diff --git a/examples/tutorial.ipynb b/examples/tutorial.ipynb
index d5459f5..b3e31c5 100644
--- a/examples/tutorial.ipynb
+++ b/examples/tutorial.ipynb
@@ -60,7 +60,7 @@
           "base_uri": "https://localhost:8080/",
           "height": 1000
         },
-        "outputId": "19bbf989-d9fa-419d-8948-aaba39db8ddb"
+        "outputId": "9012b4cd-53eb-4c84-f5b7-4976d4b4e58a"
       },
       "source": [
         "# Pip install method (recommended)\n",
@@ -74,7 +74,7 @@
           "output_type": "stream",
           "name": "stderr",
           "text": [
-            "Ultralytics YOLOv8.0.4 🚀 Python-3.8.16 torch-1.13.1+cu116 CUDA:0 (Tesla T4, 15110MiB)\n",
+            "Ultralytics YOLOv8.0.5 🚀 Python-3.8.16 torch-1.13.1+cu116 CUDA:0 (Tesla T4, 15110MiB)\n",
             "Setup complete ✅ (2 CPUs, 12.7 GB RAM, 23.0/166.8 GB disk)\n"
           ]
         }
@@ -111,28 +111,28 @@
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "outputId": "bc3ee5db-5c36-4dcc-d016-d6b93c756eb2"
+        "outputId": "3136de6b-2995-4731-e84c-962acb233d89"
       },
       "source": [
         "# Run inference on an image with YOLOv8n\n",
         "!yolo task=detect mode=predict model=yolov8n.pt conf=0.25 source='https://ultralytics.com/images/zidane.jpg'"
       ],
-      "execution_count": null,
+      "execution_count": 2,
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
             "Downloading https://ultralytics.com/images/zidane.jpg to zidane.jpg...\n",
-            "100% 165k/165k [00:00<00:00, 8.97MB/s]\n",
-            "Ultralytics YOLOv8.0.1 🚀 Python-3.8.16 torch-1.13.0+cu116 CUDA:0 (Tesla T4, 15110MiB)\n",
+            "100% 165k/165k [00:00<00:00, 12.0MB/s]\n",
+            "Ultralytics YOLOv8.0.5 🚀 Python-3.8.16 torch-1.13.1+cu116 CUDA:0 (Tesla T4, 15110MiB)\n",
             "Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt to yolov8n.pt...\n",
-            "100% 6.24M/6.24M [00:01<00:00, 6.32MB/s]\n",
+            "100% 6.24M/6.24M [00:00<00:00, 58.7MB/s]\n",
             "\n",
             "Fusing layers... \n",
             "YOLOv8n summary: 168 layers, 3151904 parameters, 0 gradients, 8.7 GFLOPs\n",
             "image 1/1 /content/zidane.jpg: 384x640 2 persons, 1 tie, 13.6ms\n",
-            "Speed: 0.4ms pre-process, 13.6ms inference, 51.9ms postprocess per image at shape (1, 3, 640, 640)\n",
+            "Speed: 0.4ms pre-process, 13.6ms inference, 52.1ms postprocess per image at shape (1, 3, 640, 640)\n",
             "Results saved to \u001b[1mruns/detect/predict\u001b[0m\n"
           ]
         }
@@ -175,111 +175,14 @@
     {
       "cell_type": "code",
       "metadata": {
-        "id": "X58w8JLpMnjH",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "ec81409c-7f16-44ec-ac70-8c09021e25a1"
+        "id": "X58w8JLpMnjH"
       },
       "source": [
         "# Validate YOLOv8n on COCO128 val\n",
         "!yolo task=detect mode=val model=yolov8n.pt data=coco128.yaml"
       ],
       "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Ultralytics YOLOv8.0.1 🚀 Python-3.8.16 torch-1.13.0+cu116 CUDA:0 (Tesla T4, 15110MiB)\n",
-            "Fusing layers... \n",
-            "YOLOv8n summary: 168 layers, 3151904 parameters, 0 gradients, 8.7 GFLOPs\n",
-            "\n",
-            "Dataset not found ⚠️, missing paths ['/datasets/coco128/images/train2017']\n",
-            "Downloading https://ultralytics.com/assets/coco128.zip to coco128.zip...\n",
-            "100% 6.66M/6.66M [00:01<00:00, 6.22MB/s]\n",
-            "Dataset download success ✅ (1.9s), saved to \u001b[1m/datasets\u001b[0m\n",
-            "Downloading https://ultralytics.com/assets/Arial.ttf to /root/.config/Ultralytics/Arial.ttf...\n",
-            "100% 755k/755k [00:00<00:00, 27.8MB/s]\n",
-            "\u001b[34m\u001b[1mval: \u001b[0mScanning /datasets/coco128/labels/train2017... 126 images, 2 backgrounds, 0 corrupt: 100% 128/128 [00:00<00:00, 1327.78it/s]\n",
-            "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /datasets/coco128/labels/train2017.cache\n",
-            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 8/8 [00:04<00:00,  1.74it/s]\n",
-            "                   all        128        929       0.64      0.537      0.605      0.446\n",
-            "                person        128        254      0.797      0.677      0.764      0.538\n",
-            "               bicycle        128          6      0.514      0.333      0.315      0.264\n",
-            "                   car        128         46      0.813      0.217      0.273      0.168\n",
-            "            motorcycle        128          5      0.687      0.887      0.898      0.685\n",
-            "              airplane        128          6       0.82      0.833      0.927      0.675\n",
-            "                   bus        128          7      0.491      0.714      0.728      0.671\n",
-            "                 train        128          3      0.534      0.667      0.706      0.604\n",
-            "                 truck        128         12          1      0.332      0.473      0.297\n",
-            "                  boat        128          6      0.226      0.167      0.316      0.134\n",
-            "         traffic light        128         14      0.734        0.2      0.202      0.139\n",
-            "             stop sign        128          2          1      0.992      0.995      0.701\n",
-            "                 bench        128          9      0.839      0.582       0.62      0.365\n",
-            "                  bird        128         16      0.921      0.728      0.864       0.51\n",
-            "                   cat        128          4      0.875          1      0.995      0.791\n",
-            "                   dog        128          9      0.603      0.889      0.785      0.585\n",
-            "                 horse        128          2      0.597          1      0.995      0.518\n",
-            "              elephant        128         17      0.849      0.765        0.9      0.679\n",
-            "                  bear        128          1      0.593          1      0.995      0.995\n",
-            "                 zebra        128          4      0.848          1      0.995      0.965\n",
-            "               giraffe        128          9       0.72          1      0.951      0.722\n",
-            "              backpack        128          6      0.589      0.333      0.376      0.232\n",
-            "              umbrella        128         18      0.804        0.5      0.643      0.414\n",
-            "               handbag        128         19      0.424     0.0526      0.165     0.0889\n",
-            "                   tie        128          7      0.804      0.714      0.674      0.476\n",
-            "              suitcase        128          4      0.635      0.883      0.745      0.534\n",
-            "               frisbee        128          5      0.675        0.8      0.759      0.688\n",
-            "                  skis        128          1      0.567          1      0.995      0.497\n",
-            "             snowboard        128          7      0.742      0.714      0.747        0.5\n",
-            "           sports ball        128          6      0.716      0.433      0.485      0.278\n",
-            "                  kite        128         10      0.817       0.45      0.569      0.184\n",
-            "          baseball bat        128          4      0.551       0.25      0.353      0.175\n",
-            "        baseball glove        128          7      0.624      0.429      0.429      0.293\n",
-            "            skateboard        128          5      0.846        0.6        0.6       0.41\n",
-            "         tennis racket        128          7      0.726      0.387      0.487       0.33\n",
-            "                bottle        128         18      0.448      0.389      0.376      0.208\n",
-            "            wine glass        128         16      0.743      0.362      0.584      0.333\n",
-            "                   cup        128         36       0.58      0.278      0.404       0.29\n",
-            "                  fork        128          6      0.527      0.167      0.246      0.184\n",
-            "                 knife        128         16      0.564        0.5       0.59       0.36\n",
-            "                 spoon        128         22      0.597      0.182      0.328       0.19\n",
-            "                  bowl        128         28      0.648      0.643      0.618      0.491\n",
-            "                banana        128          1          0          0      0.124     0.0379\n",
-            "              sandwich        128          2      0.249        0.5      0.308      0.308\n",
-            "                orange        128          4          1       0.31      0.995      0.623\n",
-            "              broccoli        128         11      0.374      0.182      0.249      0.203\n",
-            "                carrot        128         24      0.648      0.458      0.572      0.362\n",
-            "               hot dog        128          2      0.351      0.553      0.745      0.721\n",
-            "                 pizza        128          5      0.644          1      0.995      0.843\n",
-            "                 donut        128         14      0.657          1       0.94      0.864\n",
-            "                  cake        128          4      0.618          1      0.945      0.845\n",
-            "                 chair        128         35      0.506      0.514      0.442      0.239\n",
-            "                 couch        128          6      0.463        0.5      0.706      0.555\n",
-            "          potted plant        128         14       0.65      0.643      0.711      0.472\n",
-            "                   bed        128          3      0.698      0.667      0.789      0.625\n",
-            "          dining table        128         13      0.432      0.615      0.485      0.366\n",
-            "                toilet        128          2      0.615        0.5      0.695      0.676\n",
-            "                    tv        128          2      0.373       0.62      0.745      0.696\n",
-            "                laptop        128          3          1          0      0.451      0.361\n",
-            "                 mouse        128          2          1          0     0.0625    0.00625\n",
-            "                remote        128          8      0.843        0.5      0.605      0.529\n",
-            "            cell phone        128          8          0          0     0.0549     0.0393\n",
-            "             microwave        128          3      0.435      0.667      0.806      0.718\n",
-            "                  oven        128          5      0.412        0.4      0.339       0.27\n",
-            "                  sink        128          6       0.35      0.167      0.182      0.129\n",
-            "          refrigerator        128          5      0.589        0.4      0.604      0.452\n",
-            "                  book        128         29      0.629      0.103      0.346      0.178\n",
-            "                 clock        128          9      0.788       0.83      0.875       0.74\n",
-            "                  vase        128          2      0.376          1      0.828      0.795\n",
-            "              scissors        128          1          1          0      0.249     0.0746\n",
-            "            teddy bear        128         21      0.877      0.333      0.591      0.394\n",
-            "            toothbrush        128          5      0.743        0.6      0.638      0.374\n",
-            "Speed: 0.9ms pre-process, 5.5ms inference, 0.0ms loss, 2.4ms post-process per image\n"
-          ]
-        }
-      ]
+      "outputs": []
     },
     {
       "cell_type": "markdown",
@@ -291,169 +194,20 @@
         "\n",
         "<p align=\"\"><a href=\"https://roboflow.com/?ref=ultralytics\"><img width=\"1000\" src=\"https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png\"/></a></p>\n",
         "\n",
-        "Train YOLOv8 on [Detection](https://docs.ultralytics.com/tasks/detection/), [Segmentation](https://docs.ultralytics.com/tasks/detection/) and [Classification](https://docs.ultralytics.com/tasks/detection/) datasets."
+        "Train YOLOv8 on [Detection](https://docs.ultralytics.com/tasks/detection/), [Segmentation](https://docs.ultralytics.com/tasks/segmentation/) and [Classification](https://docs.ultralytics.com/tasks/classification/) datasets."
       ]
     },
     {
       "cell_type": "code",
       "metadata": {
-        "id": "1NcFxRcFdJ_O",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "0f87de5c-da4e-4290-ee64-2de4d4d7cd8e"
+        "id": "1NcFxRcFdJ_O"
       },
       "source": [
         "# Train YOLOv8n on COCO128 for 3 epochs\n",
         "!yolo task=detect mode=train model=yolov8n.pt data=coco128.yaml epochs=3 imgsz=640"
       ],
       "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "\u001b[34m\u001b[1myolo/engine/trainer: \u001b[0mtask=detect, mode=train, model=yolov8n.pt, data=coco128.yaml, epochs=3, patience=50, batch=16, imgsz=640, save=True, cache=False, device=None, workers=8, project=None, name=None, exist_ok=False, pretrained=False, optimizer=SGD, verbose=False, seed=0, deterministic=True, single_cls=False, image_weights=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, show=False, save_txt=False, save_conf=False, save_crop=False, hide_labels=False, hide_conf=False, vid_stride=1, line_thickness=3, visualize=False, augment=False, agnostic_nms=False, retina_masks=False, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=17, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, fl_gamma=0.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0, hydra={'output_subdir': None, 'run': {'dir': '.'}}, v5loader=False, save_dir=runs/detect/train\n",
-            "Ultralytics YOLOv8.0.1 🚀 Python-3.8.16 torch-1.13.0+cu116 CUDA:0 (Tesla T4, 15110MiB)\n",
-            "\n",
-            "                   from  n    params  module                                       arguments                     \n",
-            "  0                  -1  1       464  ultralytics.nn.modules.Conv                  [3, 16, 3, 2]                 \n",
-            "  1                  -1  1      4672  ultralytics.nn.modules.Conv                  [16, 32, 3, 2]                \n",
-            "  2                  -1  1      7360  ultralytics.nn.modules.C2f                   [32, 32, 1, True]             \n",
-            "  3                  -1  1     18560  ultralytics.nn.modules.Conv                  [32, 64, 3, 2]                \n",
-            "  4                  -1  2     49664  ultralytics.nn.modules.C2f                   [64, 64, 2, True]             \n",
-            "  5                  -1  1     73984  ultralytics.nn.modules.Conv                  [64, 128, 3, 2]               \n",
-            "  6                  -1  2    197632  ultralytics.nn.modules.C2f                   [128, 128, 2, True]           \n",
-            "  7                  -1  1    295424  ultralytics.nn.modules.Conv                  [128, 256, 3, 2]              \n",
-            "  8                  -1  1    460288  ultralytics.nn.modules.C2f                   [256, 256, 1, True]           \n",
-            "  9                  -1  1    164608  ultralytics.nn.modules.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.Concat                [1]                           \n",
-            " 12                  -1  1    148224  ultralytics.nn.modules.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.Concat                [1]                           \n",
-            " 15                  -1  1     37248  ultralytics.nn.modules.C2f                   [192, 64, 1]                  \n",
-            " 16                  -1  1     36992  ultralytics.nn.modules.Conv                  [64, 64, 3, 2]                \n",
-            " 17            [-1, 12]  1         0  ultralytics.nn.modules.Concat                [1]                           \n",
-            " 18                  -1  1    123648  ultralytics.nn.modules.C2f                   [192, 128, 1]                 \n",
-            " 19                  -1  1    147712  ultralytics.nn.modules.Conv                  [128, 128, 3, 2]              \n",
-            " 20             [-1, 9]  1         0  ultralytics.nn.modules.Concat                [1]                           \n",
-            " 21                  -1  1    493056  ultralytics.nn.modules.C2f                   [384, 256, 1]                 \n",
-            " 22        [15, 18, 21]  1    897664  ultralytics.nn.modules.Detect                [80, [64, 128, 256]]          \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[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.0005), 63 bias\n",
-            "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /datasets/coco128/labels/train2017.cache... 126 images, 2 backgrounds, 0 corrupt: 100% 128/128 [00:00<?, ?it/s]\n",
-            "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n",
-            "\u001b[34m\u001b[1mval: \u001b[0mScanning /datasets/coco128/labels/train2017.cache... 126 images, 2 backgrounds, 0 corrupt: 100% 128/128 [00:00<?, ?it/s]\n",
-            "Image sizes 640 train, 640 val\n",
-            "Using 2 dataloader workers\n",
-            "Logging results to \u001b[1mruns/detect/train\u001b[0m\n",
-            "Starting training for 3 epochs...\n",
-            "\n",
-            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
-            "        1/3      4.31G      1.221      1.429      1.241        196        640: 100% 8/8 [00:08<00:00,  1.06s/it]\n",
-            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 4/4 [00:01<00:00,  2.18it/s]\n",
-            "                   all        128        929      0.671      0.516      0.617      0.457\n",
-            "\n",
-            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
-            "        2/3      5.31G      1.186      1.306      1.255        287        640: 100% 8/8 [00:05<00:00,  1.57it/s]\n",
-            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 4/4 [00:01<00:00,  2.23it/s]\n",
-            "                   all        128        929      0.668      0.582      0.637      0.473\n",
-            "\n",
-            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
-            "        3/3      5.31G       1.17      1.408      1.267        189        640: 100% 8/8 [00:04<00:00,  1.62it/s]\n",
-            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 4/4 [00:03<00:00,  1.01it/s]\n",
-            "                   all        128        929      0.638      0.601      0.645      0.483\n",
-            "\n",
-            "3 epochs completed in 0.010 hours.\n",
-            "Optimizer stripped from runs/detect/train/weights/last.pt, 6.5MB\n",
-            "Optimizer stripped from runs/detect/train/weights/best.pt, 6.5MB\n",
-            "\n",
-            "Validating runs/detect/train/weights/best.pt...\n",
-            "Ultralytics YOLOv8.0.1 🚀 Python-3.8.16 torch-1.13.0+cu116 CUDA:0 (Tesla T4, 15110MiB)\n",
-            "Fusing layers... \n",
-            "Model summary: 168 layers, 3151904 parameters, 0 gradients, 8.7 GFLOPs\n",
-            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 4/4 [00:04<00:00,  1.20s/it]\n",
-            "                   all        128        929      0.638      0.602      0.644      0.483\n",
-            "                person        128        254      0.703      0.709      0.769      0.548\n",
-            "               bicycle        128          6      0.455      0.333      0.322      0.254\n",
-            "                   car        128         46      0.773      0.217      0.291      0.184\n",
-            "            motorcycle        128          5      0.551        0.8      0.895      0.724\n",
-            "              airplane        128          6      0.743      0.833      0.927       0.73\n",
-            "                   bus        128          7      0.692      0.714        0.7      0.636\n",
-            "                 train        128          3      0.733      0.931      0.913      0.797\n",
-            "                 truck        128         12      0.752        0.5      0.497      0.324\n",
-            "                  boat        128          6       0.41      0.333      0.492      0.344\n",
-            "         traffic light        128         14      0.682      0.214      0.202      0.139\n",
-            "             stop sign        128          2      0.933          1      0.995      0.671\n",
-            "                 bench        128          9      0.752      0.556      0.603      0.416\n",
-            "                  bird        128         16      0.875      0.876      0.957      0.641\n",
-            "                   cat        128          4      0.863          1      0.995       0.76\n",
-            "                   dog        128          9      0.554      0.778      0.855      0.664\n",
-            "                 horse        128          2      0.706          1      0.995      0.561\n",
-            "              elephant        128         17      0.761      0.882      0.929      0.722\n",
-            "                  bear        128          1      0.595          1      0.995      0.995\n",
-            "                 zebra        128          4       0.85          1      0.995      0.966\n",
-            "               giraffe        128          9      0.891          1      0.995      0.683\n",
-            "              backpack        128          6      0.487      0.333      0.354      0.224\n",
-            "              umbrella        128         18       0.54      0.667      0.687      0.461\n",
-            "               handbag        128         19      0.496      0.105      0.212      0.125\n",
-            "                   tie        128          7      0.611      0.714      0.615      0.432\n",
-            "              suitcase        128          4      0.469          1      0.745      0.529\n",
-            "               frisbee        128          5      0.622        0.8      0.733       0.64\n",
-            "                  skis        128          1      0.721          1      0.995      0.531\n",
-            "             snowboard        128          7      0.687      0.714      0.751       0.51\n",
-            "           sports ball        128          6       0.71       0.42      0.503      0.282\n",
-            "                  kite        128         10       0.81        0.5       0.59      0.197\n",
-            "          baseball bat        128          4      0.474      0.461      0.261      0.115\n",
-            "        baseball glove        128          7       0.67      0.429       0.43      0.317\n",
-            "            skateboard        128          5      0.751        0.6      0.599      0.387\n",
-            "         tennis racket        128          7      0.742      0.415      0.507      0.378\n",
-            "                bottle        128         18      0.409      0.333      0.354      0.235\n",
-            "            wine glass        128         16      0.562        0.5      0.597      0.356\n",
-            "                   cup        128         36       0.67      0.306      0.411      0.296\n",
-            "                  fork        128          6       0.57      0.167      0.229      0.203\n",
-            "                 knife        128         16      0.608      0.562      0.634      0.405\n",
-            "                 spoon        128         22      0.529      0.358      0.369      0.201\n",
-            "                  bowl        128         28      0.594      0.679      0.671       0.56\n",
-            "                banana        128          1     0.0625      0.312      0.199     0.0513\n",
-            "              sandwich        128          2      0.638      0.913      0.828      0.828\n",
-            "                orange        128          4      0.743      0.728      0.895      0.595\n",
-            "              broccoli        128         11       0.49      0.264      0.278      0.232\n",
-            "                carrot        128         24      0.547      0.667      0.704       0.47\n",
-            "               hot dog        128          2      0.578          1      0.828      0.796\n",
-            "                 pizza        128          5      0.835          1      0.995       0.84\n",
-            "                 donut        128         14      0.537          1      0.891      0.788\n",
-            "                  cake        128          4      0.807          1      0.995      0.904\n",
-            "                 chair        128         35      0.401      0.514      0.485      0.277\n",
-            "                 couch        128          6      0.795      0.649      0.746      0.504\n",
-            "          potted plant        128         14      0.563      0.643      0.676      0.471\n",
-            "                   bed        128          3      0.777          1      0.995      0.735\n",
-            "          dining table        128         13      0.425      0.692      0.578       0.48\n",
-            "                toilet        128          2      0.508        0.5      0.745      0.721\n",
-            "                    tv        128          2       0.55      0.649      0.828      0.762\n",
-            "                laptop        128          3          1          0      0.741      0.653\n",
-            "                 mouse        128          2          1          0     0.0454    0.00907\n",
-            "                remote        128          8       0.83        0.5      0.569      0.449\n",
-            "            cell phone        128          8          0          0     0.0819     0.0266\n",
-            "             microwave        128          3      0.475      0.667       0.83      0.699\n",
-            "                  oven        128          5        0.5        0.4      0.348      0.275\n",
-            "                  sink        128          6      0.354      0.187      0.368      0.217\n",
-            "          refrigerator        128          5      0.518        0.4      0.729      0.571\n",
-            "                  book        128         29      0.583      0.241      0.396      0.204\n",
-            "                 clock        128          9      0.891      0.889       0.91      0.773\n",
-            "                  vase        128          2      0.506          1      0.828      0.745\n",
-            "              scissors        128          1          1          0      0.142     0.0426\n",
-            "            teddy bear        128         21      0.587      0.476       0.63      0.458\n",
-            "            toothbrush        128          5      0.784      0.736      0.898      0.544\n",
-            "Speed: 0.4ms pre-process, 4.7ms inference, 0.0ms loss, 3.3ms post-process per image\n",
-            "Saving runs/detect/train/predictions.json...\n",
-            "Results saved to \u001b[1mruns/detect/train\u001b[0m\n"
-          ]
-        }
-      ]
+      "outputs": []
     },
     {
       "cell_type": "markdown",
@@ -462,6 +216,9 @@
         "\n",
         "Export a YOLOv8 model to any supported format with the `format` argument, i.e. `format=onnx`.\n",
         "\n",
+        "- 💡 ProTip: Export to ONNX or OpenVINO for up to 3x CPU speedup.  \n",
+        "- 💡 ProTip: Export to TensorRT for up to 5x GPU speedup.\n",
+        "\n",
         "\n",
         "| Format                                                                     | `format=`          | Model                     |\n",
         "|----------------------------------------------------------------------------|--------------------|---------------------------|\n",
@@ -524,7 +281,7 @@
       "source": [
         "# 5. Python Usage\n",
         "\n",
-        "YOLOv8 was reimagined using Python-first principles for the most seamless Python YOLO experience yet. YOLOv8 models can be loaded from a trained checkpoint or created from scratch. Then methods are used to train, val, predict, and export the model. See a detailed Python usage examples in the YOLOv8 [Docs](https://docs.ultralytics.com)."
+        "YOLOv8 was reimagined using Python-first principles for the most seamless Python YOLO experience yet. YOLOv8 models can be loaded from a trained checkpoint or created from scratch. Then methods are used to train, val, predict, and export the model. See a detailed Python usage examples in the YOLOv8 [Docs](https://docs.ultralytics.com/python/)."
       ],
       "metadata": {
         "id": "kUMOQ0OeDBJG"
@@ -578,12 +335,12 @@
     {
       "cell_type": "code",
       "source": [
-        "# Load a pretraind YOLOv8n detection model, train it on COCO128 for 3 epochs and predict an image with it\n",
+        "# Load YOLOv8n, train it on COCO128 for 3 epochs and predict an image with it\n",
         "from ultralytics import YOLO\n",
         "\n",
         "model = YOLO('yolov8n.pt')  # load a pretrained YOLOv8n detection model\n",
         "model.train(data='coco128.yaml', epochs=3)  # train the model\n",
-        "model.predict(source='https://ultralytics.com/images/bus.jpg')  # predict on an image"
+        "model('https://ultralytics.com/images/bus.jpg')  # predict on an image"
       ],
       "metadata": {
         "id": "8Go5qqS9LbC5"
@@ -605,12 +362,12 @@
     {
       "cell_type": "code",
       "source": [
-        "# Load a pretraind YOLOv8n segmentation model, train it on COCO128-seg for 3 epochs and predict an image with it\n",
+        "# Load YOLOv8n-seg, train it on COCO128-seg for 3 epochs and predict an image with it\n",
         "from ultralytics import YOLO\n",
         "\n",
         "model = YOLO('yolov8n-seg.pt')  # load a pretrained YOLOv8n segmentation model\n",
         "model.train(data='coco128-seg.yaml', epochs=3)  # train the model\n",
-        "model.predict(source='https://ultralytics.com/images/bus.jpg')  # predict on an image"
+        "model('https://ultralytics.com/images/bus.jpg')  # predict on an image"
       ],
       "metadata": {
         "id": "WFPJIQl_L5HT"
@@ -623,7 +380,7 @@
       "source": [
         "## 3. Classification\n",
         "\n",
-        "YOLOv8 _classification_ models use the `-cls` suffix, i.e. `yolov8n-cls.pt` and are pretrained on ImageNet. See [Classification Docs](https://docs.ultralytics.com/tasks/detection/) for full details.\n"
+        "YOLOv8 _classification_ models use the `-cls` suffix, i.e. `yolov8n-cls.pt` and are pretrained on ImageNet. See [Classification Docs](https://docs.ultralytics.com/tasks/classification/) for full details.\n"
       ],
       "metadata": {
         "id": "ax3p94VNK9zR"
@@ -632,12 +389,12 @@
     {
       "cell_type": "code",
       "source": [
-        "# Load a pretraind YOLOv8n classification model, train it on imagenette160 for 3 epochs and predict an image with it\n",
+        "# Load YOLOv8n-cls, train it on imagenette160 for 3 epochs and predict an image with it\n",
         "from ultralytics import YOLO\n",
         "\n",
         "model = YOLO('yolov8n-cls.pt')  # load a pretrained YOLOv8n classification model\n",
         "model.train(data='imagenette160', epochs=3)  # train the model\n",
-        "model.predict(source='https://ultralytics.com/images/bus.jpg')  # predict on an image"
+        "model('https://ultralytics.com/images/bus.jpg')  # predict on an image"
       ],
       "metadata": {
         "id": "5q9Zu6zlL5rS"