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Update Colab notebook for YOLOv8.1 (#8660)

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Glenn Jocher 1 year ago committed by GitHub
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@ -58,21 +58,21 @@
"colab": { "colab": {
"base_uri": "https://localhost:8080/" "base_uri": "https://localhost:8080/"
}, },
"outputId": "27ca383c-0a97-4679-f1c5-ba843f033de7" "outputId": "51d15672-e688-4fb8-d9d0-00d1916d3532"
}, },
"source": [ "source": [
"%pip install ultralytics\n", "%pip install ultralytics\n",
"import ultralytics\n", "import ultralytics\n",
"ultralytics.checks()" "ultralytics.checks()"
], ],
"execution_count": null, "execution_count": 1,
"outputs": [ "outputs": [
{ {
"output_type": "stream", "output_type": "stream",
"name": "stderr", "name": "stdout",
"text": [ "text": [
"Ultralytics YOLOv8.0.145 🚀 Python-3.10.6 torch-2.0.1+cu118 CUDA:0 (Tesla T4, 15102MiB)\n", "Ultralytics YOLOv8.1.23 🚀 Python-3.10.12 torch-2.1.0+cu121 CUDA:0 (Tesla T4, 15102MiB)\n",
"Setup complete ✅ (2 CPUs, 12.7 GB RAM, 24.2/78.2 GB disk)\n" "Setup complete ✅ (2 CPUs, 12.7 GB RAM, 26.3/78.2 GB disk)\n"
] ]
} }
] ]
@ -95,28 +95,29 @@
"colab": { "colab": {
"base_uri": "https://localhost:8080/" "base_uri": "https://localhost:8080/"
}, },
"outputId": "64489d1f-e71a-44b5-92f6-2088781ca096" "outputId": "37738db7-4284-47de-b3ed-b82f2431ed23"
}, },
"source": [ "source": [
"# Run inference on an image with YOLOv8n\n", "# Run inference on an image with YOLOv8n\n",
"!yolo predict model=yolov8n.pt source='https://ultralytics.com/images/zidane.jpg'" "!yolo predict model=yolov8n.pt source='https://ultralytics.com/images/zidane.jpg'"
], ],
"execution_count": null, "execution_count": 2,
"outputs": [ "outputs": [
{ {
"output_type": "stream", "output_type": "stream",
"name": "stdout", "name": "stdout",
"text": [ "text": [
"Downloading https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8n.pt to 'yolov8n.pt'...\n", "Downloading https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8n.pt to 'yolov8n.pt'...\n",
"100% 6.23M/6.23M [00:00<00:00, 77.2MB/s]\n", "100% 6.23M/6.23M [00:00<00:00, 72.6MB/s]\n",
"Ultralytics YOLOv8.0.145 🚀 Python-3.10.6 torch-2.0.1+cu118 CUDA:0 (Tesla T4, 15102MiB)\n", "Ultralytics YOLOv8.1.23 🚀 Python-3.10.12 torch-2.1.0+cu121 CUDA:0 (Tesla T4, 15102MiB)\n",
"YOLOv8n summary (fused): 168 layers, 3151904 parameters, 0 gradients\n", "YOLOv8n summary (fused): 168 layers, 3151904 parameters, 0 gradients, 8.7 GFLOPs\n",
"\n", "\n",
"Downloading https://ultralytics.com/images/zidane.jpg to 'zidane.jpg'...\n", "Downloading https://ultralytics.com/images/zidane.jpg to 'zidane.jpg'...\n",
"100% 165k/165k [00:00<00:00, 7.46MB/s]\n", "100% 165k/165k [00:00<00:00, 7.05MB/s]\n",
"image 1/1 /content/zidane.jpg: 384x640 2 persons, 1 tie, 365.8ms\n", "image 1/1 /content/zidane.jpg: 384x640 2 persons, 1 tie, 162.0ms\n",
"Speed: 13.7ms preprocess, 365.8ms inference, 431.7ms postprocess per image at shape (1, 3, 384, 640)\n", "Speed: 13.9ms preprocess, 162.0ms inference, 1259.5ms postprocess per image at shape (1, 3, 384, 640)\n",
"Results saved to \u001b[1mruns/detect/predict\u001b[0m\n" "Results saved to \u001b[1mruns/detect/predict\u001b[0m\n",
"💡 Learn more at https://docs.ultralytics.com/modes/predict\n"
] ]
} }
] ]
@ -159,7 +160,7 @@
"cell_type": "code", "cell_type": "code",
"metadata": { "metadata": {
"id": "X58w8JLpMnjH", "id": "X58w8JLpMnjH",
"outputId": "e3aacd98-ceca-49b7-e112-a0c25979ad6c", "outputId": "61001937-ccd2-4157-a373-156a57495231",
"colab": { "colab": {
"base_uri": "https://localhost:8080/" "base_uri": "https://localhost:8080/"
} }
@ -168,26 +169,26 @@
"# Validate YOLOv8n on COCO8 val\n", "# Validate YOLOv8n on COCO8 val\n",
"!yolo val model=yolov8n.pt data=coco8.yaml" "!yolo val model=yolov8n.pt data=coco8.yaml"
], ],
"execution_count": null, "execution_count": 3,
"outputs": [ "outputs": [
{ {
"output_type": "stream", "output_type": "stream",
"name": "stdout", "name": "stdout",
"text": [ "text": [
"Ultralytics YOLOv8.0.145 🚀 Python-3.10.6 torch-2.0.1+cu118 CUDA:0 (Tesla T4, 15102MiB)\n", "Ultralytics YOLOv8.1.23 🚀 Python-3.10.12 torch-2.1.0+cu121 CUDA:0 (Tesla T4, 15102MiB)\n",
"YOLOv8n summary (fused): 168 layers, 3151904 parameters, 0 gradients\n", "YOLOv8n summary (fused): 168 layers, 3151904 parameters, 0 gradients, 8.7 GFLOPs\n",
"\n", "\n",
"Dataset 'coco8.yaml' images not found ⚠️, missing path '/content/datasets/coco8/images/val'\n", "Dataset 'coco8.yaml' images not found ⚠️, missing path '/content/datasets/coco8/images/val'\n",
"Downloading https://ultralytics.com/assets/coco8.zip to '/content/datasets/coco8.zip'...\n", "Downloading https://ultralytics.com/assets/coco8.zip to '/content/datasets/coco8.zip'...\n",
"100% 433k/433k [00:00<00:00, 12.4MB/s]\n", "100% 433k/433k [00:00<00:00, 12.5MB/s]\n",
"Unzipping /content/datasets/coco8.zip to /content/datasets...\n", "Unzipping /content/datasets/coco8.zip to /content/datasets/coco8...: 100% 25/25 [00:00<00:00, 4546.38file/s]\n",
"Dataset download success ✅ (0.7s), saved to \u001b[1m/content/datasets\u001b[0m\n", "Dataset download success ✅ (0.9s), saved to \u001b[1m/content/datasets\u001b[0m\n",
"\n", "\n",
"Downloading https://ultralytics.com/assets/Arial.ttf to '/root/.config/Ultralytics/Arial.ttf'...\n", "Downloading https://ultralytics.com/assets/Arial.ttf to '/root/.config/Ultralytics/Arial.ttf'...\n",
"100% 755k/755k [00:00<00:00, 17.5MB/s]\n", "100% 755k/755k [00:00<00:00, 17.8MB/s]\n",
"\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", "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/coco8/labels/val... 4 images, 0 backgrounds, 0 corrupt: 100% 4/4 [00:00<00:00, 275.94it/s]\n",
"\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/datasets/coco8/labels/val.cache\n", "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/datasets/coco8/labels/val.cache\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:03<00:00, 3.84s/it]\n", " Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:02<00:00, 2.23s/it]\n",
" all 4 17 0.621 0.833 0.888 0.63\n", " all 4 17 0.621 0.833 0.888 0.63\n",
" person 4 10 0.721 0.5 0.519 0.269\n", " person 4 10 0.721 0.5 0.519 0.269\n",
" dog 4 1 0.37 1 0.995 0.597\n", " dog 4 1 0.37 1 0.995 0.597\n",
@ -195,8 +196,9 @@
" elephant 4 2 0.505 0.5 0.828 0.394\n", " elephant 4 2 0.505 0.5 0.828 0.394\n",
" umbrella 4 1 0.564 1 0.995 0.995\n", " umbrella 4 1 0.564 1 0.995 0.995\n",
" potted plant 4 1 0.814 1 0.995 0.895\n", " potted plant 4 1 0.814 1 0.995 0.895\n",
"Speed: 0.3ms preprocess, 78.7ms inference, 0.0ms loss, 65.4ms postprocess per image\n", "Speed: 0.3ms preprocess, 56.9ms inference, 0.0ms loss, 222.8ms postprocess per image\n",
"Results saved to \u001b[1mruns/detect/val\u001b[0m\n" "Results saved to \u001b[1mruns/detect/val\u001b[0m\n",
"💡 Learn more at https://docs.ultralytics.com/modes/val\n"
] ]
} }
] ]
@ -237,7 +239,7 @@
"cell_type": "code", "cell_type": "code",
"metadata": { "metadata": {
"id": "1NcFxRcFdJ_O", "id": "1NcFxRcFdJ_O",
"outputId": "b750f2fe-c4d9-4764-b8d5-ed7bd920697b", "outputId": "1ec62d53-41eb-444f-e2f7-cef5c18b9a27",
"colab": { "colab": {
"base_uri": "https://localhost:8080/" "base_uri": "https://localhost:8080/"
} }
@ -246,14 +248,14 @@
"# Train YOLOv8n on COCO8 for 3 epochs\n", "# Train YOLOv8n on COCO8 for 3 epochs\n",
"!yolo train model=yolov8n.pt data=coco8.yaml epochs=3 imgsz=640" "!yolo train model=yolov8n.pt data=coco8.yaml epochs=3 imgsz=640"
], ],
"execution_count": null, "execution_count": 4,
"outputs": [ "outputs": [
{ {
"output_type": "stream", "output_type": "stream",
"name": "stdout", "name": "stdout",
"text": [ "text": [
"Ultralytics YOLOv8.0.145 🚀 Python-3.10.6 torch-2.0.1+cu118 CUDA:0 (Tesla T4, 15102MiB)\n", "Ultralytics YOLOv8.1.23 🚀 Python-3.10.12 torch-2.1.0+cu121 CUDA:0 (Tesla T4, 15102MiB)\n",
"\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", "\u001b[34m\u001b[1mengine/trainer: \u001b[0mtask=detect, mode=train, model=yolov8n.pt, data=coco8.yaml, epochs=3, time=None, patience=100, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs/detect/train\n",
"\n", "\n",
" from n params module arguments \n", " from n params module arguments \n",
" 0 -1 1 464 ultralytics.nn.modules.conv.Conv [3, 16, 3, 2] \n", " 0 -1 1 464 ultralytics.nn.modules.conv.Conv [3, 16, 3, 2] \n",
@ -279,55 +281,59 @@
" 20 [-1, 9] 1 0 ultralytics.nn.modules.conv.Concat [1] \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", " 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", " 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", "Model summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPs\n",
"\n", "\n",
"Transferred 355/355 items from pretrained weights\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[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/detect/train', view at http://localhost:6006/\n",
"Freezing layer 'model.22.dfl.conv.weight'\n",
"\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks with YOLOv8n...\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[1mAMP: \u001b[0mchecks passed ✅\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[0mScanning /content/datasets/coco8/labels/train... 4 images, 0 backgrounds, 0 corrupt: 100% 4/4 [00:00<00:00, 43351.98it/s]\n",
"\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/datasets/coco8/labels/train.cache\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[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/coco8/labels/val.cache... 4 images, 0 backgrounds, 0 corrupt: 100% 4/4 [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", "Plotting labels to runs/detect/train/labels.jpg... \n",
"\u001b[34m\u001b[1moptimizer:\u001b[0m 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... \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", "\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",
"\u001b[34m\u001b[1mTensorBoard: \u001b[0mmodel graph visualization added ✅\n",
"Image sizes 640 train, 640 val\n", "Image sizes 640 train, 640 val\n",
"Using 2 dataloader workers\n", "Using 2 dataloader workers\n",
"Logging results to \u001b[1mruns/detect/train\u001b[0m\n", "Logging results to \u001b[1mruns/detect/train\u001b[0m\n",
"Starting training for 3 epochs...\n", "Starting training for 3 epochs...\n",
"\n", "\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 1/3 0.761G 0.9273 3.155 1.291 32 640: 100% 1/1 [00:01<00:00, 1.23s/it]\n", " 1/3 0.77G 0.9308 3.155 1.291 32 640: 100% 1/1 [00:01<00:00, 1.70s/it]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 2.21it/s]\n", " Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 1.90it/s]\n",
" all 4 17 0.613 0.899 0.888 0.621\n", " all 4 17 0.858 0.54 0.726 0.51\n",
"\n", "\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 2/3 0.78G 1.161 3.126 1.517 33 640: 100% 1/1 [00:00<00:00, 9.06it/s]\n", " 2/3 0.78G 1.162 3.127 1.518 33 640: 100% 1/1 [00:00<00:00, 8.18it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 7.18it/s]\n", " Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 3.71it/s]\n",
" all 4 17 0.601 0.896 0.888 0.613\n", " all 4 17 0.904 0.526 0.742 0.5\n",
"\n", "\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 3/3 0.757G 0.9264 2.508 1.254 17 640: 100% 1/1 [00:00<00:00, 7.32it/s]\n", " 3/3 0.759G 0.925 2.507 1.254 17 640: 100% 1/1 [00:00<00:00, 7.53it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 5.26it/s]\n", " Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 6.80it/s]\n",
" all 4 17 0.598 0.892 0.886 0.613\n", " all 4 17 0.906 0.532 0.741 0.513\n",
"\n", "\n",
"3 epochs completed in 0.003 hours.\n", "3 epochs completed in 0.002 hours.\n",
"Optimizer stripped from runs/detect/train/weights/last.pt, 6.5MB\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", "Optimizer stripped from runs/detect/train/weights/best.pt, 6.5MB\n",
"\n", "\n",
"Validating runs/detect/train/weights/best.pt...\n", "Validating runs/detect/train/weights/best.pt...\n",
"Ultralytics YOLOv8.0.145 🚀 Python-3.10.6 torch-2.0.1+cu118 CUDA:0 (Tesla T4, 15102MiB)\n", "Ultralytics YOLOv8.1.23 🚀 Python-3.10.12 torch-2.1.0+cu121 CUDA:0 (Tesla T4, 15102MiB)\n",
"Model summary (fused): 168 layers, 3151904 parameters, 0 gradients\n", "Model summary (fused): 168 layers, 3151904 parameters, 0 gradients, 8.7 GFLOPs\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 16.58it/s]\n", " Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 16.31it/s]\n",
" all 4 17 0.613 0.898 0.888 0.621\n", " all 4 17 0.906 0.533 0.755 0.515\n",
" person 4 10 0.661 0.5 0.52 0.285\n", " person 4 10 0.942 0.3 0.519 0.233\n",
" dog 4 1 0.337 1 0.995 0.597\n", " dog 4 1 1 0 0.332 0.162\n",
" horse 4 2 0.723 1 0.995 0.631\n", " horse 4 2 1 0.9 0.995 0.698\n",
" elephant 4 2 0.629 0.886 0.828 0.319\n", " elephant 4 2 1 0 0.695 0.206\n",
" umbrella 4 1 0.55 1 0.995 0.995\n", " umbrella 4 1 0.755 1 0.995 0.895\n",
" potted plant 4 1 0.776 1 0.995 0.895\n", " potted plant 4 1 0.739 1 0.995 0.895\n",
"Speed: 0.2ms preprocess, 4.6ms inference, 0.0ms loss, 1.1ms postprocess per image\n", "Speed: 0.3ms preprocess, 6.1ms inference, 0.0ms loss, 2.5ms postprocess per image\n",
"Results saved to \u001b[1mruns/detect/train\u001b[0m\n" "Results saved to \u001b[1mruns/detect/train\u001b[0m\n",
"💡 Learn more at https://docs.ultralytics.com/modes/train\n"
] ]
} }
] ]
@ -339,23 +345,22 @@
"\n", "\n",
"Export a YOLOv8 model to any supported format below with the `format` argument, i.e. `format=onnx`. See [YOLOv8 Export Docs](https://docs.ultralytics.com/modes/export/) for more information.\n", "Export a YOLOv8 model to any supported format below with the `format` argument, i.e. `format=onnx`. See [YOLOv8 Export Docs](https://docs.ultralytics.com/modes/export/) for more information.\n",
"\n", "\n",
"- 💡 ProTip: Export to [ONNX](https://onnx.ai/) or [OpenVINO](https://docs.openvino.ai/latest/index.html) for up to 3x CPU speedup. \n", "- 💡 ProTip: Export to [ONNX](https://docs.ultralytics.com/integrations/onnx/) or [OpenVINO](https://docs.ultralytics.com/integrations/openvino/) for up to 3x CPU speedup. \n",
"- 💡 ProTip: Export to [TensorRT](https://developer.nvidia.com/tensorrt) for up to 5x GPU speedup.\n", "- 💡 ProTip: Export to [TensorRT](https://docs.ultralytics.com/integrations/tensorrt/) for up to 5x GPU speedup.\n",
"\n",
"\n", "\n",
"| Format | `format` Argument | Model | Metadata | Arguments |\n", "| Format | `format` Argument | Model | Metadata | Arguments |\n",
"|--------------------------------------------------------------------|-------------------|---------------------------|----------|-----------------------------------------------------|\n", "|--------------------------------------------------------------------|-------------------|---------------------------|----------|-----------------------------------------------------|\n",
"| [PyTorch](https://pytorch.org/) | - | `yolov8n.pt` | ✅ | - |\n", "| [PyTorch](https://pytorch.org/) | - | `yolov8n.pt` | ✅ | - |\n",
"| [TorchScript](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `yolov8n.torchscript` | ✅ | `imgsz`, `optimize` |\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", "| [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`, `int8` |\n", "| [OpenVINO](https://docs.openvino.ai/) | `openvino` | `yolov8n_openvino_model/` | ✅ | `imgsz`, `half`, `int8` |\n",
"| [TensorRT](https://developer.nvidia.com/tensorrt) | `engine` | `yolov8n.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace` |\n", "| [TensorRT](https://developer.nvidia.com/tensorrt) | `engine` | `yolov8n.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace` |\n",
"| [CoreML](https://github.com/apple/coremltools) | `coreml` | `yolov8n.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms` |\n", "| [CoreML](https://github.com/apple/coremltools) | `coreml` | `yolov8n.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms` |\n",
"| [TF SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n_saved_model/` | ✅ | `imgsz`, `keras`, `int8` |\n", "| [TF SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n_saved_model/` | ✅ | `imgsz`, `keras`, `int8` |\n",
"| [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n.pb` | ❌ | `imgsz` |\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 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 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", "| [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n_web_model/` | ✅ | `imgsz`, `half`, `int8` |\n",
"| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n_paddle_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" "| [NCNN](https://github.com/Tencent/ncnn) | `ncnn` | `yolov8n_ncnn_model/` | ✅ | `imgsz`, `half` |\n"
], ],
@ -373,27 +378,28 @@
"base_uri": "https://localhost:8080/" "base_uri": "https://localhost:8080/"
}, },
"id": "CYIjW4igCjqD", "id": "CYIjW4igCjqD",
"outputId": "2b65e381-717b-4a6f-d6f5-5254c867f3a4" "outputId": "f6d45666-07b4-4214-86c0-4e83e70ac096"
}, },
"execution_count": null, "execution_count": 5,
"outputs": [ "outputs": [
{ {
"output_type": "stream", "output_type": "stream",
"name": "stdout", "name": "stdout",
"text": [ "text": [
"Ultralytics YOLOv8.0.145 🚀 Python-3.10.6 torch-2.0.1+cu118 CPU (Intel Xeon 2.30GHz)\n", "Ultralytics YOLOv8.1.23 🚀 Python-3.10.12 torch-2.1.0+cu121 CPU (Intel Xeon 2.30GHz)\n",
"YOLOv8n summary (fused): 168 layers, 3151904 parameters, 0 gradients\n", "YOLOv8n summary (fused): 168 layers, 3151904 parameters, 0 gradients, 8.7 GFLOPs\n",
"\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", "\n",
"\u001b[34m\u001b[1mTorchScript:\u001b[0m starting export with torch 2.0.1+cu118...\n", "\u001b[34m\u001b[1mTorchScript:\u001b[0m starting export with torch 2.1.0+cu121...\n",
"\u001b[34m\u001b[1mTorchScript:\u001b[0m export success ✅ 2.8s, saved as 'yolov8n.torchscript' (12.4 MB)\n", "\u001b[34m\u001b[1mTorchScript:\u001b[0m export success ✅ 2.4s, saved as 'yolov8n.torchscript' (12.4 MB)\n",
"\n", "\n",
"Export complete (4.6s)\n", "Export complete (4.5s)\n",
"Results saved to \u001b[1m/content\u001b[0m\n", "Results saved to \u001b[1m/content\u001b[0m\n",
"Predict: yolo predict task=detect model=yolov8n.torchscript imgsz=640 \n", "Predict: yolo predict task=detect model=yolov8n.torchscript imgsz=640 \n",
"Validate: yolo val task=detect model=yolov8n.torchscript imgsz=640 data=None \n", "Validate: yolo val task=detect model=yolov8n.torchscript imgsz=640 data=coco.yaml \n",
"Visualize: https://netron.app\n" "Visualize: https://netron.app\n",
"💡 Learn more at https://docs.ultralytics.com/modes/export\n"
] ]
} }
] ]
@ -541,7 +547,7 @@
"# Load YOLOv8n-pose, train it on COCO8-pose for 3 epochs and predict an image with it\n", "# Load YOLOv8n-pose, train it on COCO8-pose for 3 epochs and predict an image with it\n",
"from ultralytics import YOLO\n", "from ultralytics import YOLO\n",
"\n", "\n",
"model = YOLO('yolov8n-pose.pt') # load a pretrained YOLOv8n classification model\n", "model = YOLO('yolov8n-pose.pt') # load a pretrained YOLOv8n pose model\n",
"model.train(data='coco8-pose.yaml', epochs=3) # train the model\n", "model.train(data='coco8-pose.yaml', epochs=3) # train the model\n",
"model('https://ultralytics.com/images/bus.jpg') # predict on an image" "model('https://ultralytics.com/images/bus.jpg') # predict on an image"
], ],
@ -551,6 +557,33 @@
"execution_count": null, "execution_count": null,
"outputs": [] "outputs": []
}, },
{
"cell_type": "markdown",
"source": [
"## 4. Oriented Bounding Boxes (OBB)\n",
"\n",
"YOLOv8 _OBB_ models use the `-obb` suffix, i.e. `yolov8n-obb.pt` and are pretrained on the DOTA dataset. See [OBB Docs](https://docs.ultralytics.com/tasks/obb/) for full details."
],
"metadata": {
"id": "cf5j_T9-B5F0"
}
},
{
"cell_type": "code",
"source": [
"# Load YOLOv8n-obb, train it on DOTA8 for 3 epochs and predict an image with it\n",
"from ultralytics import YOLO\n",
"\n",
"model = YOLO('yolov8n-obb.pt') # load a pretrained YOLOv8n OBB model\n",
"model.train(data='coco8-dota.yaml', epochs=3) # train the model\n",
"model('https://ultralytics.com/images/bus.jpg') # predict on an image"
],
"metadata": {
"id": "IJNKClOOB5YS"
},
"execution_count": null,
"outputs": []
},
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {

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