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ultralytics 8.0.58 new SimpleClass, fixes and updates (#1636)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>
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Glenn Jocher
2023-03-26 22:16:38 +02:00
committed by GitHub
parent ef03e6732a
commit ec10002a4a
30 changed files with 351 additions and 314 deletions
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examples/tutorial.ipynb
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@ -59,21 +59,21 @@
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "9bda69d4-e57f-404b-b6fe-117234e24677"
"outputId": "ea235da2-8fb5-4094-9dc2-8523d0800a22"
},
"source": [
"%pip install ultralytics\n",
"import ultralytics\n",
"ultralytics.checks()"
],
"execution_count": null,
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Ultralytics YOLOv8.0.24 🚀 Python-3.8.10 torch-1.13.1+cu116 CUDA:0 (Tesla T4, 15110MiB)\n",
"Setup complete ✅ (2 CPUs, 12.7 GB RAM, 30.8/166.8 GB disk)\n"
"Ultralytics YOLOv8.0.57 🚀 Python-3.9.16 torch-1.13.1+cu116 CUDA:0 (Tesla T4, 15102MiB)\n",
"Setup complete ✅ (2 CPUs, 12.7 GB RAM, 25.9/166.8 GB disk)\n"
]
}
]
@ -96,28 +96,24 @@
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "abe002b5-3df9-4324-9e50-1587394398a2"
"outputId": "fe0a5a26-3bcc-4c1f-e688-cae00ee5b958"
},
"source": [
"# Run inference on an image with YOLOv8n\n",
"!yolo predict model=yolov8n.pt source='https://ultralytics.com/images/zidane.jpg'"
],
"execution_count": null,
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt to yolov8n.pt...\n",
"\r 0% 0.00/6.23M [00:00<?, ?B/s]\r100% 6.23M/6.23M [00:00<00:00, 266MB/s]\n",
"Ultralytics YOLOv8.0.24 🚀 Python-3.8.10 torch-1.13.1+cu116 CUDA:0 (Tesla T4, 15110MiB)\n",
"Ultralytics YOLOv8.0.57 🚀 Python-3.9.16 torch-1.13.1+cu116 CUDA:0 (Tesla T4, 15102MiB)\n",
"YOLOv8n summary (fused): 168 layers, 3151904 parameters, 0 gradients, 8.7 GFLOPs\n",
"\n",
"Downloading https://ultralytics.com/images/zidane.jpg to zidane.jpg...\n",
"100% 165k/165k [00:00<00:00, 87.4MB/s]\n",
"image 1/1 /content/zidane.jpg: 384x640 2 persons, 1 tie, 13.3ms\n",
"Speed: 0.5ms preprocess, 13.3ms inference, 43.5ms postprocess per image at shape (1, 3, 640, 640)\n",
"Results saved to \u001b[1mruns/detect/predict\u001b[0m\n"
"Found https://ultralytics.com/images/zidane.jpg locally at zidane.jpg\n",
"image 1/1 /content/zidane.jpg: 384x640 2 persons, 1 tie, 14.3ms\n",
"Speed: 0.5ms preprocess, 14.3ms inference, 1.8ms postprocess per image at shape (1, 3, 640, 640)\n"
]
}
]
@ -160,7 +156,7 @@
"cell_type": "code",
"metadata": {
"id": "X58w8JLpMnjH",
"outputId": "df71d7a8-f42f-473a-c143-75f033c58433",
"outputId": "ae2040df-0f95-4701-c680-8bbb7be92bcd",
"colab": {
"base_uri": "https://localhost:8080/"
}
@ -169,26 +165,26 @@
"# Validate YOLOv8n on COCO128 val\n",
"!yolo val model=yolov8n.pt data=coco128.yaml"
],
"execution_count": null,
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Ultralytics YOLOv8.0.24 🚀 Python-3.8.10 torch-1.13.1+cu116 CUDA:0 (Tesla T4, 15110MiB)\n",
"Ultralytics YOLOv8.0.57 🚀 Python-3.9.16 torch-1.13.1+cu116 CUDA:0 (Tesla T4, 15102MiB)\n",
"YOLOv8n summary (fused): 168 layers, 3151904 parameters, 0 gradients, 8.7 GFLOPs\n",
"\n",
"Dataset 'coco128.yaml' not found ⚠️, missing paths ['/content/datasets/coco128/images/train2017']\n",
"Dataset 'coco128.yaml' images not found ⚠️, missing paths ['/content/datasets/coco128/images/train2017']\n",
"Downloading https://ultralytics.com/assets/coco128.zip to /content/datasets/coco128.zip...\n",
"100% 6.66M/6.66M [00:00<00:00, 50.5MB/s]\n",
"Unzipping /content/datasets/coco128.zip...\n",
"Dataset download success ✅ (0.5s), saved to \u001b[1m/content/datasets\u001b[0m\n",
"100% 6.66M/6.66M [00:00<00:00, 87.2MB/s]\n",
"Unzipping /content/datasets/coco128.zip to /content/datasets...\n",
"Dataset download success ✅ (0.4s), saved to \u001b[1m/content/datasets\u001b[0m\n",
"\n",
"Downloading https://ultralytics.com/assets/Arial.ttf to /root/.config/Ultralytics/Arial.ttf...\n",
"100% 755k/755k [00:00<00:00, 99.8MB/s]\n",
"\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/coco128/labels/train2017... 126 images, 2 backgrounds, 0 corrupt: 100% 128/128 [00:00<00:00, 1290.29it/s]\n",
"100% 755k/755k [00:00<00:00, 16.9MB/s]\n",
"\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/coco128/labels/train2017... 126 images, 2 backgrounds, 0 corrupt: 100% 128/128 [00:00<00:00, 2007.12it/s]\n",
"\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/datasets/coco128/labels/train2017.cache\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:06<00:00, 1.22it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 8/8 [00:08<00:00, 1.04s/it]\n",
" all 128 929 0.64 0.537 0.605 0.446\n",
" person 128 254 0.797 0.677 0.764 0.538\n",
" bicycle 128 6 0.514 0.333 0.315 0.264\n",
@ -261,7 +257,8 @@
" scissors 128 1 1 0 0.249 0.0746\n",
" teddy bear 128 21 0.877 0.333 0.591 0.394\n",
" toothbrush 128 5 0.743 0.6 0.638 0.374\n",
"Speed: 2.4ms preprocess, 7.8ms inference, 0.0ms loss, 3.3ms postprocess per image\n"
"Speed: 2.9ms preprocess, 6.2ms inference, 0.0ms loss, 5.1ms postprocess per image\n",
"Results saved to \u001b[1mruns/detect/val\u001b[0m\n"
]
}
]
@ -283,7 +280,7 @@
"cell_type": "code",
"metadata": {
"id": "1NcFxRcFdJ_O",
"outputId": "e0978a9a-ef1d-4a20-8082-19c8049d8c7e",
"outputId": "fcb5e3da-3766-4c72-97e1-73c1bd2ccbef",
"colab": {
"base_uri": "https://localhost:8080/"
}
@ -292,14 +289,14 @@
"# Train YOLOv8n on COCO128 for 3 epochs\n",
"!yolo train model=yolov8n.pt data=coco128.yaml epochs=3 imgsz=640"
],
"execution_count": null,
"execution_count": 5,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Ultralytics YOLOv8.0.24 🚀 Python-3.8.10 torch-1.13.1+cu116 CUDA:0 (Tesla T4, 15110MiB)\n",
"\u001b[34m\u001b[1myolo/engine/trainer: \u001b[0mtask=detect, mode=train, model=yolov8n.pt, data=coco128.yaml, epochs=3, patience=50, batch=16, imgsz=640, save=True, 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=10, resume=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, 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, hide_labels=False, hide_conf=False, vid_stride=1, line_thickness=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, fl_gamma=0.0, 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, v5loader=False, save_dir=runs/detect/train\n",
"Ultralytics YOLOv8.0.57 🚀 Python-3.9.16 torch-1.13.1+cu116 CUDA:0 (Tesla T4, 15102MiB)\n",
"\u001b[34m\u001b[1myolo/engine/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=10, 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, hide_labels=False, hide_conf=False, vid_stride=1, line_thickness=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, fl_gamma=0.0, 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, v5loader=False, tracker=botsort.yaml, save_dir=runs/detect/train\n",
"\n",
" from n params module arguments \n",
" 0 -1 1 464 ultralytics.nn.modules.Conv [3, 16, 3, 2] \n",
@ -328,111 +325,120 @@
"Model summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPs\n",
"\n",
"Transferred 355/355 items from pretrained weights\n",
"2023-03-26 14:57:47.224672: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n",
"To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
"2023-03-26 14:57:48.209047: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/lib/python3.9/dist-packages/cv2/../../lib64:/usr/local/lib/python3.9/dist-packages/cv2/../../lib64:/usr/lib64-nvidia\n",
"2023-03-26 14:57:48.209179: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/lib/python3.9/dist-packages/cv2/../../lib64:/usr/local/lib/python3.9/dist-packages/cv2/../../lib64:/usr/lib64-nvidia\n",
"2023-03-26 14:57:48.209199: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\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, 0 corrupt: 100% 128/128 [00:00<?, ?it/s]\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, 0 corrupt: 100% 128/128 [00:00<?, ?it/s]\n",
"Plotting labels to runs/detect/train/labels.jpg... \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 4.31G 1.221 1.429 1.241 196 640: 100% 8/8 [00:08<00:00, 1.01s/it]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 4/4 [00:01<00:00, 2.18it/s]\n",
" all 128 929 0.671 0.516 0.617 0.457\n",
" 1/3 2.77G 1.221 1.429 1.241 196 640: 100% 8/8 [00:10<00:00, 1.34s/it]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 4/4 [00:02<00:00, 1.70it/s]\n",
" all 128 929 0.674 0.517 0.616 0.456\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 2/3 5.31G 1.186 1.306 1.255 287 640: 100% 8/8 [00:04<00:00, 1.63it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 4/4 [00:01<00:00, 2.23it/s]\n",
" all 128 929 0.668 0.582 0.637 0.473\n",
" 2/3 3.61G 1.186 1.306 1.255 287 640: 100% 8/8 [00:05<00:00, 1.58it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 4/4 [00:02<00:00, 1.83it/s]\n",
" all 128 929 0.644 0.599 0.638 0.473\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 3/3 5.31G 1.17 1.408 1.267 189 640: 100% 8/8 [00:04<00:00, 1.64it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 4/4 [00:03<00:00, 1.03it/s]\n",
" all 128 929 0.638 0.601 0.645 0.483\n",
" 3/3 3.61G 1.169 1.405 1.266 189 640: 100% 8/8 [00:04<00:00, 1.65it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 4/4 [00:06<00:00, 1.59s/it]\n",
" all 128 929 0.63 0.607 0.641 0.48\n",
"\n",
"3 epochs completed in 0.009 hours.\n",
"3 epochs completed in 0.011 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 YOLOv8.0.24 🚀 Python-3.8.10 torch-1.13.1+cu116 CUDA:0 (Tesla T4, 15110MiB)\n",
"Ultralytics YOLOv8.0.57 🚀 Python-3.9.16 torch-1.13.1+cu116 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 [00:04<00:00, 1.05s/it]\n",
" all 128 929 0.638 0.602 0.644 0.483\n",
" person 128 254 0.703 0.709 0.769 0.548\n",
" bicycle 128 6 0.455 0.333 0.322 0.254\n",
" car 128 46 0.773 0.217 0.291 0.184\n",
" motorcycle 128 5 0.551 0.8 0.895 0.724\n",
" airplane 128 6 0.743 0.833 0.927 0.73\n",
" bus 128 7 0.692 0.714 0.7 0.636\n",
" train 128 3 0.733 0.931 0.913 0.797\n",
" truck 128 12 0.752 0.5 0.497 0.324\n",
" boat 128 6 0.41 0.333 0.492 0.344\n",
" traffic light 128 14 0.682 0.214 0.202 0.139\n",
" stop sign 128 2 0.933 1 0.995 0.671\n",
" bench 128 9 0.752 0.556 0.603 0.416\n",
" bird 128 16 0.875 0.876 0.957 0.641\n",
" cat 128 4 0.863 1 0.995 0.76\n",
" dog 128 9 0.554 0.778 0.855 0.664\n",
" horse 128 2 0.706 1 0.995 0.561\n",
" elephant 128 17 0.761 0.882 0.929 0.722\n",
" bear 128 1 0.595 1 0.995 0.995\n",
" zebra 128 4 0.85 1 0.995 0.966\n",
" giraffe 128 9 0.891 1 0.995 0.683\n",
" backpack 128 6 0.487 0.333 0.354 0.224\n",
" umbrella 128 18 0.54 0.667 0.687 0.461\n",
" handbag 128 19 0.496 0.105 0.212 0.125\n",
" tie 128 7 0.611 0.714 0.615 0.432\n",
" suitcase 128 4 0.469 1 0.745 0.529\n",
" frisbee 128 5 0.622 0.8 0.733 0.64\n",
" skis 128 1 0.721 1 0.995 0.531\n",
" snowboard 128 7 0.687 0.714 0.751 0.51\n",
" sports ball 128 6 0.71 0.42 0.503 0.282\n",
" kite 128 10 0.81 0.5 0.59 0.197\n",
" baseball bat 128 4 0.474 0.461 0.261 0.115\n",
" baseball glove 128 7 0.67 0.429 0.43 0.317\n",
" skateboard 128 5 0.751 0.6 0.599 0.387\n",
" tennis racket 128 7 0.742 0.415 0.507 0.378\n",
" bottle 128 18 0.409 0.333 0.354 0.235\n",
" wine glass 128 16 0.562 0.5 0.597 0.356\n",
" cup 128 36 0.67 0.306 0.411 0.296\n",
" fork 128 6 0.57 0.167 0.229 0.203\n",
" knife 128 16 0.608 0.562 0.634 0.405\n",
" spoon 128 22 0.529 0.358 0.369 0.201\n",
" bowl 128 28 0.594 0.679 0.671 0.56\n",
" banana 128 1 0.0625 0.312 0.199 0.0513\n",
" sandwich 128 2 0.638 0.913 0.828 0.828\n",
" orange 128 4 0.743 0.728 0.895 0.595\n",
" broccoli 128 11 0.49 0.264 0.278 0.232\n",
" carrot 128 24 0.547 0.667 0.704 0.47\n",
" hot dog 128 2 0.578 1 0.828 0.796\n",
" pizza 128 5 0.835 1 0.995 0.84\n",
" donut 128 14 0.537 1 0.891 0.788\n",
" cake 128 4 0.807 1 0.995 0.904\n",
" chair 128 35 0.401 0.514 0.485 0.277\n",
" couch 128 6 0.795 0.649 0.746 0.504\n",
" potted plant 128 14 0.563 0.643 0.676 0.471\n",
" bed 128 3 0.777 1 0.995 0.735\n",
" dining table 128 13 0.425 0.692 0.578 0.48\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 4/4 [00:06<00:00, 1.66s/it]\n",
" all 128 929 0.632 0.597 0.639 0.479\n",
" person 128 254 0.708 0.693 0.766 0.55\n",
" bicycle 128 6 0.458 0.333 0.323 0.254\n",
" car 128 46 0.763 0.217 0.287 0.181\n",
" motorcycle 128 5 0.542 0.8 0.895 0.727\n",
" airplane 128 6 0.744 0.833 0.903 0.695\n",
" bus 128 7 0.699 0.714 0.724 0.64\n",
" train 128 3 0.73 0.919 0.913 0.797\n",
" truck 128 12 0.747 0.5 0.497 0.324\n",
" boat 128 6 0.365 0.333 0.393 0.291\n",
" traffic light 128 14 0.708 0.214 0.202 0.139\n",
" stop sign 128 2 1 0.953 0.995 0.666\n",
" bench 128 9 0.754 0.556 0.606 0.412\n",
" bird 128 16 0.929 0.819 0.948 0.643\n",
" cat 128 4 0.864 1 0.995 0.778\n",
" dog 128 9 0.598 0.828 0.834 0.627\n",
" horse 128 2 0.679 1 0.995 0.547\n",
" elephant 128 17 0.757 0.882 0.929 0.722\n",
" bear 128 1 0.593 1 0.995 0.995\n",
" zebra 128 4 0.851 1 0.995 0.966\n",
" giraffe 128 9 0.838 1 0.984 0.681\n",
" backpack 128 6 0.473 0.333 0.342 0.217\n",
" umbrella 128 18 0.569 0.667 0.708 0.454\n",
" handbag 128 19 0.649 0.105 0.233 0.137\n",
" tie 128 7 0.608 0.714 0.614 0.428\n",
" suitcase 128 4 0.471 1 0.745 0.54\n",
" frisbee 128 5 0.629 0.8 0.732 0.64\n",
" skis 128 1 0.699 1 0.995 0.522\n",
" snowboard 128 7 0.654 0.714 0.758 0.497\n",
" sports ball 128 6 0.707 0.415 0.515 0.288\n",
" kite 128 10 0.687 0.4 0.561 0.207\n",
" baseball bat 128 4 0.439 0.409 0.263 0.114\n",
" baseball glove 128 7 0.679 0.429 0.43 0.317\n",
" skateboard 128 5 0.738 0.6 0.599 0.386\n",
" tennis racket 128 7 0.738 0.408 0.493 0.371\n",
" bottle 128 18 0.44 0.333 0.377 0.247\n",
" wine glass 128 16 0.545 0.5 0.596 0.358\n",
" cup 128 36 0.651 0.278 0.412 0.297\n",
" fork 128 6 0.568 0.167 0.229 0.202\n",
" knife 128 16 0.557 0.562 0.628 0.399\n",
" spoon 128 22 0.471 0.318 0.369 0.214\n",
" bowl 128 28 0.611 0.679 0.671 0.547\n",
" banana 128 1 0.0573 0.286 0.199 0.0522\n",
" sandwich 128 2 0.377 0.5 0.745 0.745\n",
" orange 128 4 0.743 0.729 0.895 0.595\n",
" broccoli 128 11 0.491 0.265 0.281 0.233\n",
" carrot 128 24 0.548 0.667 0.694 0.465\n",
" hot dog 128 2 0.586 1 0.828 0.796\n",
" pizza 128 5 0.873 1 0.995 0.798\n",
" donut 128 14 0.554 1 0.891 0.786\n",
" cake 128 4 0.806 1 0.995 0.904\n",
" chair 128 35 0.408 0.514 0.469 0.267\n",
" couch 128 6 0.517 0.5 0.655 0.463\n",
" potted plant 128 14 0.564 0.643 0.673 0.467\n",
" bed 128 3 0.72 1 0.995 0.734\n",
" dining table 128 13 0.433 0.692 0.58 0.48\n",
" toilet 128 2 0.508 0.5 0.745 0.721\n",
" tv 128 2 0.55 0.649 0.828 0.762\n",
" laptop 128 3 1 0 0.741 0.653\n",
" mouse 128 2 1 0 0.0454 0.00907\n",
" remote 128 8 0.83 0.5 0.569 0.449\n",
" cell phone 128 8 0 0 0.0819 0.0266\n",
" microwave 128 3 0.475 0.667 0.83 0.699\n",
" oven 128 5 0.5 0.4 0.348 0.275\n",
" sink 128 6 0.354 0.187 0.368 0.217\n",
" refrigerator 128 5 0.518 0.4 0.729 0.571\n",
" book 128 29 0.583 0.241 0.396 0.204\n",
" clock 128 9 0.891 0.889 0.91 0.773\n",
" vase 128 2 0.506 1 0.828 0.745\n",
" tv 128 2 0.563 0.689 0.828 0.762\n",
" laptop 128 3 1 0.455 0.747 0.657\n",
" mouse 128 2 1 0 0.0405 0.0081\n",
" remote 128 8 0.832 0.5 0.574 0.445\n",
" cell phone 128 8 0 0 0.0724 0.0315\n",
" microwave 128 3 0.496 0.667 0.806 0.685\n",
" oven 128 5 0.501 0.4 0.345 0.273\n",
" sink 128 6 0.379 0.212 0.366 0.21\n",
" refrigerator 128 5 0.612 0.4 0.77 0.608\n",
" book 128 29 0.553 0.207 0.387 0.2\n",
" clock 128 9 0.88 0.889 0.907 0.772\n",
" vase 128 2 0.508 1 0.828 0.745\n",
" scissors 128 1 1 0 0.142 0.0426\n",
" teddy bear 128 21 0.587 0.476 0.63 0.458\n",
" toothbrush 128 5 0.784 0.736 0.898 0.544\n",
"Speed: 2.0ms preprocess, 4.0ms inference, 0.0ms loss, 2.5ms postprocess per image\n",
" teddy bear 128 21 0.662 0.476 0.603 0.442\n",
" toothbrush 128 5 0.792 0.768 0.898 0.574\n",
"Speed: 1.1ms preprocess, 5.4ms inference, 0.0ms loss, 2.9ms postprocess per image\n",
"Results saved to \u001b[1mruns/detect/train\u001b[0m\n"
]
}
@ -479,26 +485,26 @@
"base_uri": "https://localhost:8080/"
},
"id": "CYIjW4igCjqD",
"outputId": "69cab2fb-cbfa-4acf-8e29-9c4fb6f4a38f"
"outputId": "49b5bb9d-2c16-415b-c3e7-ec95c15a9e62"
},
"execution_count": null,
"execution_count": 6,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Ultralytics YOLOv8.0.24 🚀 Python-3.8.10 torch-1.13.1+cu116 CPU\n",
"Ultralytics YOLOv8.0.57 🚀 Python-3.9.16 torch-1.13.1+cu116 CPU\n",
"YOLOv8n summary (fused): 168 layers, 3151904 parameters, 0 gradients, 8.7 GFLOPs\n",
"\n",
"\u001b[34m\u001b[1mPyTorch:\u001b[0m starting from yolov8n.pt with output shape (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 1.13.1+cu116...\n",
"\u001b[34m\u001b[1mTorchScript:\u001b[0m export success ✅ 1.7s, saved as yolov8n.torchscript (12.4 MB)\n",
"\u001b[34m\u001b[1mTorchScript:\u001b[0m export success ✅ 1.9s, saved as yolov8n.torchscript (12.4 MB)\n",
"\n",
"Export complete (2.2s)\n",
"Export complete (2.6s)\n",
"Results saved to \u001b[1m/content\u001b[0m\n",
"Predict: yolo task=detect mode=predict model=yolov8n.torchscript -WARNING ⚠️ not yet supported for YOLOv8 exported models\n",
"Validate: yolo task=detect mode=val model=yolov8n.torchscript -WARNING ⚠️ not yet supported for YOLOv8 exported models\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",
"Visualize: https://netron.app\n"
]
}