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pull/10292/merge
Glenn Jocher 2022-11-22 20:47:54 +01:00
parent c9d47ae056
commit 61ebf5e5ed
1 changed files with 31 additions and 31 deletions

62
segment/tutorial.ipynb vendored
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@ -42,14 +42,14 @@
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"YOLOv5 🚀 v6.2-257-g2ecaa96 Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n"
"YOLOv5 🚀 v7.0-2-gc9d47ae Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n"
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@ -108,16 +108,16 @@
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"text": [
"\u001b[34m\u001b[1msegment/predict: \u001b[0mweights=['yolov5s-seg.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/predict-seg, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1, retina_masks=False\n",
"YOLOv5 🚀 v6.2-257-g2ecaa96 Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n",
"YOLOv5 🚀 v7.0-2-gc9d47ae Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n",
"\n",
"Downloading https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5s-seg.pt to yolov5s-seg.pt...\n",
"100% 14.9M/14.9M [00:01<00:00, 9.09MB/s]\n",
"Downloading https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-seg.pt to yolov5s-seg.pt...\n",
"100% 14.9M/14.9M [00:01<00:00, 12.0MB/s]\n",
"\n",
"Fusing layers... \n",
"YOLOv5s-seg summary: 224 layers, 7611485 parameters, 0 gradients, 26.4 GFLOPs\n",
"image 1/2 /content/yolov5/data/images/bus.jpg: 640x480 4 persons, 1 bus, 18.0ms\n",
"image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 1 tie, 13.5ms\n",
"Speed: 0.5ms pre-process, 15.7ms inference, 18.5ms NMS per image at shape (1, 3, 640, 640)\n",
"image 1/2 /content/yolov5/data/images/bus.jpg: 640x480 4 persons, 1 bus, 18.2ms\n",
"image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 1 tie, 13.4ms\n",
"Speed: 0.5ms pre-process, 15.8ms inference, 18.5ms NMS per image at shape (1, 3, 640, 640)\n",
"Results saved to \u001b[1mruns/predict-seg/exp\u001b[0m\n"
]
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@ -155,7 +155,7 @@
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@ -182,7 +182,7 @@
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@ -190,15 +190,15 @@
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"text": [
"\u001b[34m\u001b[1msegment/val: \u001b[0mdata=/content/yolov5/data/coco.yaml, weights=['yolov5s-seg.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=False, project=runs/val-seg, name=exp, exist_ok=False, half=True, dnn=False\n",
"YOLOv5 🚀 v6.2-257-g2ecaa96 Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n",
"YOLOv5 🚀 v7.0-2-gc9d47ae Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n",
"\n",
"Fusing layers... \n",
"YOLOv5s-seg summary: 224 layers, 7611485 parameters, 0 gradients, 26.4 GFLOPs\n",
"\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/coco/val2017... 4952 images, 48 backgrounds, 0 corrupt: 100% 5000/5000 [00:03<00:00, 1409.04it/s]\n",
"\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/coco/val2017... 4952 images, 48 backgrounds, 0 corrupt: 100% 5000/5000 [00:03<00:00, 1361.31it/s]\n",
"\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/datasets/coco/val2017.cache\n",
" Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 157/157 [01:53<00:00, 1.38it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 157/157 [01:54<00:00, 1.37it/s]\n",
" all 5000 36335 0.673 0.517 0.566 0.373 0.672 0.49 0.532 0.319\n",
"Speed: 0.8ms pre-process, 4.0ms inference, 2.8ms NMS per image at shape (32, 3, 640, 640)\n",
"Speed: 0.6ms pre-process, 4.4ms inference, 2.9ms NMS per image at shape (32, 3, 640, 640)\n",
"Results saved to \u001b[1mruns/val-seg/exp\u001b[0m\n"
]
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@ -270,7 +270,7 @@
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@ -279,15 +279,15 @@
"text": [
"\u001b[34m\u001b[1msegment/train: \u001b[0mweights=yolov5s-seg.pt, cfg=, data=coco128-seg.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-seg, 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, mask_ratio=4, no_overlap=False\n",
"\u001b[34m\u001b[1mgithub: \u001b[0mup to date with https://github.com/ultralytics/yolov5 ✅\n",
"YOLOv5 🚀 v6.2-257-g2ecaa96 Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n",
"YOLOv5 🚀 v7.0-2-gc9d47ae 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[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train-seg', view at http://localhost:6006/\n",
"\n",
"Dataset not found ⚠️, missing paths ['/content/datasets/coco128-seg/images/train2017']\n",
"Downloading https://ultralytics.com/assets/coco128-seg.zip to coco128-seg.zip...\n",
"100% 6.79M/6.79M [00:01<00:00, 5.87MB/s]\n",
"Dataset download success ✅ (2.1s), saved to \u001b[1m/content/datasets\u001b[0m\n",
"100% 6.79M/6.79M [00:01<00:00, 6.73MB/s]\n",
"Dataset download success ✅ (1.9s), 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",
@ -321,11 +321,11 @@
"\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
"\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 60 weight(decay=0.0), 63 weight(decay=0.0005), 63 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-seg/labels/train2017... 126 images, 2 backgrounds, 0 corrupt: 100% 128/128 [00:00<00:00, 1439.54it/s]\n",
"\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/datasets/coco128-seg/labels/train2017... 126 images, 2 backgrounds, 0 corrupt: 100% 128/128 [00:00<00:00, 1389.59it/s]\n",
"\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/datasets/coco128-seg/labels/train2017.cache\n",
"\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (0.1GB ram): 100% 128/128 [00:00<00:00, 253.53it/s]\n",
"\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (0.1GB ram): 100% 128/128 [00:00<00:00, 238.86it/s]\n",
"\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/coco128-seg/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, 93.82it/s]\n",
"\u001b[34m\u001b[1mval: \u001b[0mCaching images (0.1GB ram): 100% 128/128 [00:01<00:00, 98.90it/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-seg/exp/labels.jpg... \n",
@ -335,28 +335,28 @@
"Starting training for 3 epochs...\n",
"\n",
" Epoch GPU_mem box_loss seg_loss obj_loss cls_loss Instances Size\n",
" 0/2 4.92G 0.0417 0.04646 0.06066 0.02126 192 640: 100% 8/8 [00:07<00:00, 1.11it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 4/4 [00:02<00:00, 1.85it/s]\n",
" 0/2 4.92G 0.0417 0.04646 0.06066 0.02126 192 640: 100% 8/8 [00:08<00:00, 1.10s/it]\n",
" Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 4/4 [00:02<00:00, 1.81it/s]\n",
" all 128 929 0.737 0.649 0.715 0.492 0.719 0.617 0.658 0.408\n",
"\n",
" Epoch GPU_mem box_loss seg_loss obj_loss cls_loss Instances Size\n",
" 1/2 6.29G 0.04157 0.04503 0.05772 0.01777 208 640: 100% 8/8 [00:09<00:00, 1.19s/it]\n",
" Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 4/4 [00:02<00:00, 1.75it/s]\n",
" 1/2 6.29G 0.04157 0.04503 0.05772 0.01777 208 640: 100% 8/8 [00:09<00:00, 1.21s/it]\n",
" Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 4/4 [00:02<00:00, 1.87it/s]\n",
" all 128 929 0.756 0.674 0.738 0.506 0.725 0.64 0.68 0.422\n",
"\n",
" Epoch GPU_mem box_loss seg_loss obj_loss cls_loss Instances Size\n",
" 2/2 6.29G 0.0425 0.04793 0.06784 0.01863 161 640: 100% 8/8 [00:04<00:00, 1.99it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 4/4 [00:02<00:00, 1.81it/s]\n",
" 2/2 6.29G 0.0425 0.04793 0.06784 0.01863 161 640: 100% 8/8 [00:03<00:00, 2.02it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 4/4 [00:02<00:00, 1.88it/s]\n",
" all 128 929 0.736 0.694 0.747 0.522 0.769 0.622 0.683 0.427\n",
"\n",
"3 epochs completed in 0.008 hours.\n",
"3 epochs completed in 0.009 hours.\n",
"Optimizer stripped from runs/train-seg/exp/weights/last.pt, 15.6MB\n",
"Optimizer stripped from runs/train-seg/exp/weights/best.pt, 15.6MB\n",
"\n",
"Validating runs/train-seg/exp/weights/best.pt...\n",
"Fusing layers... \n",
"Model summary: 165 layers, 7611485 parameters, 0 gradients, 26.4 GFLOPs\n",
" Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 4/4 [00:06<00:00, 1.58s/it]\n",
" Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 4/4 [00:06<00:00, 1.59s/it]\n",
" all 128 929 0.738 0.694 0.746 0.522 0.759 0.625 0.682 0.426\n",
" person 128 254 0.845 0.756 0.836 0.55 0.861 0.669 0.759 0.407\n",
" bicycle 128 6 0.475 0.333 0.549 0.341 0.711 0.333 0.526 0.322\n",
@ -590,4 +590,4 @@
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