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Glenn Jocher committed 2023-04-09 16:18:24 +02:00
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@@ -4,360 +4,13 @@
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@@ -378,7 +31,7 @@
" <a href=\"https://www.kaggle.com/ultralytics/yolov5\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open In Kaggle\"></a>\n",
"<br>\n",
"\n",
"This <a href=\"https://github.com/ultralytics/yolov5\">YOLOv5</a> 🚀 notebook by <a href=\"https://ultralytics.com\">Ultralytics</a> presents simple train, validate and predict examples to help start your AI adventure.<br>See <a href=\"https://github.com/ultralytics/yolov5/issues/new/choose\">GitHub</a> for community support or <a href=\"https://ultralytics.com/contact\">contact us</a> for professional support.\n",
"This <a href=\"https://github.com/ultralytics/yolov5\">YOLOv5</a> 🚀 notebook by <a href=\"https://ultralytics.com\">Ultralytics</a> presents simple train, validate and predict examples to help start your AI adventure.<br>We hope that the resources in this notebook will help you get the most out of YOLOv5. Please browse the YOLOv5 <a href=\"https://docs.ultralytics.com/yolov5\">Docs</a> for details, raise an issue on <a href=\"https://github.com/ultralytics/yolov5\">GitHub</a> for support, and join our <a href=\"https://discord.gg/n6cFeSPZdD\">Discord</a> community for questions and discussions!\n",
"\n",
"</div>"
]
@@ -401,7 +54,7 @@
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "f9f016ad-3dcf-4bd2-e1c3-d5b79efc6f32"
"outputId": "e8225db4-e61d-4640-8b1f-8bfce3331cea"
},
"source": [
"!git clone https://github.com/ultralytics/yolov5 # clone\n",
@@ -412,20 +65,20 @@
"import utils\n",
"display = utils.notebook_init() # checks"
],
"execution_count": null,
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"YOLOv5 🚀 v7.0-1-gb32f67f Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n"
"YOLOv5 🚀 v7.0-136-g71244ae Python-3.9.16 torch-2.0.0+cu118 CUDA:0 (Tesla T4, 15102MiB)\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Setup complete ✅ (2 CPUs, 12.7 GB RAM, 22.6/78.2 GB disk)\n"
"Setup complete ✅ (2 CPUs, 12.7 GB RAM, 23.3/166.8 GB disk)\n"
]
}
]
@@ -459,29 +112,29 @@
"colab": {
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},
"outputId": "b4db5c49-f501-4505-cf0d-a1d35236c485"
"outputId": "284ef04b-1596-412f-88f6-948828dd2b49"
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"source": [
"!python detect.py --weights yolov5s.pt --img 640 --conf 0.25 --source data/images\n",
"# display.Image(filename='runs/detect/exp/zidane.jpg', width=600)"
],
"execution_count": null,
"execution_count": 13,
"outputs": [
{
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"text": [
"\u001b[34m\u001b[1mdetect: \u001b[0mweights=['yolov5s.pt'], source=data/images, data=data/coco128.yaml, 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, vid_stride=1\n",
"YOLOv5 🚀 v7.0-1-gb32f67f Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n",
"YOLOv5 🚀 v7.0-136-g71244ae Python-3.9.16 torch-2.0.0+cu118 CUDA:0 (Tesla T4, 15102MiB)\n",
"\n",
"Downloading https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt to yolov5s.pt...\n",
"100% 14.1M/14.1M [00:00<00:00, 116MB/s] \n",
"100% 14.1M/14.1M [00:00<00:00, 24.5MB/s]\n",
"\n",
"Fusing layers... \n",
"YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients\n",
"image 1/2 /content/yolov5/data/images/bus.jpg: 640x480 4 persons, 1 bus, 17.0ms\n",
"image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 2 ties, 14.3ms\n",
"Speed: 0.5ms pre-process, 15.7ms inference, 18.6ms NMS per image at shape (1, 3, 640, 640)\n",
"image 1/2 /content/yolov5/data/images/bus.jpg: 640x480 4 persons, 1 bus, 41.5ms\n",
"image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 2 ties, 60.0ms\n",
"Speed: 0.5ms pre-process, 50.8ms inference, 37.7ms NMS per image at shape (1, 3, 640, 640)\n",
"Results saved to \u001b[1mruns/detect/exp\u001b[0m\n"
]
}
@@ -512,44 +165,23 @@
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"source": [
"# Download COCO val\n",
"torch.hub.download_url_to_file('https://ultralytics.com/assets/coco2017val.zip', 'tmp.zip') # download (780M - 5000 images)\n",
"!unzip -q tmp.zip -d ../datasets && rm tmp.zip # unzip"
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" 0%| | 0.00/780M [00:00<?, ?B/s]"
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@@ -560,28 +192,28 @@
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"source": [
"# Validate YOLOv5s on COCO val\n",
"!python val.py --weights yolov5s.pt --data coco.yaml --img 640 --half"
],
"execution_count": null,
"execution_count": 4,
"outputs": [
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"\u001b[34m\u001b[1mval: \u001b[0mdata=/content/yolov5/data/coco.yaml, weights=['yolov5s.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=val, device=, workers=8, 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",
"YOLOv5 🚀 v7.0-1-gb32f67f Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n",
"YOLOv5 🚀 v7.0-136-g71244ae Python-3.9.16 torch-2.0.0+cu118 CUDA:0 (Tesla T4, 15102MiB)\n",
"\n",
"Fusing layers... \n",
"YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients\n",
"\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/coco/val2017... 4952 images, 48 backgrounds, 0 corrupt: 100% 5000/5000 [00:02<00:00, 1977.30it/s]\n",
"\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/coco/val2017... 4952 images, 48 backgrounds, 0 corrupt: 100% 5000/5000 [00:02<00:00, 2024.59it/s]\n",
"\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/datasets/coco/val2017.cache\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 157/157 [01:12<00:00, 2.17it/s]\n",
" all 5000 36335 0.67 0.521 0.566 0.371\n",
"Speed: 0.1ms pre-process, 2.9ms inference, 2.0ms NMS per image at shape (32, 3, 640, 640)\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 157/157 [01:25<00:00, 1.84it/s]\n",
" all 5000 36335 0.671 0.519 0.566 0.371\n",
"Speed: 0.1ms pre-process, 3.1ms inference, 2.3ms NMS per image at shape (32, 3, 640, 640)\n",
"\n",
"Evaluating pycocotools mAP... saving runs/val/exp/yolov5s_predictions.json...\n",
"loading annotations into memory...\n",
@@ -589,14 +221,14 @@
"creating index...\n",
"index created!\n",
"Loading and preparing results...\n",
"DONE (t=5.85s)\n",
"DONE (t=5.32s)\n",
"creating index...\n",
"index created!\n",
"Running per image evaluation...\n",
"Evaluate annotation type *bbox*\n",
"DONE (t=82.22s).\n",
"DONE (t=78.89s).\n",
"Accumulating evaluation results...\n",
"DONE (t=14.92s).\n",
"DONE (t=14.51s).\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.374\n",
" Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.572\n",
" Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.402\n",
@@ -608,7 +240,7 @@
" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.566\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.378\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.625\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.723\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.722\n",
"Results saved to \u001b[1mruns/val/exp\u001b[0m\n"
]
}
@@ -622,7 +254,7 @@
"source": [
"# 3. Train\n",
"\n",
"<p align=\"\"><a href=\"https://roboflow.com/?ref=ultralytics\"><img width=\"1000\" src=\"https://github.com/ultralytics/assets/raw/main/im/integrations-loop.png\"/></a></p>\n",
"<p align=\"\"><a href=\"https://bit.ly/ultralytics_hub\"><img width=\"1000\" src=\"https://github.com/ultralytics/assets/raw/main/im/integrations-loop.png\"/></a></p>\n",
"Close the active learning loop by sampling images from your inference conditions with the `roboflow` pip package\n",
"<br><br>\n",
"\n",
@@ -645,14 +277,14 @@
"cell_type": "code",
"source": [
"#@title Select YOLOv5 🚀 logger {run: 'auto'}\n",
"logger = 'ClearML' #@param ['ClearML', 'Comet', 'TensorBoard']\n",
"logger = 'Comet' #@param ['Comet', 'ClearML', 'TensorBoard']\n",
"\n",
"if logger == 'ClearML':\n",
" %pip install -q clearml\n",
" import clearml; clearml.browser_login()\n",
"elif logger == 'Comet':\n",
"if logger == 'Comet':\n",
" %pip install -q comet_ml\n",
" import comet_ml; comet_ml.init()\n",
"elif logger == 'ClearML':\n",
" %pip install -q clearml\n",
" import clearml; clearml.browser_login()\n",
"elif logger == 'TensorBoard':\n",
" %load_ext tensorboard\n",
" %tensorboard --logdir runs/train"
@@ -670,21 +302,24 @@
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "721b9028-767f-4a05-c964-692c245f7398"
"outputId": "bbeeea2b-04fc-4185-aa64-258690495b5a"
},
"source": [
"# Train YOLOv5s on COCO128 for 3 epochs\n",
"!python train.py --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --cache"
],
"execution_count": null,
"execution_count": 5,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"2023-04-09 14:11:38.063605: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
"To enable the following instructions: AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
"2023-04-09 14:11:39.026661: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
"\u001b[34m\u001b[1mtrain: \u001b[0mweights=yolov5s.pt, cfg=, data=coco128.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=3, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=ram, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, 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/yolov5 ✅\n",
"YOLOv5 🚀 v7.0-1-gb32f67f Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n",
"YOLOv5 🚀 v7.0-136-g71244ae Python-3.9.16 torch-2.0.0+cu118 CUDA:0 (Tesla T4, 15102MiB)\n",
"\n",
"\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=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=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[1mClearML: \u001b[0mrun 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 🚀 in ClearML\n",
@@ -693,8 +328,8 @@
"\n",
"Dataset not found ⚠️, missing paths ['/content/datasets/coco128/images/train2017']\n",
"Downloading https://ultralytics.com/assets/coco128.zip to coco128.zip...\n",
"100% 6.66M/6.66M [00:00<00:00, 261MB/s]\n",
"Dataset download success ✅ (0.3s), saved to \u001b[1m/content/datasets\u001b[0m\n",
"100% 6.66M/6.66M [00:00<00:00, 75.6MB/s]\n",
"Dataset download success ✅ (0.6s), saved to \u001b[1m/content/datasets\u001b[0m\n",
"\n",
" from n params module arguments \n",
" 0 -1 1 3520 models.common.Conv [3, 32, 6, 2, 2] \n",
@@ -728,11 +363,11 @@
"\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
"\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias\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[1mtrain: \u001b[0mScanning /content/datasets/coco128/labels/train2017... 126 images, 2 backgrounds, 0 corrupt: 100% 128/128 [00:00<00:00, 1911.57it/s]\n",
"\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/datasets/coco128/labels/train2017... 126 images, 2 backgrounds, 0 corrupt: 100% 128/128 [00:00<00:00, 1709.36it/s]\n",
"\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/datasets/coco128/labels/train2017.cache\n",
"\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (0.1GB ram): 100% 128/128 [00:00<00:00, 229.69it/s]\n",
"\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (0.1GB ram): 100% 128/128 [00:00<00:00, 264.35it/s]\n",
"\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrounds, 0 corrupt: 100% 128/128 [00:00<?, ?it/s]\n",
"\u001b[34m\u001b[1mval: \u001b[0mCaching images (0.1GB ram): 100% 128/128 [00:01<00:00, 97.70it/s] \n",
"\u001b[34m\u001b[1mval: \u001b[0mCaching images (0.1GB ram): 100% 128/128 [00:01<00:00, 107.05it/s]\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",
"Plotting labels to runs/train/exp/labels.jpg... \n",
@@ -742,100 +377,100 @@
"Starting training for 3 epochs...\n",
"\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
" 0/2 3.74G 0.04618 0.07207 0.017 232 640: 100% 8/8 [00:07<00:00, 1.10it/s]\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 4/4 [00:01<00:00, 2.28it/s]\n",
" all 128 929 0.672 0.594 0.682 0.451\n",
" 0/2 3.91G 0.04618 0.07209 0.01703 232 640: 100% 8/8 [00:09<00:00, 1.17s/it]\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 4/4 [00:01<00:00, 2.01it/s]\n",
" all 128 929 0.667 0.602 0.68 0.45\n",
"\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
" 1/2 5.36G 0.04623 0.06888 0.01821 201 640: 100% 8/8 [00:02<00:00, 3.29it/s]\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 4/4 [00:01<00:00, 3.17it/s]\n",
" all 128 929 0.721 0.639 0.724 0.48\n",
" 1/2 4.76G 0.04622 0.06891 0.01817 201 640: 100% 8/8 [00:02<00:00, 3.78it/s]\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 4/4 [00:01<00:00, 2.16it/s]\n",
" all 128 929 0.709 0.645 0.722 0.478\n",
"\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
" 2/2 5.36G 0.04361 0.06479 0.01698 227 640: 100% 8/8 [00:02<00:00, 3.46it/s]\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 4/4 [00:01<00:00, 3.11it/s]\n",
" all 128 929 0.758 0.641 0.731 0.487\n",
" 2/2 4.76G 0.0436 0.0647 0.01698 227 640: 100% 8/8 [00:01<00:00, 4.19it/s]\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 4/4 [00:01<00:00, 2.95it/s]\n",
" all 128 929 0.761 0.647 0.735 0.49\n",
"\n",
"3 epochs completed in 0.005 hours.\n",
"Optimizer stripped from runs/train/exp/weights/last.pt, 14.9MB\n",
"Optimizer stripped from runs/train/exp/weights/best.pt, 14.9MB\n",
"3 epochs completed in 0.006 hours.\n",
"Optimizer stripped from runs/train/exp/weights/last.pt, 14.8MB\n",
"Optimizer stripped from runs/train/exp/weights/best.pt, 14.8MB\n",
"\n",
"Validating runs/train/exp/weights/best.pt...\n",
"Fusing layers... \n",
"Model summary: 157 layers, 7225885 parameters, 0 gradients, 16.4 GFLOPs\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 4/4 [00:03<00:00, 1.05it/s]\n",
" all 128 929 0.757 0.641 0.732 0.487\n",
" person 128 254 0.86 0.705 0.804 0.528\n",
" bicycle 128 6 0.773 0.578 0.725 0.426\n",
" car 128 46 0.658 0.435 0.554 0.239\n",
" motorcycle 128 5 0.59 0.8 0.837 0.635\n",
" airplane 128 6 1 0.996 0.995 0.696\n",
" bus 128 7 0.635 0.714 0.756 0.666\n",
" train 128 3 0.691 0.333 0.753 0.511\n",
" truck 128 12 0.604 0.333 0.472 0.26\n",
" boat 128 6 0.941 0.333 0.46 0.183\n",
" traffic light 128 14 0.557 0.183 0.302 0.214\n",
" stop sign 128 2 0.827 1 0.995 0.846\n",
" bench 128 9 0.79 0.556 0.677 0.318\n",
" bird 128 16 0.962 1 0.995 0.663\n",
" cat 128 4 0.867 1 0.995 0.754\n",
" dog 128 9 1 0.649 0.903 0.654\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 4/4 [00:06<00:00, 1.56s/it]\n",
" all 128 929 0.759 0.646 0.734 0.49\n",
" person 128 254 0.857 0.706 0.805 0.525\n",
" bicycle 128 6 0.773 0.577 0.725 0.414\n",
" car 128 46 0.664 0.435 0.551 0.24\n",
" motorcycle 128 5 0.587 0.8 0.837 0.635\n",
" airplane 128 6 1 0.989 0.995 0.715\n",
" bus 128 7 0.635 0.714 0.753 0.651\n",
" train 128 3 0.686 0.333 0.72 0.504\n",
" truck 128 12 0.604 0.333 0.472 0.259\n",
" boat 128 6 0.938 0.333 0.449 0.177\n",
" traffic light 128 14 0.778 0.255 0.401 0.217\n",
" stop sign 128 2 0.826 1 0.995 0.895\n",
" bench 128 9 0.711 0.556 0.661 0.313\n",
" bird 128 16 0.962 1 0.995 0.642\n",
" cat 128 4 0.868 1 0.995 0.754\n",
" dog 128 9 1 0.652 0.899 0.651\n",
" horse 128 2 0.853 1 0.995 0.622\n",
" elephant 128 17 0.908 0.882 0.934 0.698\n",
" bear 128 1 0.697 1 0.995 0.995\n",
" zebra 128 4 0.867 1 0.995 0.905\n",
" giraffe 128 9 0.788 0.829 0.912 0.701\n",
" backpack 128 6 0.841 0.5 0.738 0.311\n",
" umbrella 128 18 0.786 0.815 0.859 0.48\n",
" handbag 128 19 0.772 0.263 0.366 0.216\n",
" tie 128 7 0.975 0.714 0.77 0.491\n",
" suitcase 128 4 0.643 0.75 0.912 0.563\n",
" frisbee 128 5 0.72 0.8 0.76 0.717\n",
" skis 128 1 0.748 1 0.995 0.3\n",
" snowboard 128 7 0.827 0.686 0.833 0.57\n",
" elephant 128 17 0.909 0.882 0.934 0.698\n",
" bear 128 1 0.696 1 0.995 0.995\n",
" zebra 128 4 0.855 1 0.995 0.905\n",
" giraffe 128 9 0.788 0.828 0.912 0.701\n",
" backpack 128 6 0.835 0.5 0.738 0.311\n",
" umbrella 128 18 0.785 0.814 0.859 0.48\n",
" handbag 128 19 0.759 0.263 0.366 0.205\n",
" tie 128 7 0.983 0.714 0.77 0.492\n",
" suitcase 128 4 0.656 1 0.945 0.631\n",
" frisbee 128 5 0.721 0.8 0.759 0.724\n",
" skis 128 1 0.737 1 0.995 0.3\n",
" snowboard 128 7 0.829 0.696 0.83 0.537\n",
" sports ball 128 6 0.637 0.667 0.602 0.311\n",
" kite 128 10 0.645 0.6 0.594 0.224\n",
" baseball bat 128 4 0.519 0.278 0.468 0.205\n",
" baseball glove 128 7 0.483 0.429 0.465 0.278\n",
" skateboard 128 5 0.923 0.6 0.687 0.493\n",
" tennis racket 128 7 0.774 0.429 0.544 0.333\n",
" bottle 128 18 0.577 0.379 0.551 0.275\n",
" wine glass 128 16 0.715 0.875 0.893 0.511\n",
" cup 128 36 0.843 0.667 0.833 0.531\n",
" fork 128 6 0.998 0.333 0.45 0.315\n",
" knife 128 16 0.77 0.688 0.695 0.399\n",
" spoon 128 22 0.839 0.473 0.638 0.383\n",
" bowl 128 28 0.765 0.583 0.715 0.512\n",
" banana 128 1 0.903 1 0.995 0.301\n",
" sandwich 128 2 1 0 0.359 0.301\n",
" orange 128 4 0.718 0.75 0.912 0.581\n",
" broccoli 128 11 0.545 0.364 0.43 0.319\n",
" carrot 128 24 0.62 0.625 0.724 0.495\n",
" hot dog 128 2 0.385 1 0.828 0.762\n",
" pizza 128 5 0.833 1 0.962 0.725\n",
" donut 128 14 0.631 1 0.96 0.833\n",
" cake 128 4 0.871 1 0.995 0.83\n",
" chair 128 35 0.583 0.6 0.608 0.318\n",
" couch 128 6 0.909 0.667 0.813 0.543\n",
" potted plant 128 14 0.745 0.786 0.822 0.48\n",
" bed 128 3 0.973 0.333 0.753 0.41\n",
" dining table 128 13 0.821 0.356 0.577 0.342\n",
" toilet 128 2 1 0.949 0.995 0.797\n",
" tv 128 2 0.566 1 0.995 0.796\n",
" laptop 128 3 1 0 0.59 0.311\n",
" mouse 128 2 1 0 0.105 0.0527\n",
" remote 128 8 1 0.623 0.634 0.538\n",
" cell phone 128 8 0.565 0.375 0.399 0.179\n",
" microwave 128 3 0.709 1 0.995 0.736\n",
" oven 128 5 0.328 0.4 0.43 0.282\n",
" sink 128 6 0.438 0.333 0.339 0.266\n",
" refrigerator 128 5 0.564 0.8 0.798 0.535\n",
" book 128 29 0.597 0.256 0.351 0.155\n",
" clock 128 9 0.763 0.889 0.934 0.737\n",
" vase 128 2 0.331 1 0.995 0.895\n",
" scissors 128 1 1 0 0.497 0.0552\n",
" teddy bear 128 21 0.857 0.57 0.837 0.544\n",
" toothbrush 128 5 0.799 1 0.928 0.556\n",
" kite 128 10 0.636 0.6 0.599 0.226\n",
" baseball bat 128 4 0.501 0.25 0.468 0.205\n",
" baseball glove 128 7 0.483 0.429 0.465 0.292\n",
" skateboard 128 5 0.932 0.6 0.687 0.493\n",
" tennis racket 128 7 0.77 0.429 0.547 0.332\n",
" bottle 128 18 0.577 0.379 0.554 0.276\n",
" wine glass 128 16 0.704 0.875 0.89 0.51\n",
" cup 128 36 0.841 0.667 0.837 0.533\n",
" fork 128 6 0.992 0.333 0.45 0.315\n",
" knife 128 16 0.768 0.688 0.695 0.403\n",
" spoon 128 22 0.838 0.47 0.639 0.384\n",
" bowl 128 28 0.764 0.58 0.716 0.513\n",
" banana 128 1 0.902 1 0.995 0.301\n",
" sandwich 128 2 1 0 0.359 0.326\n",
" orange 128 4 0.722 0.75 0.912 0.581\n",
" broccoli 128 11 0.547 0.364 0.432 0.317\n",
" carrot 128 24 0.619 0.625 0.724 0.495\n",
" hot dog 128 2 0.409 1 0.828 0.762\n",
" pizza 128 5 0.833 0.995 0.962 0.727\n",
" donut 128 14 0.631 1 0.96 0.839\n",
" cake 128 4 0.87 1 0.995 0.83\n",
" chair 128 35 0.583 0.6 0.608 0.317\n",
" couch 128 6 0.907 0.667 0.815 0.544\n",
" potted plant 128 14 0.739 0.786 0.823 0.48\n",
" bed 128 3 0.985 0.333 0.83 0.441\n",
" dining table 128 13 0.821 0.357 0.578 0.342\n",
" toilet 128 2 1 0.988 0.995 0.846\n",
" tv 128 2 0.57 1 0.995 0.796\n",
" laptop 128 3 1 0 0.593 0.312\n",
" mouse 128 2 1 0 0.089 0.0445\n",
" remote 128 8 1 0.624 0.634 0.538\n",
" cell phone 128 8 0.622 0.417 0.421 0.187\n",
" microwave 128 3 0.711 1 0.995 0.766\n",
" oven 128 5 0.329 0.4 0.43 0.282\n",
" sink 128 6 0.437 0.333 0.338 0.265\n",
" refrigerator 128 5 0.567 0.8 0.799 0.536\n",
" book 128 29 0.597 0.257 0.349 0.154\n",
" clock 128 9 0.765 0.889 0.932 0.736\n",
" vase 128 2 0.33 1 0.995 0.895\n",
" scissors 128 1 1 0 0.497 0.0498\n",
" teddy bear 128 21 0.856 0.569 0.841 0.547\n",
" toothbrush 128 5 0.8 1 0.928 0.574\n",
"Results saved to \u001b[1mruns/train/exp\u001b[0m\n"
]
}
@@ -967,4 +602,4 @@
"outputs": []
}
]
}
}