diff --git a/examples/tutorial.ipynb b/examples/tutorial.ipynb
index c705f394de..0992abf460 100644
--- a/examples/tutorial.ipynb
+++ b/examples/tutorial.ipynb
@@ -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"
       ],