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Glenn Jocher
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"source": [
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"!git clone https://github.com/ultralytics/yolov5 # clone\n",
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@ -414,20 +413,20 @@
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"import utils\n",
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"display = utils.notebook_init() # checks"
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],
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"execution_count": null,
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"outputs": [
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"text": [
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"YOLOv5 🚀 v6.2-56-g30e674b Python-3.7.13 torch-1.12.1+cu113 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB)\n"
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"YOLOv5 🚀 v6.2-250-g467a57f Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n"
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]
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{
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"output_type": "stream",
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"text": [
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"Setup complete ✅ (8 CPUs, 51.0 GB RAM, 37.4/166.8 GB disk)\n"
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"Setup complete ✅ (2 CPUs, 12.7 GB RAM, 22.6/78.2 GB disk)\n"
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]
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}
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]
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@ -461,29 +460,29 @@
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"colab": {
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},
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"outputId": "de684b46-7623-4836-ee44-49cdb320cbf3"
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},
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"source": [
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"!python detect.py --weights yolov5s.pt --img 640 --conf 0.25 --source data/images\n",
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"# display.Image(filename='runs/detect/exp/zidane.jpg', width=600)"
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],
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"execution_count": null,
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"execution_count": 2,
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"outputs": [
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"text": [
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"\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\n",
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"YOLOv5 🚀 v6.2-56-g30e674b Python-3.7.13 torch-1.12.1+cu113 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB)\n",
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"\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",
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"YOLOv5 🚀 v6.2-250-g467a57f Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n",
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"\n",
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"Downloading https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5s.pt to yolov5s.pt...\n",
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"100% 14.1M/14.1M [00:00<00:00, 27.8MB/s]\n",
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"100% 14.1M/14.1M [00:00<00:00, 162MB/s]\n",
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"\n",
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"Fusing layers... \n",
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"YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients\n",
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"image 1/2 /content/yolov5/data/images/bus.jpg: 640x480 4 persons, 1 bus, 14.8ms\n",
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"image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 2 ties, 20.1ms\n",
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"Speed: 0.6ms pre-process, 17.4ms inference, 21.6ms NMS per image at shape (1, 3, 640, 640)\n",
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"image 1/2 /content/yolov5/data/images/bus.jpg: 640x480 4 persons, 1 bus, 17.2ms\n",
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"image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 2 ties, 13.3ms\n",
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"Speed: 0.5ms pre-process, 15.2ms inference, 19.5ms NMS per image at shape (1, 3, 640, 640)\n",
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"Results saved to \u001b[1mruns/detect/exp\u001b[0m\n"
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]
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}
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@ -517,27 +516,27 @@
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"base_uri": "https://localhost:8080/",
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},
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"source": [
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"# Download COCO val\n",
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"torch.hub.download_url_to_file('https://ultralytics.com/assets/coco2017val.zip', 'tmp.zip') # download (780M - 5000 images)\n",
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"!unzip -q tmp.zip -d ../datasets && rm tmp.zip # unzip"
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],
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"execution_count": null,
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"execution_count": 3,
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"outputs": [
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{
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"output_type": "display_data",
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"version_minor": 0,
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"metadata": {}
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@ -562,45 +561,43 @@
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"colab": {
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},
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"source": [
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"# Validate YOLOv5s on COCO val\n",
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"!python val.py --weights yolov5s.pt --data coco.yaml --img 640 --half"
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],
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"execution_count": null,
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"execution_count": 4,
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"outputs": [
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"text": [
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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, 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",
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"YOLOv5 🚀 v6.2-56-g30e674b Python-3.7.13 torch-1.12.1+cu113 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB)\n",
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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",
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"YOLOv5 🚀 v6.2-250-g467a57f Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n",
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"\n",
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"Fusing layers... \n",
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"YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients\n",
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"Downloading https://ultralytics.com/assets/Arial.ttf to /root/.config/Ultralytics/Arial.ttf...\n",
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"100% 755k/755k [00:00<00:00, 52.7MB/s]\n",
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"\u001b[34m\u001b[1mval: \u001b[0mScanning '/content/datasets/coco/val2017' images and labels...4952 found, 48 missing, 0 empty, 0 corrupt: 100% 5000/5000 [00:00<00:00, 10509.20it/s]\n",
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"\u001b[34m\u001b[1mval: \u001b[0mScanning '/content/datasets/coco/val2017' images and labels...4952 found, 48 missing, 0 empty, 0 corrupt: 100% 5000/5000 [00:02<00:00, 2019.92it/s]\n",
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"\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/datasets/coco/val2017.cache\n",
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" Class Images Instances P R mAP@.5 mAP@.5:.95: 100% 157/157 [00:50<00:00, 3.10it/s]\n",
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" Class Images Instances P R mAP50 mAP50-95: 100% 157/157 [01:09<00:00, 2.25it/s]\n",
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" all 5000 36335 0.67 0.521 0.566 0.371\n",
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"Speed: 0.1ms pre-process, 1.0ms inference, 1.5ms NMS per image at shape (32, 3, 640, 640)\n",
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"Speed: 0.2ms pre-process, 2.7ms inference, 2.1ms NMS per image at shape (32, 3, 640, 640)\n",
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"\n",
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"Evaluating pycocotools mAP... saving runs/val/exp/yolov5s_predictions.json...\n",
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"loading annotations into memory...\n",
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"Done (t=0.81s)\n",
|
||||
"Done (t=0.41s)\n",
|
||||
"creating index...\n",
|
||||
"index created!\n",
|
||||
"Loading and preparing results...\n",
|
||||
"DONE (t=5.62s)\n",
|
||||
"DONE (t=6.19s)\n",
|
||||
"creating index...\n",
|
||||
"index created!\n",
|
||||
"Running per image evaluation...\n",
|
||||
"Evaluate annotation type *bbox*\n",
|
||||
"DONE (t=77.03s).\n",
|
||||
"DONE (t=75.81s).\n",
|
||||
"Accumulating evaluation results...\n",
|
||||
"DONE (t=14.63s).\n",
|
||||
"DONE (t=15.26s).\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",
|
||||
|
|
@ -612,7 +609,7 @@
|
|||
" 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 ] = 0.724\n",
|
||||
" Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.723\n",
|
||||
"Results saved to \u001b[1mruns/val/exp\u001b[0m\n"
|
||||
]
|
||||
}
|
||||
|
|
@ -664,7 +661,8 @@
|
|||
" %pip install -q comet_ml\n",
|
||||
" import comet_ml; comet_ml.init()\n",
|
||||
"elif logger == 'ClearML':\n",
|
||||
" %pip install -q clearml && clearml-init"
|
||||
" %pip install -q clearml\n",
|
||||
" import clearml; clearml.browser_login()"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "i3oKtE4g-aNn"
|
||||
|
|
@ -679,13 +677,13 @@
|
|||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"outputId": "baa6d4be-3379-4aab-844a-d5a5396c0e49"
|
||||
"outputId": "7d03d4d2-9a6e-47de-88f4-c673b55c73c5"
|
||||
},
|
||||
"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",
|
||||
|
|
@ -693,17 +691,17 @@
|
|||
"text": [
|
||||
"\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 🚀 v6.2-56-g30e674b Python-3.7.13 torch-1.12.1+cu113 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB)\n",
|
||||
"YOLOv5 🚀 v6.2-250-g467a57f Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\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[1mComet: \u001b[0mrun 'pip install comet' to automatically track and visualize YOLOv5 🚀 runs with Comet\n",
|
||||
"\u001b[34m\u001b[1mClearML: \u001b[0mrun 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 🚀 in ClearML\n",
|
||||
"\u001b[34m\u001b[1mComet: \u001b[0mrun 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet\n",
|
||||
"\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
|
||||
"\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, 41.1MB/s]\n",
|
||||
"Dataset download success ✅ (0.8s), saved to \u001b[1m/content/datasets\u001b[0m\n",
|
||||
"100% 6.66M/6.66M [00:00<00:00, 26.1MB/s]\n",
|
||||
"Dataset download success ✅ (0.9s), 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",
|
||||
|
|
@ -731,120 +729,120 @@
|
|||
" 22 [-1, 10] 1 0 models.common.Concat [1] \n",
|
||||
" 23 -1 1 1182720 models.common.C3 [512, 512, 1, False] \n",
|
||||
" 24 [17, 20, 23] 1 229245 models.yolo.Detect [80, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
|
||||
"Model summary: 270 layers, 7235389 parameters, 7235389 gradients, 16.6 GFLOPs\n",
|
||||
"Model summary: 214 layers, 7235389 parameters, 7235389 gradients, 16.6 GFLOPs\n",
|
||||
"\n",
|
||||
"Transferred 349/349 items from yolov5s.pt\n",
|
||||
"\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' images and labels...128 found, 0 missing, 2 empty, 0 corrupt: 100% 128/128 [00:00<00:00, 9659.25it/s]\n",
|
||||
"\u001b[34m\u001b[1mtrain: \u001b[0mScanning '/content/datasets/coco128/labels/train2017' images and labels...126 found, 2 missing, 0 empty, 0 corrupt: 100% 128/128 [00:00<00:00, 1989.66it/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, 951.31it/s]\n",
|
||||
"\u001b[34m\u001b[1mval: \u001b[0mScanning '/content/datasets/coco128/labels/train2017.cache' images and labels... 128 found, 0 missing, 2 empty, 0 corrupt: 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, 274.67it/s]\n",
|
||||
"\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (0.1GB ram): 100% 128/128 [00:00<00:00, 246.25it/s]\n",
|
||||
"\u001b[34m\u001b[1mval: \u001b[0mScanning '/content/datasets/coco128/labels/train2017.cache' images and labels... 126 found, 2 missing, 0 empty, 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, 101.45it/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",
|
||||
"Image sizes 640 train, 640 val\n",
|
||||
"Using 8 dataloader workers\n",
|
||||
"Using 2 dataloader workers\n",
|
||||
"Logging results to \u001b[1mruns/train/exp\u001b[0m\n",
|
||||
"Starting training for 3 epochs...\n",
|
||||
"\n",
|
||||
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
|
||||
" 0/2 3.44G 0.04529 0.06712 0.01835 323 640: 100% 8/8 [00:04<00:00, 1.71it/s]\n",
|
||||
" Class Images Instances P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:00<00:00, 4.02it/s]\n",
|
||||
" all 128 929 0.666 0.611 0.684 0.452\n",
|
||||
" 0/2 3.74G 0.04618 0.07207 0.017 232 640: 100% 8/8 [00:06<00:00, 1.33it/s]\n",
|
||||
" Class Images Instances P R mAP50 mAP50-95: 100% 4/4 [00:01<00:00, 2.84it/s]\n",
|
||||
" all 128 929 0.672 0.594 0.682 0.451\n",
|
||||
"\n",
|
||||
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
|
||||
" 1/2 4.46G 0.04244 0.06423 0.01611 236 640: 100% 8/8 [00:01<00:00, 7.91it/s]\n",
|
||||
" Class Images Instances P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:00<00:00, 4.19it/s]\n",
|
||||
" all 128 929 0.746 0.626 0.722 0.481\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, 3.12it/s]\n",
|
||||
" all 128 929 0.721 0.639 0.724 0.48\n",
|
||||
"\n",
|
||||
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
|
||||
" 2/2 4.46G 0.04695 0.06875 0.0173 189 640: 100% 8/8 [00:00<00:00, 8.05it/s]\n",
|
||||
" Class Images Instances P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:00<00:00, 4.29it/s]\n",
|
||||
" all 128 929 0.774 0.647 0.746 0.499\n",
|
||||
" 2/2 5.36G 0.04361 0.06479 0.01698 227 640: 100% 8/8 [00:02<00:00, 3.39it/s]\n",
|
||||
" Class Images Instances P R mAP50 mAP50-95: 100% 4/4 [00:01<00:00, 3.13it/s]\n",
|
||||
" all 128 929 0.758 0.641 0.731 0.487\n",
|
||||
"\n",
|
||||
"3 epochs completed in 0.003 hours.\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",
|
||||
"\n",
|
||||
"Validating runs/train/exp/weights/best.pt...\n",
|
||||
"Fusing layers... \n",
|
||||
"Model summary: 213 layers, 7225885 parameters, 0 gradients, 16.4 GFLOPs\n",
|
||||
" Class Images Instances P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:03<00:00, 1.21it/s]\n",
|
||||
" all 128 929 0.774 0.647 0.746 0.499\n",
|
||||
" person 128 254 0.87 0.697 0.806 0.534\n",
|
||||
" bicycle 128 6 0.759 0.528 0.725 0.444\n",
|
||||
" car 128 46 0.774 0.413 0.554 0.239\n",
|
||||
" motorcycle 128 5 0.791 1 0.962 0.595\n",
|
||||
" airplane 128 6 0.981 1 0.995 0.689\n",
|
||||
" bus 128 7 0.65 0.714 0.755 0.691\n",
|
||||
" train 128 3 1 0.573 0.995 0.602\n",
|
||||
" truck 128 12 0.613 0.333 0.489 0.263\n",
|
||||
" boat 128 6 0.933 0.333 0.507 0.209\n",
|
||||
" traffic light 128 14 0.76 0.228 0.367 0.209\n",
|
||||
" stop sign 128 2 0.821 1 0.995 0.821\n",
|
||||
" bench 128 9 0.824 0.526 0.676 0.31\n",
|
||||
" bird 128 16 0.974 1 0.995 0.611\n",
|
||||
" cat 128 4 0.859 1 0.995 0.772\n",
|
||||
" dog 128 9 1 0.666 0.883 0.647\n",
|
||||
" horse 128 2 0.84 1 0.995 0.622\n",
|
||||
" elephant 128 17 0.926 0.882 0.93 0.716\n",
|
||||
" bear 128 1 0.709 1 0.995 0.995\n",
|
||||
" zebra 128 4 0.866 1 0.995 0.922\n",
|
||||
" giraffe 128 9 0.777 0.778 0.891 0.705\n",
|
||||
" backpack 128 6 0.894 0.5 0.753 0.294\n",
|
||||
" umbrella 128 18 0.876 0.783 0.899 0.54\n",
|
||||
" handbag 128 19 0.799 0.209 0.335 0.179\n",
|
||||
" tie 128 7 0.782 0.714 0.787 0.478\n",
|
||||
" suitcase 128 4 0.658 1 0.945 0.581\n",
|
||||
" frisbee 128 5 0.726 0.8 0.76 0.701\n",
|
||||
" skis 128 1 0.8 1 0.995 0.103\n",
|
||||
" snowboard 128 7 0.815 0.714 0.852 0.574\n",
|
||||
" sports ball 128 6 0.649 0.667 0.602 0.307\n",
|
||||
" kite 128 10 0.7 0.47 0.546 0.206\n",
|
||||
" baseball bat 128 4 1 0.497 0.544 0.182\n",
|
||||
" baseball glove 128 7 0.598 0.429 0.47 0.31\n",
|
||||
" skateboard 128 5 0.851 0.6 0.685 0.495\n",
|
||||
" tennis racket 128 7 0.754 0.429 0.544 0.34\n",
|
||||
" bottle 128 18 0.564 0.333 0.53 0.264\n",
|
||||
" wine glass 128 16 0.715 0.875 0.907 0.528\n",
|
||||
" cup 128 36 0.825 0.639 0.803 0.535\n",
|
||||
" fork 128 6 1 0.329 0.5 0.384\n",
|
||||
" knife 128 16 0.706 0.625 0.666 0.405\n",
|
||||
" spoon 128 22 0.836 0.464 0.619 0.379\n",
|
||||
" bowl 128 28 0.763 0.607 0.717 0.516\n",
|
||||
" banana 128 1 0.886 1 0.995 0.399\n",
|
||||
" sandwich 128 2 1 0 0.62 0.546\n",
|
||||
" orange 128 4 1 0.75 0.995 0.622\n",
|
||||
" broccoli 128 11 0.548 0.443 0.467 0.35\n",
|
||||
" carrot 128 24 0.7 0.585 0.699 0.458\n",
|
||||
" hot dog 128 2 0.502 1 0.995 0.995\n",
|
||||
" pizza 128 5 0.813 1 0.962 0.747\n",
|
||||
" donut 128 14 0.662 1 0.96 0.838\n",
|
||||
" cake 128 4 0.868 1 0.995 0.822\n",
|
||||
" chair 128 35 0.538 0.571 0.594 0.322\n",
|
||||
" couch 128 6 0.924 0.667 0.828 0.538\n",
|
||||
" potted plant 128 14 0.731 0.786 0.824 0.495\n",
|
||||
" bed 128 3 0.736 0.333 0.83 0.425\n",
|
||||
" dining table 128 13 0.624 0.259 0.494 0.336\n",
|
||||
" toilet 128 2 0.79 1 0.995 0.846\n",
|
||||
" tv 128 2 0.574 1 0.995 0.796\n",
|
||||
" laptop 128 3 1 0 0.695 0.367\n",
|
||||
" mouse 128 2 1 0 0.173 0.0864\n",
|
||||
" remote 128 8 1 0.62 0.634 0.557\n",
|
||||
" cell phone 128 8 0.612 0.397 0.437 0.221\n",
|
||||
" microwave 128 3 0.741 1 0.995 0.766\n",
|
||||
" oven 128 5 0.33 0.4 0.449 0.3\n",
|
||||
" sink 128 6 0.444 0.333 0.331 0.231\n",
|
||||
" refrigerator 128 5 0.561 0.8 0.798 0.546\n",
|
||||
" book 128 29 0.635 0.276 0.355 0.164\n",
|
||||
" clock 128 9 0.766 0.889 0.888 0.73\n",
|
||||
" vase 128 2 0.303 1 0.995 0.895\n",
|
||||
" scissors 128 1 1 0 0.332 0.0397\n",
|
||||
" teddy bear 128 21 0.842 0.508 0.739 0.499\n",
|
||||
" toothbrush 128 5 0.787 1 0.928 0.59\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, 1.04it/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",
|
||||
" 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",
|
||||
" 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",
|
||||
"Results saved to \u001b[1mruns/train/exp\u001b[0m\n"
|
||||
]
|
||||
}
|
||||
|
|
@ -975,4 +973,4 @@
|
|||
"outputs": []
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
Loading…
Reference in New Issue