diff --git a/tutorial.ipynb b/tutorial.ipynb
index 4e7049d8..3bc0523c 100644
--- a/tutorial.ipynb
+++ b/tutorial.ipynb
@@ -402,7 +402,7 @@
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "outputId": "b15def02-a331-4580-9878-8d06900e296d"
+        "outputId": "7efd38e6-c41f-4fe3-9864-ce4fa43fbb5b"
       },
       "source": [
         "!git clone https://github.com/ultralytics/yolov3  # clone\n",
@@ -413,13 +413,13 @@
         "from yolov3 import utils\n",
         "display = utils.notebook_init()  # checks"
       ],
-      "execution_count": 11,
+      "execution_count": 24,
       "outputs": [
         {
           "output_type": "stream",
           "name": "stderr",
           "text": [
-            "YOLOv3 🚀 v9.6.0-0-g7eb23e3 torch 1.10.0+cu111 CUDA:0 (A100-SXM4-40GB, 40536MiB)\n"
+            "YOLOv3 🚀 v9.6.0-1-g93a2bcc torch 1.10.0+cu111 CUDA:0 (A100-SXM4-40GB, 40536MiB)\n"
           ]
         },
         {
@@ -459,26 +459,26 @@
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "outputId": "5754dd6d-b5b0-41aa-ce81-7cc7a4c30553"
+        "outputId": "486202a4-bae2-454f-da62-2c74676a3058"
       },
       "source": [
         "!python detect.py --weights yolov3.pt --img 640 --conf 0.25 --source data/images\n",
         "display.Image(filename='runs/detect/exp/zidane.jpg', width=600)"
       ],
-      "execution_count": 15,
+      "execution_count": 22,
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
             "\u001b[34m\u001b[1mdetect: \u001b[0mweights=['yolov3.pt'], source=data/images, imgsz=[640, 640], conf_thres=0.25, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=runs/detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False\n",
-            "YOLOv3 🚀 v9.6.0-0-g7eb23e3 torch 1.10.0+cu111 CUDA:0 (A100-SXM4-40GB, 40536MiB)\n",
+            "YOLOv3 🚀 v9.6.0-1-g93a2bcc torch 1.10.0+cu111 CUDA:0 (A100-SXM4-40GB, 40536MiB)\n",
             "\n",
             "Fusing layers... \n",
-            "Model Summary: 261 layers, 61922845 parameters, 0 gradients\n",
-            "image 1/2 /content/yolov3/data/images/bus.jpg: 640x480 4 persons, 1 bus, 1 tie, Done. (0.020s)\n",
+            "Model Summary: 261 layers, 61922845 parameters, 0 gradients, 156.1 GFLOPs\n",
+            "image 1/2 /content/yolov3/data/images/bus.jpg: 640x480 4 persons, 1 bus, 1 tie, 1 sports ball, Done. (0.020s)\n",
             "image 2/2 /content/yolov3/data/images/zidane.jpg: 384x640 2 persons, 3 ties, Done. (0.020s)\n",
-            "Speed: 0.6ms pre-process, 20.0ms inference, 1.2ms NMS per image at shape (1, 3, 640, 640)\n",
+            "Speed: 0.5ms pre-process, 20.0ms inference, 1.3ms NMS per image at shape (1, 3, 640, 640)\n",
             "Results saved to \u001b[1mruns/detect/exp\u001b[0m\n"
           ]
         }
@@ -567,27 +567,27 @@
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "outputId": "fe3159ef-a2e4-49e3-ec0b-2ef434e9a28e"
+        "outputId": "15c92efb-05ec-48e0-b9ef-ff34871354c8"
       },
       "source": [
         "# Run YOLOv3 on COCO val\n",
         "!python val.py --weights yolov3.pt --data coco.yaml --img 640 --iou 0.65 --half"
       ],
-      "execution_count": 13,
+      "execution_count": 23,
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
             "\u001b[34m\u001b[1mval: \u001b[0mdata=/content/yolov3/data/coco.yaml, weights=['yolov3.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.65, task=val, device=, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=True, project=runs/val, name=exp, exist_ok=False, half=True, dnn=False\n",
-            "YOLOv3 🚀 v9.6.0-0-g7eb23e3 torch 1.10.0+cu111 CUDA:0 (A100-SXM4-40GB, 40536MiB)\n",
+            "YOLOv3 🚀 v9.6.0-1-g93a2bcc torch 1.10.0+cu111 CUDA:0 (A100-SXM4-40GB, 40536MiB)\n",
             "\n",
             "Fusing layers... \n",
-            "Model Summary: 261 layers, 61922845 parameters, 0 gradients\n",
+            "Model Summary: 261 layers, 61922845 parameters, 0 gradients, 156.1 GFLOPs\n",
             "\u001b[34m\u001b[1mval: \u001b[0mScanning '../datasets/coco/val2017.cache' images and labels... 4952 found, 48 missing, 0 empty, 0 corrupted: 100% 5000/5000 [00:00<?, ?it/s]\n",
-            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 157/157 [00:42<00:00,  3.68it/s]\n",
-            "                 all       5000      36335      0.655      0.599      0.623       0.42\n",
-            "Speed: 0.1ms pre-process, 2.1ms inference, 1.2ms NMS per image at shape (32, 3, 640, 640)\n",
+            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 157/157 [00:45<00:00,  3.45it/s]\n",
+            "                 all       5000      36335       0.71      0.602      0.649      0.453\n",
+            "Speed: 0.1ms pre-process, 2.1ms inference, 1.5ms NMS per image at shape (32, 3, 640, 640)\n",
             "\n",
             "Evaluating pycocotools mAP... saving runs/val/exp/yolov3_predictions.json...\n",
             "loading annotations into memory...\n",
@@ -595,26 +595,26 @@
             "creating index...\n",
             "index created!\n",
             "Loading and preparing results...\n",
-            "DONE (t=4.95s)\n",
+            "DONE (t=4.99s)\n",
             "creating index...\n",
             "index created!\n",
             "Running per image evaluation...\n",
             "Evaluate annotation type *bbox*\n",
-            "DONE (t=86.74s).\n",
+            "DONE (t=91.45s).\n",
             "Accumulating evaluation results...\n",
-            "DONE (t=12.46s).\n",
-            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.433\n",
-            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.630\n",
-            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.470\n",
-            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.283\n",
-            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.485\n",
-            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.538\n",
-            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.346\n",
-            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.580\n",
-            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.634\n",
-            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.473\n",
-            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.686\n",
-            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.766\n",
+            "DONE (t=15.18s).\n",
+            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.464\n",
+            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.655\n",
+            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.507\n",
+            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.298\n",
+            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.515\n",
+            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.585\n",
+            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.359\n",
+            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.594\n",
+            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.649\n",
+            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.476\n",
+            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.698\n",
+            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.779\n",
             "Results saved to \u001b[1mruns/val/exp\u001b[0m\n"
           ]
         }
@@ -711,13 +711,13 @@
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "outputId": "ed857ac6-f674-425b-8163-c7ae0c107311"
+        "outputId": "a601aa72-687c-4dda-a16c-c0b2d9073910"
       },
       "source": [
         "# Train YOLOv3 on COCO128 for 3 epochs\n",
         "!python train.py --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov3.pt --cache"
       ],
-      "execution_count": 14,
+      "execution_count": 21,
       "outputs": [
         {
           "output_type": "stream",
@@ -725,7 +725,7 @@
           "text": [
             "\u001b[34m\u001b[1mtrain: \u001b[0mweights=yolov3.pt, cfg=, data=coco128.yaml, hyp=data/hyps/hyp.scratch.yaml, epochs=3, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, evolve=None, bucket=, cache=ram, image_weights=False, device=, multi_scale=False, single_cls=False, adam=False, sync_bn=False, workers=8, project=runs/train, name=exp, exist_ok=False, quad=False, linear_lr=False, label_smoothing=0.0, patience=100, freeze=0, save_period=-1, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest\n",
             "\u001b[34m\u001b[1mgithub: \u001b[0mup to date with https://github.com/ultralytics/yolov3 ✅\n",
-            "YOLOv3 🚀 v9.6.0-0-g7eb23e3 torch 1.10.0+cu111 CUDA:0 (A100-SXM4-40GB, 40536MiB)\n",
+            "YOLOv3 🚀 v9.6.0-1-g93a2bcc torch 1.10.0+cu111 CUDA:0 (A100-SXM4-40GB, 40536MiB)\n",
             "\n",
             "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.1, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, 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\n",
             "\u001b[34m\u001b[1mWeights & Biases: \u001b[0mrun 'pip install wandb' to automatically track and visualize YOLOv3 🚀 runs (RECOMMENDED)\n",
@@ -768,9 +768,9 @@
             "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD with parameter groups 72 weight, 75 weight (no decay), 75 bias\n",
             "\u001b[34m\u001b[1malbumentations: \u001b[0mversion 1.0.3 required by YOLOv3, but version 0.1.12 is currently installed\n",
             "\u001b[34m\u001b[1mtrain: \u001b[0mScanning '../datasets/coco128/labels/train2017.cache' images and labels... 128 found, 0 missing, 2 empty, 0 corrupted: 100% 128/128 [00:00<?, ?it/s]\n",
-            "\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (0.1GB ram): 100% 128/128 [00:00<00:00, 1510.60it/s]\n",
+            "\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (0.1GB ram): 100% 128/128 [00:00<00:00, 1333.45it/s]\n",
             "\u001b[34m\u001b[1mval: \u001b[0mScanning '../datasets/coco128/labels/train2017.cache' images and labels... 128 found, 0 missing, 2 empty, 0 corrupted: 100% 128/128 [00:00<?, ?it/s]\n",
-            "\u001b[34m\u001b[1mval: \u001b[0mCaching images (0.1GB ram): 100% 128/128 [00:00<00:00, 400.72it/s]\n",
+            "\u001b[34m\u001b[1mval: \u001b[0mCaching images (0.1GB ram): 100% 128/128 [00:00<00:00, 377.72it/s]\n",
             "Plotting labels to runs/train/exp/labels.jpg... \n",
             "\n",
             "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m4.27 anchors/target, 0.994 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
@@ -780,100 +780,100 @@
             "Starting training for 3 epochs...\n",
             "\n",
             "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
-            "       0/2     12.2G   0.06494   0.08502   0.01907       186       640: 100% 8/8 [00:04<00:00,  1.94it/s]\n",
-            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 4/4 [00:00<00:00,  5.14it/s]\n",
-            "                 all        128        929        0.5      0.343      0.394      0.175\n",
-            "\n",
-            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
-            "       1/2     16.3G   0.06255   0.08206   0.01831       164       640: 100% 8/8 [00:01<00:00,  4.80it/s]\n",
+            "       0/2     12.2G   0.03673   0.05445    0.0102       186       640: 100% 8/8 [00:04<00:00,  1.93it/s]\n",
             "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 4/4 [00:00<00:00,  5.11it/s]\n",
-            "                 all        128        929      0.487      0.437      0.475      0.205\n",
+            "                 all        128        929      0.769      0.747      0.822      0.584\n",
             "\n",
             "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
-            "       2/2     16.3G   0.06032   0.07331   0.01861       192       640: 100% 8/8 [00:01<00:00,  4.81it/s]\n",
-            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 4/4 [00:00<00:00,  5.17it/s]\n",
-            "                 all        128        929      0.565      0.455      0.528      0.231\n",
+            "       1/2     16.3G   0.03681   0.05345   0.01067       164       640: 100% 8/8 [00:01<00:00,  4.77it/s]\n",
+            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 4/4 [00:00<00:00,  4.98it/s]\n",
+            "                 all        128        929      0.745       0.77      0.827      0.589\n",
             "\n",
-            "3 epochs completed in 0.006 hours.\n",
+            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
+            "       2/2     16.3G   0.03496   0.04829   0.01105       192       640: 100% 8/8 [00:01<00:00,  4.77it/s]\n",
+            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 4/4 [00:00<00:00,  5.20it/s]\n",
+            "                 all        128        929      0.779      0.752      0.829      0.588\n",
+            "\n",
+            "3 epochs completed in 0.005 hours.\n",
             "Optimizer stripped from runs/train/exp/weights/last.pt, 124.4MB\n",
             "Optimizer stripped from runs/train/exp/weights/best.pt, 124.4MB\n",
             "\n",
             "Validating runs/train/exp/weights/best.pt...\n",
             "Fusing layers... \n",
             "Model Summary: 261 layers, 61922845 parameters, 0 gradients, 156.1 GFLOPs\n",
-            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 4/4 [00:03<00:00,  1.22it/s]\n",
-            "                 all        128        929      0.565      0.454      0.528      0.231\n",
-            "              person        128        254      0.576      0.567      0.593      0.245\n",
-            "             bicycle        128          6      0.184      0.167      0.273      0.124\n",
-            "                 car        128         46      0.568       0.13      0.217     0.0537\n",
-            "          motorcycle        128          5      0.683        0.8      0.818      0.272\n",
-            "            airplane        128          6      0.766      0.555      0.809      0.342\n",
-            "                 bus        128          7      0.718      0.714      0.752      0.541\n",
-            "               train        128          3      0.676      0.667      0.863      0.221\n",
-            "               truck        128         12      0.699      0.333      0.469      0.186\n",
-            "                boat        128          6      0.675      0.333      0.472       0.19\n",
-            "       traffic light        128         14      0.176     0.0252     0.0721     0.0435\n",
-            "           stop sign        128          2       0.37        0.5      0.497      0.137\n",
-            "               bench        128          9       0.48      0.333      0.393      0.126\n",
-            "                bird        128         16      0.518      0.471       0.59      0.224\n",
-            "                 cat        128          4      0.656       0.96      0.828      0.555\n",
-            "                 dog        128          9      0.534      0.333      0.523      0.265\n",
-            "               horse        128          2        0.7          1      0.995      0.596\n",
-            "            elephant        128         17      0.677      0.647      0.754      0.286\n",
-            "                bear        128          1      0.684          1      0.995      0.398\n",
-            "               zebra        128          4      0.755          1      0.945        0.4\n",
-            "             giraffe        128          9      0.275      0.889      0.767      0.328\n",
-            "            backpack        128          6      0.611      0.167      0.463      0.183\n",
-            "            umbrella        128         18      0.679      0.706      0.749       0.25\n",
-            "             handbag        128         19      0.663      0.158      0.305      0.157\n",
-            "                 tie        128          7      0.811      0.618      0.753       0.29\n",
-            "            suitcase        128          4      0.522        0.5      0.781       0.34\n",
-            "             frisbee        128          5      0.552      0.503      0.454      0.289\n",
-            "                skis        128          1          0          0      0.124     0.0124\n",
-            "           snowboard        128          7      0.749      0.571      0.695      0.305\n",
-            "         sports ball        128          6      0.609      0.167      0.177     0.0865\n",
-            "                kite        128         10      0.225        0.2      0.154      0.026\n",
-            "        baseball bat        128          4      0.956       0.25      0.461      0.212\n",
-            "      baseball glove        128          7      0.905      0.143      0.248      0.159\n",
-            "          skateboard        128          5      0.611        0.2      0.371       0.16\n",
-            "       tennis racket        128          7      0.427      0.286      0.343      0.135\n",
-            "              bottle        128         18      0.432      0.278      0.425      0.167\n",
-            "          wine glass        128         16      0.614      0.438      0.531      0.239\n",
-            "                 cup        128         36      0.421      0.278      0.411      0.121\n",
-            "                fork        128          6      0.439      0.333      0.441      0.123\n",
-            "               knife        128         16      0.744      0.365      0.419      0.161\n",
-            "               spoon        128         22      0.487      0.273      0.342      0.112\n",
-            "                bowl        128         28      0.582      0.496      0.615      0.382\n",
-            "              banana        128          1      0.776          1      0.995      0.298\n",
-            "            sandwich        128          2          1          0      0.663      0.498\n",
-            "              orange        128          4      0.463      0.658      0.472      0.235\n",
-            "            broccoli        128         11      0.133      0.182      0.123     0.0912\n",
-            "              carrot        128         24      0.225       0.25      0.266      0.112\n",
-            "             hot dog        128          2      0.647          1      0.995      0.275\n",
-            "               pizza        128          5      0.398        0.8      0.639      0.322\n",
-            "               donut        128         14      0.662          1       0.82      0.436\n",
-            "                cake        128          4      0.775      0.877      0.945      0.449\n",
-            "               chair        128         35      0.323        0.4      0.328      0.113\n",
-            "               couch        128          6      0.428      0.333      0.399      0.251\n",
-            "        potted plant        128         14      0.806      0.643      0.678       0.28\n",
-            "                 bed        128          3      0.667      0.333       0.83      0.416\n",
-            "        dining table        128         13      0.337      0.615      0.291      0.128\n",
-            "              toilet        128          2      0.791          1      0.995      0.298\n",
-            "                  tv        128          2      0.629          1      0.828      0.414\n",
-            "              laptop        128          3          1          0      0.194      0.179\n",
-            "               mouse        128          2          1          0      0.129     0.0258\n",
-            "              remote        128          8      0.217      0.125      0.204     0.0994\n",
-            "          cell phone        128          8      0.557      0.168       0.42      0.076\n",
-            "           microwave        128          3      0.652      0.667      0.747      0.292\n",
-            "                oven        128          5      0.422        0.4      0.486      0.195\n",
-            "                sink        128          6      0.275      0.333       0.24      0.118\n",
-            "        refrigerator        128          5      0.568        0.6      0.699      0.371\n",
-            "                book        128         29      0.411      0.138      0.263     0.0897\n",
-            "               clock        128          9      0.509      0.333      0.572      0.291\n",
-            "                vase        128          2      0.257      0.523      0.695      0.367\n",
-            "            scissors        128          1          1          0     0.0113    0.00226\n",
-            "          teddy bear        128         21      0.508      0.333      0.403      0.146\n",
-            "          toothbrush        128          5      0.279        0.2      0.263     0.0837\n",
+            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00,  1.36it/s]\n",
+            "                 all        128        929      0.746       0.77      0.827      0.589\n",
+            "              person        128        254      0.862      0.787      0.863      0.624\n",
+            "             bicycle        128          6      0.723      0.667      0.723       0.47\n",
+            "                 car        128         46      0.851      0.435      0.681      0.305\n",
+            "          motorcycle        128          5      0.816          1      0.962      0.784\n",
+            "            airplane        128          6      0.916          1      0.995      0.786\n",
+            "                 bus        128          7          1      0.684      0.937      0.797\n",
+            "               train        128          3      0.989          1      0.995      0.863\n",
+            "               truck        128         12      0.728        0.5       0.61      0.414\n",
+            "                boat        128          6        0.7        0.5      0.582      0.337\n",
+            "       traffic light        128         14          1      0.452       0.58      0.268\n",
+            "           stop sign        128          2      0.712          1      0.995      0.747\n",
+            "               bench        128          9      0.793      0.853      0.848      0.507\n",
+            "                bird        128         16      0.966          1      0.995      0.655\n",
+            "                 cat        128          4      0.862          1      0.995      0.958\n",
+            "                 dog        128          9      0.787          1      0.995      0.774\n",
+            "               horse        128          2      0.695          1      0.995      0.697\n",
+            "            elephant        128         17      0.938      0.894      0.943      0.767\n",
+            "                bear        128          1      0.672          1      0.995      0.895\n",
+            "               zebra        128          4      0.869          1      0.995      0.995\n",
+            "             giraffe        128          9      0.942          1      0.995      0.733\n",
+            "            backpack        128          6      0.668       0.67      0.754      0.505\n",
+            "            umbrella        128         18      0.893      0.889      0.948      0.646\n",
+            "             handbag        128         19       0.71      0.526       0.55      0.336\n",
+            "                 tie        128          7      0.905      0.857      0.857        0.6\n",
+            "            suitcase        128          4      0.857          1      0.995      0.722\n",
+            "             frisbee        128          5      0.729        0.8      0.761      0.658\n",
+            "                skis        128          1      0.685          1      0.995      0.497\n",
+            "           snowboard        128          7      0.923      0.714      0.824      0.628\n",
+            "         sports ball        128          6      0.565      0.667      0.712      0.435\n",
+            "                kite        128         10      0.668        0.6      0.671      0.188\n",
+            "        baseball bat        128          4      0.663          1      0.945      0.422\n",
+            "      baseball glove        128          7      0.697      0.663      0.649      0.331\n",
+            "          skateboard        128          5      0.792        0.8      0.766      0.455\n",
+            "       tennis racket        128          7      0.862      0.714      0.718      0.359\n",
+            "              bottle        128         18       0.69      0.722      0.764      0.497\n",
+            "          wine glass        128         16      0.759      0.875      0.891      0.559\n",
+            "                 cup        128         36      0.865      0.889      0.916      0.635\n",
+            "                fork        128          6      0.671        0.5       0.76      0.474\n",
+            "               knife        128         16      0.751      0.812      0.842      0.512\n",
+            "               spoon        128         22      0.832      0.636      0.687      0.473\n",
+            "                bowl        128         28      0.873       0.75      0.774      0.625\n",
+            "              banana        128          1      0.767          1      0.995      0.597\n",
+            "            sandwich        128          2      0.134      0.134      0.448      0.408\n",
+            "              orange        128          4      0.534          1      0.995      0.746\n",
+            "            broccoli        128         11      0.474      0.364       0.36        0.3\n",
+            "              carrot        128         24      0.762       0.75      0.861      0.573\n",
+            "             hot dog        128          2      0.561          1      0.995      0.995\n",
+            "               pizza        128          5      0.893          1      0.995      0.771\n",
+            "               donut        128         14      0.735          1      0.959      0.903\n",
+            "                cake        128          4      0.711          1      0.995      0.908\n",
+            "               chair        128         35      0.639      0.771      0.793      0.505\n",
+            "               couch        128          6      0.702      0.667      0.796      0.599\n",
+            "        potted plant        128         14      0.697      0.714      0.827      0.555\n",
+            "                 bed        128          3          1          1      0.995      0.643\n",
+            "        dining table        128         13      0.566      0.403      0.521       0.32\n",
+            "              toilet        128          2        0.8          1      0.995      0.945\n",
+            "                  tv        128          2      0.473          1      0.995      0.821\n",
+            "              laptop        128          3      0.507      0.347       0.72      0.481\n",
+            "               mouse        128          2          1          0      0.828        0.5\n",
+            "              remote        128          8      0.845      0.625      0.751      0.628\n",
+            "          cell phone        128          8      0.503      0.375      0.571       0.35\n",
+            "           microwave        128          3      0.643          1      0.995      0.816\n",
+            "                oven        128          5       0.47        0.6      0.615      0.465\n",
+            "                sink        128          6      0.403      0.333      0.465       0.32\n",
+            "        refrigerator        128          5      0.923        0.8      0.862      0.659\n",
+            "                book        128         29      0.552      0.241      0.354      0.161\n",
+            "               clock        128          9      0.915          1      0.995        0.8\n",
+            "                vase        128          2       0.44          1      0.663      0.622\n",
+            "            scissors        128          1      0.634          1      0.995     0.0995\n",
+            "          teddy bear        128         21      0.874      0.658      0.926      0.621\n",
+            "          toothbrush        128          5      0.902          1      0.995      0.794\n",
             "Results saved to \u001b[1mruns/train/exp\u001b[0m\n"
           ]
         }