytics.nn.modules.Concat [1] \n",
|
||||
" 21 -1 1 493056 ultralytics.nn.modules.C2f [384, 256, 1] \n",
|
||||
" 22 [15, 18, 21] 1 897664 ultralytics.nn.modules.Detect [80, [64, 128, 256]] \n",
|
||||
"Model summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPs\n",
|
||||
" 14 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1] \n",
|
||||
" 15 -1 1 37248 ultralytics.nn.modules.block.C2f [192, 64, 1] \n",
|
||||
" 16 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2] \n",
|
||||
" 17 [-1, 12] 1 0 ultralytics.nn.modules.conv.Concat [1] \n",
|
||||
" 18 -1 1 123648 ultralytics.nn.modules.block.C2f [192, 128, 1] \n",
|
||||
" 19 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2] \n",
|
||||
" 20 [-1, 9] 1 0 ultralytics.nn.modules.conv.Concat [1] \n",
|
||||
" 21 -1 1 493056 ultralytics.nn.modules.block.C2f [384, 256, 1] \n",
|
||||
" 22 [15, 18, 21] 1 897664 ultralytics.nn.modules.head.Detect [80, [64, 128, 256]] \n",
|
||||
"Model summary: 225 layers, 3157200 parameters, 3157184 gradients\n",
|
||||
"\n",
|
||||
"Transferred 355/355 items from pretrained weights\n",
|
||||
"\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/detect/train', view at http://localhost:6006/\n",
|
||||
"\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks with YOLOv8n...\n",
|
||||
"\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
|
||||
"\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.0005), 63 bias\n",
|
||||
"\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrounds, 80 corrupt: 100% 128/128 [00:00<?, ?it/s]\n",
|
||||
"\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/datasets/coco8/labels/train... 4 images, 0 backgrounds, 0 corrupt: 100% 4/4 [00:00<00:00, 860.11it/s]\n",
|
||||
"\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/datasets/coco8/labels/train.cache\n",
|
||||
"\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n",
|
||||
"\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrounds, 80 corrupt: 100% 128/128 [00:00<?, ?it/s]\n",
|
||||
"\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/coco8/labels/val.cache... 4 images, 0 backgrounds, 0 corrupt: 100% 4/4 [00:00<?, ?it/s]\n",
|
||||
"Plotting labels to runs/detect/train/labels.jpg... \n",
|
||||
"\u001b[34m\u001b[1moptimizer:\u001b[0m AdamW(lr=0.000119, momentum=0.9) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.0005), 63 bias(decay=0.0)\n",
|
||||
"Image sizes 640 train, 640 val\n",
|
||||
"Using 2 dataloader workers\n",
|
||||
"Logging results to \u001b[1mruns/detect/train\u001b[0m\n",
|
||||
"Starting training for 3 epochs...\n",
|
||||
"\n",
|
||||
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
||||
" 1/3 2.78G 1.177 1.338 1.25 230 640: 100% 8/8 [00:06<00:00, 1.21it/s]\n",
|
||||
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 4/4 [00:04<00:00, 1.21s/it]\n",
|
||||
" all 128 929 0.631 0.549 0.614 0.455\n",
|
||||
" 1/3 0.761G 0.9273 3.155 1.291 32 640: 100% 1/1 [00:01<00:00, 1.23s/it]\n",
|
||||
"/usr/local/lib/python3.10/dist-packages/torch/optim/lr_scheduler.py:139: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`. Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n",
|
||||
" warnings.warn(\"Detected call of `lr_scheduler.step()` before `optimizer.step()`. \"\n",
|
||||
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 2.21it/s]\n",
|
||||
" all 4 17 0.613 0.899 0.888 0.621\n",
|
||||
"\n",
|
||||
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
||||
" 2/3 2.69G 1.131 1.405 1.24 179 640: 100% 8/8 [00:02<00:00, 3.13it/s]\n",
|
||||
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 4/4 [00:02<00:00, 1.51it/s]\n",
|
||||
" all 128 929 0.669 0.569 0.634 0.478\n",
|
||||
" 2/3 0.78G 1.161 3.126 1.517 33 640: 100% 1/1 [00:00<00:00, 9.06it/s]\n",
|
||||
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 7.18it/s]\n",
|
||||
" all 4 17 0.601 0.896 0.888 0.613\n",
|
||||
"\n",
|
||||
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
||||
" 3/3 2.84G 1.151 1.281 1.212 214 640: 100% 8/8 [00:02<00:00, 3.27it/s]\n",
|
||||
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 4/4 [00:09<00:00, 2.42s/it]\n",
|
||||
" all 128 929 0.687 0.58 0.65 0.488\n",
|
||||
" 3/3 0.757G 0.9264 2.508 1.254 17 640: 100% 1/1 [00:00<00:00, 7.32it/s]\n",
|
||||
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 5.26it/s]\n",
|
||||
" all 4 17 0.598 0.892 0.886 0.613\n",
|
||||
"\n",
|
||||
"3 epochs completed in 0.010 hours.\n",
|
||||
"3 epochs completed in 0.003 hours.\n",
|
||||
"Optimizer stripped from runs/detect/train/weights/last.pt, 6.5MB\n",
|
||||
"Optimizer stripped from runs/detect/train/weights/best.pt, 6.5MB\n",
|
||||
"\n",
|
||||
"Validating runs/detect/train/weights/best.pt...\n",
|
||||
"Ultralytics YOLOv8.0.71 🚀 Python-3.9.16 torch-2.0.0+cu118 CUDA:0 (Tesla T4, 15102MiB)\n",
|
||||
"Model summary (fused): 168 layers, 3151904 parameters, 0 gradients, 8.7 GFLOPs\n",
|
||||
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 4/4 [00:06<00:00, 1.63s/it]\n",
|
||||
" all 128 929 0.689 0.578 0.65 0.486\n",
|
||||
" person 128 254 0.763 0.673 0.769 0.544\n",
|
||||
" bicycle 128 6 1 0.328 0.379 0.332\n",
|
||||
" car 128 46 0.84 0.217 0.292 0.18\n",
|
||||
" motorcycle 128 5 0.612 0.8 0.872 0.709\n",
|
||||
" airplane 128 6 0.766 0.833 0.894 0.694\n",
|
||||
" bus 128 7 0.748 0.714 0.721 0.675\n",
|
||||
" train 128 3 0.686 1 0.913 0.83\n",
|
||||
" truck 128 12 0.889 0.5 0.529 0.342\n",
|
||||
" boat 128 6 0.393 0.333 0.44 0.216\n",
|
||||
" traffic light 128 14 1 0.21 0.224 0.142\n",
|
||||
" stop sign 128 2 1 0.977 0.995 0.697\n",
|
||||
" bench 128 9 0.795 0.434 0.658 0.418\n",
|
||||
" bird 128 16 0.933 0.868 0.955 0.656\n",
|
||||
" cat 128 4 0.796 1 0.995 0.786\n",
|
||||
" dog 128 9 0.713 0.889 0.823 0.608\n",
|
||||
" horse 128 2 0.576 1 0.995 0.547\n",
|
||||
" elephant 128 17 0.786 0.824 0.911 0.719\n",
|
||||
" bear 128 1 0.432 1 0.995 0.895\n",
|
||||
" zebra 128 4 0.86 1 0.995 0.935\n",
|
||||
" giraffe 128 9 0.966 1 0.995 0.727\n",
|
||||
" backpack 128 6 0.534 0.333 0.399 0.227\n",
|
||||
" umbrella 128 18 0.757 0.519 0.665 0.447\n",
|
||||
" handbag 128 19 0.939 0.105 0.25 0.14\n",
|
||||
" tie 128 7 0.677 0.602 0.682 0.505\n",
|
||||
" suitcase 128 4 0.636 1 0.995 0.646\n",
|
||||
" frisbee 128 5 1 0.789 0.799 0.689\n",
|
||||
" skis 128 1 0.794 1 0.995 0.497\n",
|
||||
" snowboard 128 7 0.575 0.714 0.762 0.48\n",
|
||||
" sports ball 128 6 0.703 0.407 0.514 0.288\n",
|
||||
" kite 128 10 0.645 0.4 0.506 0.206\n",
|
||||
" baseball bat 128 4 0.436 0.404 0.253 0.125\n",
|
||||
" baseball glove 128 7 0.786 0.429 0.43 0.303\n",
|
||||
" skateboard 128 5 0.752 0.6 0.6 0.433\n",
|
||||
" tennis racket 128 7 0.707 0.286 0.508 0.313\n",
|
||||
" bottle 128 18 0.484 0.389 0.43 0.271\n",
|
||||
" wine glass 128 16 0.471 0.562 0.584 0.327\n",
|
||||
" cup 128 36 0.569 0.278 0.404 0.286\n",
|
||||
" fork 128 6 0.529 0.167 0.207 0.192\n",
|
||||
" knife 128 16 0.697 0.562 0.594 0.377\n",
|
||||
" spoon 128 22 0.68 0.182 0.376 0.213\n",
|
||||
" bowl 128 28 0.623 0.679 0.653 0.536\n",
|
||||
" banana 128 1 0 0 0.142 0.0363\n",
|
||||
" sandwich 128 2 1 0 0.745 0.745\n",
|
||||
" orange 128 4 1 0.457 0.849 0.56\n",
|
||||
" broccoli 128 11 0.465 0.273 0.284 0.246\n",
|
||||
" carrot 128 24 0.581 0.751 0.745 0.489\n",
|
||||
" hot dog 128 2 0.654 0.961 0.828 0.763\n",
|
||||
" pizza 128 5 0.631 1 0.995 0.854\n",
|
||||
" donut 128 14 0.583 1 0.933 0.84\n",
|
||||
" cake 128 4 0.643 1 0.995 0.88\n",
|
||||
" chair 128 35 0.5 0.543 0.459 0.272\n",
|
||||
" couch 128 6 0.488 0.5 0.624 0.47\n",
|
||||
" potted plant 128 14 0.645 0.714 0.747 0.542\n",
|
||||
" bed 128 3 0.718 1 0.995 0.798\n",
|
||||
" dining table 128 13 0.448 0.615 0.538 0.437\n",
|
||||
" toilet 128 2 1 0.884 0.995 0.946\n",
|
||||
" tv 128 2 0.548 0.644 0.828 0.762\n",
|
||||
" laptop 128 3 1 0.563 0.72 0.639\n",
|
||||
" mouse 128 2 1 0 0.0623 0.0125\n",
|
||||
" remote 128 8 0.697 0.5 0.578 0.496\n",
|
||||
" cell phone 128 8 0 0 0.102 0.0471\n",
|
||||
" microwave 128 3 0.651 0.667 0.863 0.738\n",
|
||||
" oven 128 5 0.471 0.4 0.415 0.309\n",
|
||||
" sink 128 6 0.45 0.284 0.268 0.159\n",
|
||||
" refrigerator 128 5 0.679 0.4 0.695 0.537\n",
|
||||
" book 128 29 0.656 0.133 0.424 0.227\n",
|
||||
" clock 128 9 0.878 0.778 0.898 0.759\n",
|
||||
" vase 128 2 0.413 1 0.828 0.745\n",
|
||||
" scissors 128 1 1 0 0.199 0.0597\n",
|
||||
" teddy bear 128 21 0.553 0.472 0.669 0.447\n",
|
||||
" toothbrush 128 5 1 0.518 0.8 0.521\n",
|
||||
"Speed: 2.7ms preprocess, 3.5ms inference, 0.0ms loss, 3.2ms postprocess per image\n",
|
||||
"Ultralytics YOLOv8.0.145 🚀 Python-3.10.6 torch-2.0.1+cu118 CUDA:0 (Tesla T4, 15102MiB)\n",
|
||||
"Model summary (fused): 168 layers, 3151904 parameters, 0 gradients\n",
|
||||
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 16.58it/s]\n",
|
||||
" all 4 17 0.613 0.898 0.888 0.621\n",
|
||||
" person 4 10 0.661 0.5 0.52 0.285\n",
|
||||
" dog 4 1 0.337 1 0.995 0.597\n",
|
||||
" horse 4 2 0.723 1 0.995 0.631\n",
|
||||
" elephant 4 2 0.629 0.886 0.828 0.319\n",
|
||||
" umbrella 4 1 0.55 1 0.995 0.995\n",
|
||||
" potted plant 4 1 0.776 1 0.995 0.895\n",
|
||||
"Speed: 0.2ms preprocess, 4.6ms inference, 0.0ms loss, 1.1ms postprocess per image\n",
|
||||
"Results saved to \u001b[1mruns/detect/train\u001b[0m\n"
|
||||
]
|
||||
}
|
||||
@ -454,21 +327,21 @@
|
||||
"- 💡 ProTip: Export to [TensorRT](https://developer.nvidia.com/tensorrt) for up to 5x GPU speedup.\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"| Format | `format` Argument | Model |\n",
|
||||
"|----------------------------------------------------------------------------|-------------------|---------------------------|\n",
|
||||
"| [PyTorch](https://pytorch.org/) | - | `yolov8n.pt` |\n",
|
||||
"| [TorchScript](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `yolov8n.torchscript` |\n",
|
||||
"| [ONNX](https://onnx.ai/) | `onnx` | `yolov8n.onnx` |\n",
|
||||
"| [OpenVINO](https://docs.openvino.ai/latest/index.html) | `openvino` | `yolov8n_openvino_model/` |\n",
|
||||
"| [TensorRT](https://developer.nvidia.com/tensorrt) | `engine` | `yolov8n.engine` |\n",
|
||||
"| [CoreML](https://github.com/apple/coremltools) | `coreml` | `yolov8n.mlmodel` |\n",
|
||||
"| [TensorFlow SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n_saved_model/` |\n",
|
||||
"| [TensorFlow GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n.pb` |\n",
|
||||
"| [TensorFlow Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n.tflite` |\n",
|
||||
"| [TensorFlow Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n_edgetpu.tflite` |\n",
|
||||
"| [TensorFlow.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n_web_model/` |\n",
|
||||
"| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n_paddle_model/` |\n",
|
||||
"| [ncnn](https://github.com/Tencent/ncnn) | `ncnn` | `yolov8n_ncnn_model/` |\n"
|
||||
"| Format | `format` Argument | Model | Metadata | Arguments |\n",
|
||||
"|--------------------------------------------------------------------|-------------------|---------------------------|----------|-----------------------------------------------------|\n",
|
||||
"| [PyTorch](https://pytorch.org/) | - | `yolov8n.pt` | ✅ | - |\n",
|
||||
"| [TorchScript](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `yolov8n.torchscript` | ✅ | `imgsz`, `optimize` |\n",
|
||||
"| [ONNX](https://onnx.ai/) | `onnx` | `yolov8n.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset` |\n",
|
||||
"| [OpenVINO](https://docs.openvino.ai/latest/index.html) | `openvino` | `yolov8n_openvino_model/` | ✅ | `imgsz`, `half` |\n",
|
||||
"| [TensorRT](https://developer.nvidia.com/tensorrt) | `engine` | `yolov8n.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace` |\n",
|
||||
"| [CoreML](https://github.com/apple/coremltools) | `coreml` | `yolov8n.mlmodel` | ✅ | `imgsz`, `half`, `int8`, `nms` |\n",
|
||||
"| [TF SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n_saved_model/` | ✅ | `imgsz`, `keras` |\n",
|
||||
"| [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n.pb` | ❌ | `imgsz` |\n",
|
||||
"| [TF Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n.tflite` | ✅ | `imgsz`, `half`, `int8` |\n",
|
||||
"| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ | `imgsz` |\n",
|
||||
"| [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n_web_model/` | ✅ | `imgsz` |\n",
|
||||
"| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n_paddle_model/` | ✅ | `imgsz` |\n",
|
||||
"| [ncnn](https://github.com/Tencent/ncnn) | `ncnn` | `yolov8n_ncnn_model/` | ✅ | `imgsz`, `half` |\n"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "nPZZeNrLCQG6"
|
||||
@ -484,26 +357,26 @@
|
||||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"id": "CYIjW4igCjqD",
|
||||
"outputId": "fc41bf7a-0ea2-41a6-9ec5-dd0455af43bc"
|
||||
"outputId": "2b65e381-717b-4a6f-d6f5-5254c867f3a4"
|
||||
},
|
||||
"execution_count": null,
|
||||
"execution_count": 5,
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "stream",
|
||||
"name": "stdout",
|
||||
"text": [
|
||||
"Ultralytics YOLOv8.0.71 🚀 Python-3.9.16 torch-2.0.0+cu118 CPU\n",
|
||||
"YOLOv8n summary (fused): 168 layers, 3151904 parameters, 0 gradients, 8.7 GFLOPs\n",
|
||||
"Ultralytics YOLOv8.0.145 🚀 Python-3.10.6 torch-2.0.1+cu118 CPU (Intel Xeon 2.30GHz)\n",
|
||||
"YOLOv8n summary (fused): 168 layers, 3151904 parameters, 0 gradients\n",
|
||||
"\n",
|
||||
"\u001b[34m\u001b[1mPyTorch:\u001b[0m starting from yolov8n.pt with input shape (1, 3, 640, 640) BCHW and output shape(s) (1, 84, 8400) (6.2 MB)\n",
|
||||
"\u001b[34m\u001b[1mPyTorch:\u001b[0m starting from 'yolov8n.pt' with input shape (1, 3, 640, 640) BCHW and output shape(s) (1, 84, 8400) (6.2 MB)\n",
|
||||
"\n",
|
||||
"\u001b[34m\u001b[1mTorchScript:\u001b[0m starting export with torch 2.0.0+cu118...\n",
|
||||
"\u001b[34m\u001b[1mTorchScript:\u001b[0m export success ✅ 2.3s, saved as yolov8n.torchscript (12.4 MB)\n",
|
||||
"\u001b[34m\u001b[1mTorchScript:\u001b[0m starting export with torch 2.0.1+cu118...\n",
|
||||
"\u001b[34m\u001b[1mTorchScript:\u001b[0m export success ✅ 2.8s, saved as 'yolov8n.torchscript' (12.4 MB)\n",
|
||||
"\n",
|
||||
"Export complete (3.1s)\n",
|
||||
"Export complete (4.6s)\n",
|
||||
"Results saved to \u001b[1m/content\u001b[0m\n",
|
||||
"Predict: yolo predict task=detect model=yolov8n.torchscript imgsz=640 \n",
|
||||
"Validate: yolo val task=detect model=yolov8n.torchscript imgsz=640 data=coco.yaml \n",
|
||||
"Validate: yolo val task=detect model=yolov8n.torchscript imgsz=640 data=None \n",
|
||||
"Visualize: https://netron.app\n"
|
||||
]
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user