diff --git a/examples/tutorial.ipynb b/examples/tutorial.ipynb index 3b74d6d4..51e5c8f2 100644 --- a/examples/tutorial.ipynb +++ b/examples/tutorial.ipynb @@ -58,21 +58,21 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "27ca383c-0a97-4679-f1c5-ba843f033de7" + "outputId": "51d15672-e688-4fb8-d9d0-00d1916d3532" }, "source": [ "%pip install ultralytics\n", "import ultralytics\n", "ultralytics.checks()" ], - "execution_count": null, + "execution_count": 1, "outputs": [ { "output_type": "stream", - "name": "stderr", + "name": "stdout", "text": [ - "Ultralytics YOLOv8.0.145 🚀 Python-3.10.6 torch-2.0.1+cu118 CUDA:0 (Tesla T4, 15102MiB)\n", - "Setup complete ✅ (2 CPUs, 12.7 GB RAM, 24.2/78.2 GB disk)\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, 26.3/78.2 GB disk)\n" ] } ] @@ -95,28 +95,29 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "64489d1f-e71a-44b5-92f6-2088781ca096" + "outputId": "37738db7-4284-47de-b3ed-b82f2431ed23" }, "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": 2, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "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", - "Ultralytics YOLOv8.0.145 🚀 Python-3.10.6 torch-2.0.1+cu118 CUDA:0 (Tesla T4, 15102MiB)\n", - "YOLOv8n summary (fused): 168 layers, 3151904 parameters, 0 gradients\n", + "100% 6.23M/6.23M [00:00<00:00, 72.6MB/s]\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, 8.7 GFLOPs\n", "\n", "Downloading https://ultralytics.com/images/zidane.jpg to 'zidane.jpg'...\n", - "100% 165k/165k [00:00<00:00, 7.46MB/s]\n", - "image 1/1 /content/zidane.jpg: 384x640 2 persons, 1 tie, 365.8ms\n", - "Speed: 13.7ms preprocess, 365.8ms inference, 431.7ms postprocess per image at shape (1, 3, 384, 640)\n", - "Results saved to \u001b[1mruns/detect/predict\u001b[0m\n" + "100% 165k/165k [00:00<00:00, 7.05MB/s]\n", + "image 1/1 /content/zidane.jpg: 384x640 2 persons, 1 tie, 162.0ms\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", + "💡 Learn more at https://docs.ultralytics.com/modes/predict\n" ] } ] @@ -159,7 +160,7 @@ "cell_type": "code", "metadata": { "id": "X58w8JLpMnjH", - "outputId": "e3aacd98-ceca-49b7-e112-a0c25979ad6c", + "outputId": "61001937-ccd2-4157-a373-156a57495231", "colab": { "base_uri": "https://localhost:8080/" } @@ -168,26 +169,26 @@ "# Validate YOLOv8n on COCO8 val\n", "!yolo val model=yolov8n.pt data=coco8.yaml" ], - "execution_count": null, + "execution_count": 3, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "Ultralytics YOLOv8.0.145 🚀 Python-3.10.6 torch-2.0.1+cu118 CUDA:0 (Tesla T4, 15102MiB)\n", - "YOLOv8n summary (fused): 168 layers, 3151904 parameters, 0 gradients\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, 8.7 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, 12.4MB/s]\n", - "Unzipping /content/datasets/coco8.zip to /content/datasets...\n", - "Dataset download success ✅ (0.7s), saved to \u001b[1m/content/datasets\u001b[0m\n", + "100% 433k/433k [00:00<00:00, 12.5MB/s]\n", + "Unzipping /content/datasets/coco8.zip to /content/datasets/coco8...: 100% 25/25 [00:00<00:00, 4546.38file/s]\n", + "Dataset download success ✅ (0.9s), 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, 17.5MB/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", + "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, 275.94it/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 [00:03<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", " person 4 10 0.721 0.5 0.519 0.269\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", " 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, 78.7ms inference, 0.0ms loss, 65.4ms postprocess per image\n", - "Results saved to \u001b[1mruns/detect/val\u001b[0m\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", + "💡 Learn more at https://docs.ultralytics.com/modes/val\n" ] } ] @@ -237,7 +239,7 @@ "cell_type": "code", "metadata": { "id": "1NcFxRcFdJ_O", - "outputId": "b750f2fe-c4d9-4764-b8d5-ed7bd920697b", + "outputId": "1ec62d53-41eb-444f-e2f7-cef5c18b9a27", "colab": { "base_uri": "https://localhost:8080/" } @@ -246,14 +248,14 @@ "# Train YOLOv8n on COCO8 for 3 epochs\n", "!yolo train model=yolov8n.pt data=coco8.yaml epochs=3 imgsz=640" ], - "execution_count": null, + "execution_count": 4, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "Ultralytics YOLOv8.0.145 🚀 Python-3.10.6 torch-2.0.1+cu118 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", + "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, 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", " from n params module arguments \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", " 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", + "Model summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPs\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", + "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[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[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