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Colab notebook update (#16609)

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Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
pull/16598/head
Glenn Jocher 2 months ago committed by GitHub
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@ -65,21 +65,21 @@
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "96335d4c-20a9-4864-f7a4-bb2eb0077a9d"
"outputId": "2e992f9f-90bb-4668-de12-fed629975285"
},
"source": [
"%pip install ultralytics\n",
"import ultralytics\n",
"ultralytics.checks()"
],
"execution_count": null,
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Ultralytics 8.3.0 🚀 Python-3.10.12 torch-2.2.1+cu121 CUDA:0 (T4, 15102MiB)\n",
"Setup complete ✅ (2 CPUs, 12.7 GB RAM, 28.8/78.2 GB disk)\n"
"Ultralytics 8.3.2 🚀 Python-3.10.12 torch-2.4.1+cu121 CUDA:0 (Tesla T4, 15102MiB)\n",
"Setup complete ✅ (2 CPUs, 12.7 GB RAM, 41.1/112.6 GB disk)\n"
]
}
]
@ -102,27 +102,27 @@
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "84f32db2-80b0-4f35-9a2a-a56d11f7863f"
"outputId": "e3ebec6f-658a-4803-d80c-e07d12908767"
},
"source": [
"# Run inference on an image with YOLOv8n\n",
"# Run inference on an image with YOLO11n\n",
"!yolo predict model=yolo11n.pt source='https://ultralytics.com/images/zidane.jpg'"
],
"execution_count": null,
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Downloading https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8n.pt to 'yolov8n.pt'...\n",
"100% 6.23M/6.23M [00:00<00:00, 83.2MB/s]\n",
"Ultralytics 8.3.0 🚀 Python-3.10.12 torch-2.2.1+cu121 CUDA:0 (T4, 15102MiB)\n",
"YOLOv8n summary (fused): 168 layers, 3151904 parameters, 0 gradients, 8.7 GFLOPs\n",
"Downloading https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt to 'yolo11n.pt'...\n",
"100% 5.35M/5.35M [00:00<00:00, 72.7MB/s]\n",
"Ultralytics 8.3.2 🚀 Python-3.10.12 torch-2.4.1+cu121 CUDA:0 (Tesla T4, 15102MiB)\n",
"YOLO11n summary (fused): 238 layers, 2,616,248 parameters, 0 gradients, 6.5 GFLOPs\n",
"\n",
"Downloading https://ultralytics.com/images/zidane.jpg to 'zidane.jpg'...\n",
"100% 165k/165k [00:00<00:00, 11.1MB/s]\n",
"image 1/1 /content/zidane.jpg: 384x640 2 persons, 1 tie, 21.4ms\n",
"Speed: 1.9ms preprocess, 21.4ms inference, 6.2ms postprocess per image at shape (1, 3, 384, 640)\n",
"100% 49.2k/49.2k [00:00<00:00, 5.37MB/s]\n",
"image 1/1 /content/zidane.jpg: 384x640 2 persons, 1 tie, 63.4ms\n",
"Speed: 14.5ms preprocess, 63.4ms inference, 820.9ms postprocess per image at shape (1, 3, 384, 640)\n",
"Results saved to \u001b[1mruns/detect/predict\u001b[0m\n",
"💡 Learn more at https://docs.ultralytics.com/modes/predict\n"
]
@ -167,43 +167,43 @@
"cell_type": "code",
"metadata": {
"id": "X58w8JLpMnjH",
"outputId": "bed10d45-ceb6-4b6f-86b7-9428208b142a",
"outputId": "af2a5deb-029b-466d-96a4-bd3e406987fa",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"# Validate YOLOv8n on COCO8 val\n",
"# Validate YOLO11n on COCO8 val\n",
"!yolo val model=yolo11n.pt data=coco8.yaml"
],
"execution_count": null,
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Ultralytics 8.3.0 🚀 Python-3.10.12 torch-2.2.1+cu121 CUDA:0 (T4, 15102MiB)\n",
"YOLOv8n summary (fused): 168 layers, 3151904 parameters, 0 gradients, 8.7 GFLOPs\n",
"Ultralytics 8.3.2 🚀 Python-3.10.12 torch-2.4.1+cu121 CUDA:0 (Tesla T4, 15102MiB)\n",
"YOLO11n summary (fused): 238 layers, 2,616,248 parameters, 0 gradients, 6.5 GFLOPs\n",
"\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",
"100% 433k/433k [00:00<00:00, 14.2MB/s]\n",
"Unzipping /content/datasets/coco8.zip to /content/datasets/coco8...: 100% 25/25 [00:00<00:00, 1093.93file/s]\n",
"Dataset download success ✅ (1.3s), saved to \u001b[1m/content/datasets\u001b[0m\n",
"100% 433k/433k [00:00<00:00, 15.8MB/s]\n",
"Unzipping /content/datasets/coco8.zip to /content/datasets/coco8...: 100% 25/25 [00:00<00:00, 1188.35file/s]\n",
"Dataset download success ✅ (1.4s), 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, 17.4MB/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, 157.00it/s]\n",
"100% 755k/755k [00:00<00:00, 17.7MB/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, 142.04it/s]\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 [00:06<00:00, 6.89s/it]\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",
" dog 4 1 0.37 1 0.995 0.597\n",
" horse 4 2 0.751 1 0.995 0.631\n",
" elephant 4 2 0.505 0.5 0.828 0.394\n",
" umbrella 4 1 0.564 1 0.995 0.995\n",
" potted plant 4 1 0.814 1 0.995 0.895\n",
"Speed: 0.3ms preprocess, 4.9ms inference, 0.0ms loss, 1.3ms postprocess per image\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:04<00:00, 4.75s/it]\n",
" all 4 17 0.57 0.85 0.847 0.632\n",
" person 3 10 0.557 0.6 0.585 0.272\n",
" dog 1 1 0.548 1 0.995 0.697\n",
" horse 1 2 0.531 1 0.995 0.674\n",
" elephant 1 2 0.371 0.5 0.516 0.256\n",
" umbrella 1 1 0.569 1 0.995 0.995\n",
" potted plant 1 1 0.847 1 0.995 0.895\n",
"Speed: 1.0ms preprocess, 73.8ms inference, 0.0ms loss, 561.4ms postprocess per image\n",
"Results saved to \u001b[1mruns/detect/val\u001b[0m\n",
"💡 Learn more at https://docs.ultralytics.com/modes/val\n"
]
@ -246,64 +246,62 @@
"cell_type": "code",
"metadata": {
"id": "1NcFxRcFdJ_O",
"outputId": "9f60c6cb-fa9c-4785-cb7a-71d40abeaf38",
"outputId": "952f35f7-666f-4121-fbdf-2b3a33b28081",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"# Train YOLOv8n on COCO8 for 3 epochs\n",
"# Train YOLO11n on COCO8 for 3 epochs\n",
"!yolo train model=yolo11n.pt data=coco8.yaml epochs=3 imgsz=640"
],
"execution_count": null,
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Ultralytics 8.3.0 🚀 Python-3.10.12 torch-2.2.1+cu121 CUDA:0 (T4, 15102MiB)\n",
"\u001b[34m\u001b[1mengine/trainer: \u001b[0mtask=detect, mode=train, model=yolo11n.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, bgr=0.0, 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",
"Ultralytics 8.3.2 🚀 Python-3.10.12 torch-2.4.1+cu121 CUDA:0 (Tesla T4, 15102MiB)\n",
"\u001b[34m\u001b[1mengine/trainer: \u001b[0mtask=detect, mode=train, model=yolo11n.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=train3, 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=True, 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, bgr=0.0, mosaic=1.0, mixup=0.0, copy_paste=0.0, copy_paste_mode=flip, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs/detect/train3\n",
"\n",
" from n params module arguments \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",
" 2 -1 1 6640 ultralytics.nn.modules.block.C3k2 [32, 64, 1, False, 0.25] \n",
" 3 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2] \n",
" 4 -1 1 26080 ultralytics.nn.modules.block.C3k2 [64, 128, 1, False, 0.25] \n",
" 5 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2] \n",
" 6 -1 1 87040 ultralytics.nn.modules.block.C3k2 [128, 128, 1, 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",
" 8 -1 1 346112 ultralytics.nn.modules.block.C3k2 [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.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.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, 8.9 GFLOPs\n",
" 10 -1 1 249728 ultralytics.nn.modules.block.C2PSA [256, 256, 1] \n",
" 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n",
" 12 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1] \n",
" 13 -1 1 111296 ultralytics.nn.modules.block.C3k2 [384, 128, 1, False] \n",
" 14 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n",
" 15 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1] \n",
" 16 -1 1 32096 ultralytics.nn.modules.block.C3k2 [256, 64, 1, False] \n",
" 17 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2] \n",
" 18 [-1, 13] 1 0 ultralytics.nn.modules.conv.Concat [1] \n",
" 19 -1 1 86720 ultralytics.nn.modules.block.C3k2 [192, 128, 1, False] \n",
" 20 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2] \n",
" 21 [-1, 10] 1 0 ultralytics.nn.modules.conv.Concat [1] \n",
" 22 -1 1 378880 ultralytics.nn.modules.block.C3k2 [384, 256, 1, True] \n",
" 23 [16, 19, 22] 1 464912 ultralytics.nn.modules.head.Detect [80, [64, 128, 256]] \n",
"YOLO11n summary: 319 layers, 2,624,080 parameters, 2,624,064 gradients, 6.6 GFLOPs\n",
"\n",
"Transferred 355/355 items from pretrained weights\n",
"Transferred 499/499 items from pretrained weights\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",
"Freezing layer 'model.23.dfl.conv.weight'\n",
"\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks with YOLO11n...\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, 837.19it/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",
"/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
" self.pid = os.fork()\n",
"\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/datasets/coco8/labels/train.cache... 4 images, 0 backgrounds, 0 corrupt: 100% 4/4 [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, num_output_channels=3, method='weighted_average'), 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",
"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 81 weight(decay=0.0), 88 weight(decay=0.0005), 87 bias(decay=0.0)\n",
"\u001b[34m\u001b[1mTensorBoard: \u001b[0mmodel graph visualization added ✅\n",
"Image sizes 640 train, 640 val\n",
"Using 2 dataloader workers\n",
@ -311,36 +309,36 @@
"Starting training for 3 epochs...\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 1/3 0.81G 1.039 3.146 1.498 25 640: 100% 1/1 [00:01<00:00, 1.51s/it]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 2.32it/s]\n",
" all 4 17 0.62 0.885 0.888 0.621\n",
" 1/3 0.719G 1.004 3.249 1.367 30 640: 100% 1/1 [00:00<00:00, 1.16it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 5.07it/s]\n",
" all 4 17 0.58 0.85 0.849 0.631\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 2/3 0.772G 1.169 2.779 1.442 36 640: 100% 1/1 [00:00<00:00, 8.14it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 3.22it/s]\n",
" all 4 17 0.595 0.903 0.888 0.616\n",
" 2/3 0.715G 1.31 4.043 1.603 35 640: 100% 1/1 [00:00<00:00, 6.88it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 9.08it/s]\n",
" all 4 17 0.581 0.85 0.851 0.63\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 3/3 0.776G 0.6701 3.697 1.096 17 640: 100% 1/1 [00:00<00:00, 6.45it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 5.66it/s]\n",
" all 4 17 0.577 0.833 0.874 0.614\n",
" 3/3 0.692G 1.134 3.174 1.599 18 640: 100% 1/1 [00:00<00:00, 6.75it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 11.60it/s]\n",
" all 4 17 0.582 0.85 0.855 0.632\n",
"\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/best.pt, 6.5MB\n",
"3 epochs completed in 0.003 hours.\n",
"Optimizer stripped from runs/detect/train/weights/last.pt, 5.5MB\n",
"Optimizer stripped from runs/detect/train/weights/best.pt, 5.5MB\n",
"\n",
"Validating runs/detect/train/weights/best.pt...\n",
"Ultralytics 8.3.0 🚀 Python-3.10.12 torch-2.2.1+cu121 CUDA:0 (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% 1/1 [00:00<00:00, 18.23it/s]\n",
" all 4 17 0.617 0.884 0.888 0.622\n",
" person 4 10 0.67 0.5 0.52 0.278\n",
" dog 4 1 0.361 1 0.995 0.597\n",
" horse 4 2 0.728 1 0.995 0.631\n",
" elephant 4 2 0.602 0.805 0.828 0.332\n",
" umbrella 4 1 0.553 1 0.995 0.995\n",
" potted plant 4 1 0.789 1 0.995 0.895\n",
"Speed: 0.3ms preprocess, 4.1ms inference, 0.0ms loss, 1.2ms postprocess per image\n",
"Ultralytics 8.3.2 🚀 Python-3.10.12 torch-2.4.1+cu121 CUDA:0 (Tesla T4, 15102MiB)\n",
"YOLO11n summary (fused): 238 layers, 2,616,248 parameters, 0 gradients, 6.5 GFLOPs\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 23.42it/s]\n",
" all 4 17 0.579 0.85 0.855 0.615\n",
" person 3 10 0.579 0.6 0.623 0.268\n",
" dog 1 1 0.549 1 0.995 0.697\n",
" horse 1 2 0.553 1 0.995 0.675\n",
" elephant 1 2 0.364 0.5 0.528 0.261\n",
" umbrella 1 1 0.571 1 0.995 0.895\n",
" potted plant 1 1 0.857 1 0.995 0.895\n",
"Speed: 0.2ms preprocess, 4.3ms inference, 0.0ms loss, 1.2ms postprocess per image\n",
"Results saved to \u001b[1mruns/detect/train\u001b[0m\n",
"💡 Learn more at https://docs.ultralytics.com/modes/train\n"
]
@ -387,23 +385,23 @@
"base_uri": "https://localhost:8080/"
},
"id": "CYIjW4igCjqD",
"outputId": "947e65cc-79c8-4713-bfd4-3139903ac05a"
"outputId": "5357fa04-6749-4508-effe-8d4078533539"
},
"execution_count": null,
"execution_count": 5,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Ultralytics 8.3.0 🚀 Python-3.10.12 torch-2.2.1+cu121 CPU (Intel Xeon 2.00GHz)\n",
"YOLOv8n summary (fused): 168 layers, 3151904 parameters, 0 gradients, 8.7 GFLOPs\n",
"Ultralytics 8.3.2 🚀 Python-3.10.12 torch-2.4.1+cu121 CPU (Intel Xeon 2.20GHz)\n",
"YOLO11n summary (fused): 238 layers, 2,616,248 parameters, 0 gradients, 6.5 GFLOPs\n",
"\n",
"\u001b[34m\u001b[1mPyTorch:\u001b[0m starting from 'yolo11n.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 'yolo11n.pt' with input shape (1, 3, 640, 640) BCHW and output shape(s) (1, 84, 8400) (5.4 MB)\n",
"\n",
"\u001b[34m\u001b[1mTorchScript:\u001b[0m starting export with torch 2.2.1+cu121...\n",
"\u001b[34m\u001b[1mTorchScript:\u001b[0m export success ✅ 2.0s, saved as 'yolo11n.torchscript' (12.4 MB)\n",
"\u001b[34m\u001b[1mTorchScript:\u001b[0m starting export with torch 2.4.1+cu121...\n",
"\u001b[34m\u001b[1mTorchScript:\u001b[0m export success ✅ 2.4s, saved as 'yolo11n.torchscript' (10.5 MB)\n",
"\n",
"Export complete (4.0s)\n",
"Export complete (4.2s)\n",
"Results saved to \u001b[1m/content\u001b[0m\n",
"Predict: yolo predict task=detect model=yolo11n.torchscript imgsz=640 \n",
"Validate: yolo val task=detect model=yolo11n.torchscript imgsz=640 data=coco.yaml \n",
@ -472,10 +470,10 @@
{
"cell_type": "code",
"source": [
"# Load YOLOv8n, train it on COCO128 for 3 epochs and predict an image with it\n",
"# Load YOLO11n, train it on COCO128 for 3 epochs and predict an image with it\n",
"from ultralytics import YOLO\n",
"\n",
"model = YOLO('yolo11n.pt') # load a pretrained YOLOv8n detection model\n",
"model = YOLO('yolo11n.pt') # load a pretrained YOLO detection model\n",
"model.train(data='coco8.yaml', epochs=3) # train the model\n",
"model('https://ultralytics.com/images/bus.jpg') # predict on an image"
],
@ -499,10 +497,10 @@
{
"cell_type": "code",
"source": [
"# Load YOLOv8n-seg, train it on COCO128-seg for 3 epochs and predict an image with it\n",
"# Load YOLO11n-seg, train it on COCO128-seg for 3 epochs and predict an image with it\n",
"from ultralytics import YOLO\n",
"\n",
"model = YOLO('yolo11n-seg.pt') # load a pretrained YOLOv8n segmentation model\n",
"model = YOLO('yolo11n-seg.pt') # load a pretrained YOLO segmentation model\n",
"model.train(data='coco8-seg.yaml', epochs=3) # train the model\n",
"model('https://ultralytics.com/images/bus.jpg') # predict on an image"
],
@ -526,10 +524,10 @@
{
"cell_type": "code",
"source": [
"# Load YOLOv8n-cls, train it on mnist160 for 3 epochs and predict an image with it\n",
"# Load YOLO11n-cls, train it on mnist160 for 3 epochs and predict an image with it\n",
"from ultralytics import YOLO\n",
"\n",
"model = YOLO('yolo11n-cls.pt') # load a pretrained YOLOv8n classification model\n",
"model = YOLO('yolo11n-cls.pt') # load a pretrained YOLO classification model\n",
"model.train(data='mnist160', epochs=3) # train the model\n",
"model('https://ultralytics.com/images/bus.jpg') # predict on an image"
],
@ -553,10 +551,10 @@
{
"cell_type": "code",
"source": [
"# Load YOLOv8n-pose, train it on COCO8-pose for 3 epochs and predict an image with it\n",
"# Load YOLO11n-pose, train it on COCO8-pose for 3 epochs and predict an image with it\n",
"from ultralytics import YOLO\n",
"\n",
"model = YOLO('yolo11n-pose.pt') # load a pretrained YOLOv8n pose model\n",
"model = YOLO('yolo11n-pose.pt') # load a pretrained YOLO pose 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"
],
@ -580,10 +578,10 @@
{
"cell_type": "code",
"source": [
"# Load YOLOv8n-obb, train it on DOTA8 for 3 epochs and predict an image with it\n",
"# Load YOLO11n-obb, train it on DOTA8 for 3 epochs and predict an image with it\n",
"from ultralytics import YOLO\n",
"\n",
"model = YOLO('yolo11n-obb.pt') # load a pretrained YOLOv8n OBB model\n",
"model = YOLO('yolo11n-obb.pt') # load a pretrained YOLO 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"
],

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