14373 lines
48 MiB
Plaintext
14373 lines
48 MiB
Plaintext
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[34m[1mtrain: [0mweights=./weights/yolov5s.pt, cfg=./models/yolov5s.yaml, data=./data/forest.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=110, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=None, 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, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest
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/bin/sh: 1: git: not found
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YOLOv5 🚀 2022-7-12 Python-3.9.12 torch-1.7.1+cu110 CUDA:0 (GeForce RTX 2080 Ti, 11017MiB)
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[34m[1mhyperparameters: [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
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[34m[1mWeights & Biases: [0mrun 'pip install wandb' to automatically track and visualize YOLOv5 🚀 runs (RECOMMENDED)
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[34m[1mTensorBoard: [0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/
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Overriding model.yaml nc=80 with nc=2
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from n params module arguments
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0 -1 1 3520 models.common.Conv [3, 32, 6, 2, 2]
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1 -1 1 18560 models.common.Conv [32, 64, 3, 2]
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2 -1 1 18816 models.common.C3 [64, 64, 1]
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3 -1 1 73984 models.common.Conv [64, 128, 3, 2]
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4 -1 2 115712 models.common.C3 [128, 128, 2]
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5 -1 1 295424 models.common.Conv [128, 256, 3, 2]
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6 -1 3 625152 models.common.C3 [256, 256, 3]
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7 -1 1 1180672 models.common.Conv [256, 512, 3, 2]
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8 -1 1 1182720 models.common.C3 [512, 512, 1]
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9 -1 1 656896 models.common.SPPF [512, 512, 5]
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10 -1 1 131584 models.common.Conv [512, 256, 1, 1]
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11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
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12 [-1, 6] 1 0 models.common.Concat [1]
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13 -1 1 361984 models.common.C3 [512, 256, 1, False]
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14 -1 1 33024 models.common.Conv [256, 128, 1, 1]
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15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
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16 [-1, 4] 1 0 models.common.Concat [1]
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17 -1 1 90880 models.common.C3 [256, 128, 1, False]
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18 -1 1 147712 models.common.Conv [128, 128, 3, 2]
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19 [-1, 14] 1 0 models.common.Concat [1]
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20 -1 1 296448 models.common.C3 [256, 256, 1, False]
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21 -1 1 590336 models.common.Conv [256, 256, 3, 2]
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22 [-1, 10] 1 0 models.common.Concat [1]
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23 -1 1 1182720 models.common.C3 [512, 512, 1, False]
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24 [17, 20, 23] 1 18879 models.yolo.Detect [2, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]
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YOLOv5s summary: 270 layers, 7025023 parameters, 7025023 gradients, 16.0 GFLOPs
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Transferred 342/349 items from weights/yolov5s.pt
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[34m[1mAMP: [0mchecks passed ✅
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Scaled weight_decay = 0.0005
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[34m[1moptimizer:[0m SGD with parameter groups 57 weight (no decay), 60 weight, 60 bias
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WARNING: DP not recommended, use torch.distributed.run for best DDP Multi-GPU results.
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See Multi-GPU Tutorial at https://github.com/ultralytics/yolov5/issues/475 to get started.
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[34m[1mgithub: [0mskipping check (Docker image), for updates see https://github.com/ultralytics/yolov5
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[34m[1mtrain: [0mScanning '/host/zjc/yolov5/VOCdevkit/labels/train.cache' images and labels... 890 found, 0 missing, 1 empty, 0 corrupt: 100%|██████████| 890/890 [00:00<?, ?it/s]
[34m[1mtrain: [0mScanning '/host/zjc/yolov5/VOCdevkit/labels/train.cache' images and labels... 890 found, 0 missing, 1 empty, 0 corrupt: 100%|██████████| 890/890 [00:00<?, ?it/s]
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[34m[1mval: [0mScanning '/host/zjc/yolov5/VOCdevkit/labels/val.cache' images and labels... 98 found, 0 missing, 0 empty, 0 corrupt: 100%|██████████| 98/98 [00:00<?, ?it/s]
[34m[1mval: [0mScanning '/host/zjc/yolov5/VOCdevkit/labels/val.cache' images and labels... 98 found, 0 missing, 0 empty, 0 corrupt: 100%|██████████| 98/98 [00:00<?, ?it/s]
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Plotting labels to runs/train/exp18/labels.jpg...
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[34m[1mAutoAnchor: [0m3.68 anchors/target, 0.993 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅
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Image sizes 640 train, 640 val
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Using 8 dataloader workers
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Logging results to [1mruns/train/exp18[0m
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Starting training for 110 epochs...
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Epoch gpu_mem box obj cls labels img_size
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0%| | 0/56 [00:00<?, ?it/s]
0/109 1.89G 0.1243 0.04066 0.03015 122 640: 0%| | 0/56 [00:51<?, ?it/s]
0/109 1.89G 0.1243 0.04066 0.03015 122 640: 2%|▏ | 1/56 [00:53<48:43, 53.15s/it]
0/109 1.96G 0.1257 0.03987 0.03029 138 640: 2%|▏ | 1/56 [00:53<48:43, 53.15s/it]
0/109 1.96G 0.1257 0.03987 0.03029 138 640: 4%|▎ | 2/56 [00:53<19:53, 22.10s/it]
0/109 2.11G 0.1257 0.04 0.03013 164 640: 4%|▎ | 2/56 [00:53<19:53, 22.10s/it]
0/109 2.11G 0.1257 0.04 0.03013 164 640: 5%|▌ | 3/56 [00:53<10:43, 12.14s/it]
0/109 2.11G 0.1267 0.03967 0.03041 201 640: 5%|▌ | 3/56 [00:54<10:43, 12.14s/it]
0/109 2.11G 0.1267 0.03967 0.03041 201 640: 7%|▋ | 4/56 [00:54<06:28, 7.46s/it]
0/109 2.11G 0.1264 0.03946 0.03032 161 640: 7%|▋ | 4/56 [00:54<06:28, 7.46s/it]
0/109 2.11G 0.1264 0.03946 0.03032 161 640: 9%|▉ | 5/56 [00:54<04:06, 4.83s/it]
0/109 2.11G 0.1268 0.03903 0.03031 170 640: 9%|▉ | 5/56 [00:54<04:06, 4.83s/it]
0/109 2.11G 0.1268 0.03903 0.03031 170 640: 11%|█ | 6/56 [00:54<02:41, 3.23s/it]
0/109 2.11G 0.1268 0.03887 0.03028 142 640: 11%|█ | 6/56 [00:54<02:41, 3.23s/it]
0/109 2.11G 0.1268 0.03887 0.03028 142 640: 12%|█▎ | 7/56 [00:54<01:49, 2.23s/it]
0/109 2.11G 0.1264 0.03898 0.03026 143 640: 12%|█▎ | 7/56 [00:54<01:49, 2.23s/it]
0/109 2.11G 0.1264 0.03898 0.03026 143 640: 14%|█▍ | 8/56 [00:54<01:16, 1.59s/it]
0/109 2.11G 0.1263 0.03863 0.03019 119 640: 14%|█▍ | 8/56 [01:28<01:16, 1.59s/it]
0/109 2.11G 0.1263 0.03863 0.03019 119 640: 16%|█▌ | 9/56 [01:28<09:02, 11.55s/it]
0/109 2.11G 0.1261 0.03845 0.03008 125 640: 16%|█▌ | 9/56 [01:28<09:02, 11.55s/it]
0/109 2.11G 0.1261 0.03845 0.03008 125 640: 18%|█▊ | 10/56 [01:28<06:09, 8.03s/it]
0/109 2.11G 0.1259 0.03848 0.02998 158 640: 18%|█▊ | 10/56 [01:28<06:09, 8.03s/it]
0/109 2.11G 0.1259 0.03848 0.02998 158 640: 20%|█▉ | 11/56 [01:28<04:12, 5.62s/it]
0/109 2.11G 0.1256 0.03827 0.0299 121 640: 20%|█▉ | 11/56 [01:28<04:12, 5.62s/it]
0/109 2.11G 0.1256 0.03827 0.0299 121 640: 21%|██▏ | 12/56 [01:28<02:53, 3.95s/it]
0/109 2.11G 0.1254 0.03838 0.02979 165 640: 21%|██▏ | 12/56 [01:28<02:53, 3.95s/it]
0/109 2.11G 0.1254 0.03838 0.02979 165 640: 23%|██▎ | 13/56 [01:28<02:00, 2.80s/it]
0/109 2.11G 0.1253 0.0381 0.02968 144 640: 23%|██▎ | 13/56 [01:28<02:00, 2.80s/it]
0/109 2.11G 0.1253 0.0381 0.02968 144 640: 25%|██▌ | 14/56 [01:28<01:23, 2.00s/it]
0/109 2.11G 0.125 0.03796 0.02958 112 640: 25%|██▌ | 14/56 [01:29<01:23, 2.00s/it]
0/109 2.11G 0.125 0.03796 0.02958 112 640: 27%|██▋ | 15/56 [01:29<00:58, 1.44s/it]
0/109 2.11G 0.125 0.03768 0.02953 137 640: 27%|██▋ | 15/56 [01:29<00:58, 1.44s/it]
0/109 2.11G 0.125 0.03768 0.02953 137 640: 29%|██▊ | 16/56 [01:29<00:41, 1.05s/it]
0/109 2.11G 0.1247 0.0374 0.02942 135 640: 29%|██▊ | 16/56 [02:02<00:41, 1.05s/it
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Class Images Labels P R mAP@.5 mAP@.5:.95: 0%| | 0/4 [00:00<?, ?it/s]
Class Images Labels P R mAP@.5 mAP@.5:.95: 25%|██▌ | 1/4 [00:00<00:00, 5.48it/s]
Class Images Labels P R mAP@.5 mAP@.5:.95: 50%|█████ | 2/4 [00:00<00:00, 5.24it/s]
Class Images Labels P R mAP@.5 mAP@.5:.95: 75%|███████▌ | 3/4 [00:00<00:00, 5.17it/s]
Class Images Labels P R mAP@.5 mAP@.5:.95: 100%|██████████| 4/4 [00:00<00:00, 6.50it/s]
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all 98 501 0.00109 0.175 0.00424 0.0011
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Epoch gpu_mem box obj cls labels img_size
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0%| | 0/56 [00:00<?, ?it/s]
1/109 2.65G 0.1014 0.03468 0.01729 116 640: 0%| | 0/56 [00:40<?, ?it/s]
1/109 2.65G 0.1014 0.03468 0.01729 116 640: 2%|▏ | 1/56 [00:40<37:01, 40.39s/it]
1/109 2.65G 0.1023 0.03189 0.01843 86 640: 2%|▏ | 1/56 [00:40<37:01, 40.39s/it]
1/109 2.65G 0.1023 0.03189 0.01843 86 640: 4%|▎ | 2/56 [00:40<15:03, 16.72s/it]
1/109 2.65G 0.1034 0.03463 0.01853 206 640: 4%|▎ | 2/56 [00:40<15:03, 16.72s/it]
1/109 2.65G 0.1034 0.03463 0.01853 206 640: 5%|▌ | 3/56 [00:40<08:04, 9.14s/it]
1/109 2.65G 0.1025 0.03791 0.01854 151 640: 5%|▌ | 3/56 [00:40<08:04, 9.14s/it]
1/109 2.65G 0.1025 0.03791 0.01854 151 640: 7%|▋ | 4/56 [00:40<04:50, 5.59s/it]
1/109 2.65G 0.1023 0.03788 0.01877 115 640: 7%|▋ | 4/56 [00:40<04:50, 5.59s/it]
1/109 2.65G 0.1023 0.03788 0.01877 115 640: 9%|▉ | 5/56 [00:40<03:04, 3.62s/it]
1/109 2.65G 0.1028 0.03839 0.01854 143 640: 9%|▉ | 5/56 [00:41<03:04, 3.62s/it]
1/109 2.65G 0.1028 0.03839 0.01854 143 640: 11%|█ | 6/56 [00:41<02:01, 2.44s/it]
1/109 2.65G 0.102 0.03835 0.01834 116 640: 11%|█ | 6/56 [00:41<02:01, 2.44s/it]
1/109 2.65G 0.102 0.03835 0.01834 116 640: 12%|█▎ | 7/56 [00:41<01:22, 1.68s/it]
1/109 2.65G 0.1015 0.03792 0.01839 103 640: 12%|█▎ | 7/56 [00:41<01:22, 1.68s/it]
1/109 2.65G 0.1015 0.03792 0.01839 103 640: 14%|█▍ | 8/56 [00:41<00:57, 1.19s/it]
1/109 2.65G 0.1013 0.03826 0.01824 136 640: 14%|█▍ | 8/56 [01:14<00:57, 1.19s/it]
1/109 2.65G 0.1013 0.03826 0.01824 136 640: 16%|█▌ | 9/56 [01:14<08:52, 11.34s/it]
1/109 2.65G 0.1014 0.03824 0.01802 176 640: 16%|█▌ | 9/56 [01:18<08:52, 11.34s/it]
1/109 2.65G 0.1014 0.03824 0.01802 176 640: 18%|█▊ | 10/56 [01:18<06:50, 8.92s/it]
1/109 2.65G 0.1013 0.03841 0.01795 152 640: 18%|█▊ | 10/56 [01:18<06:50, 8.92s/it]
1/109 2.65G 0.1013 0.03841 0.01795 152 640: 20%|█▉ | 11/56 [01:18<04:40, 6.23s/it]
1/109 2.65G 0.1016 0.03896 0.01786 166 640: 20%|█▉ | 11/56 [01:18<04:40, 6.23s/it]
1/109 2.65G 0.1016 0.03896 0.01786 166 640: 21%|██▏ | 12/56 [01:18<03:12, 4.38s/it]
1/109 2.65G 0.1018 0.03907 0.0177 156 640: 21%|██▏ | 12/56 [01:21<03:12, 4.38s/it]
1/109 2.65G 0.1018 0.03907 0.0177 156 640: 23%|██▎ | 13/56 [01:21<02:44, 3.82s/it]
1/109 2.65G 0.1014 0.03893 0.01749 117 640: 23%|██▎ | 13/56 [01:21<02:44, 3.82s/it]
1/109 2.65G 0.1014 0.03893 0.01749 117 640: 25%|██▌ | 14/56 [01:21<01:54, 2.72s/it]
1/109 2.65G 0.1013 0.03848 0.01744 132 640: 25%|██▌ | 14/56 [01:21<01:54, 2.72s/it]
1/109 2.65G 0.1013 0.03848 0.01744 132 640: 27%|██▋ | 15/56 [01:21<01:19, 1.93s/it]
1/109 2.65G 0.1009 0.03828 0.01739 109 640: 27%|██▋ | 15/56 [01:21<01:19, 1.93s/it]
1/109 2.65G 0.1009 0.03828 0.01739 109 640: 29%|██▊ | 16/56 [01:21<00:55, 1.40s/it]
1/109 2.65G 0.1007 0.03836 0.01739 123 640: 29%|██▊ | 16/56 [01:49<00:55, 1.40s/it
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Traceback (most recent call last):
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File "/host/zjc/yolov5/train.py", line 666, in <module>
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main(opt)
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File "/host/zjc/yolov5/train.py", line 561, in main
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train(opt.hyp, opt, device, callbacks)
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File "/host/zjc/yolov5/train.py", line 326, in train
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for i, (imgs, targets, paths, _) in pbar: # batch -------------------------------------------------------------
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File "/opt/conda/envs/torch1.7/lib/python3.9/site-packages/tqdm/std.py", line 1195, in __iter__
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for obj in iterable:
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File "/host/zjc/yolov5/utils/dataloaders.py", line 158, in __iter__
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yield next(self.iterator)
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File "/opt/conda/envs/torch1.7/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 435, in __next__
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data = self._next_data()
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File "/opt/conda/envs/torch1.7/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 1068, in _next_data
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idx, data = self._get_data()
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File "/opt/conda/envs/torch1.7/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 1024, in _get_data
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success, data = self._try_get_data()
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File "/opt/conda/envs/torch1.7/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 872, in _try_get_data
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data = self._data_queue.get(timeout=timeout)
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File "/opt/conda/envs/torch1.7/lib/python3.9/queue.py", line 180, in get
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self.not_empty.wait(remaining)
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File "/opt/conda/envs/torch1.7/lib/python3.9/threading.py", line 316, in wait
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gotit = waiter.acquire(True, timeout)
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KeyboardInterrupt
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[34m[1mtrain: [0mweights=./weights/yolov5s.pt, cfg=./models/yolov5s.yaml, data=./data/forest.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=100, batch_size=32, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=None, 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, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest
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/bin/sh: 1: git: not found
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YOLOv5 🚀 2022-7-12 Python-3.9.12 torch-1.7.1+cu110 CUDA:0 (GeForce RTX 2080 Ti, 11017MiB)
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[34m[1mhyperparameters: [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
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[34m[1mWeights & Biases: [0mrun 'pip install wandb' to automatically track and visualize YOLOv5 🚀 runs (RECOMMENDED)
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[34m[1mTensorBoard: [0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/
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Overriding model.yaml nc=80 with nc=2
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from n params module arguments
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0 -1 1 3520 models.common.Conv [3, 32, 6, 2, 2]
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1 -1 1 18560 models.common.Conv [32, 64, 3, 2]
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2 -1 1 18816 models.common.C3 [64, 64, 1]
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3 -1 1 73984 models.common.Conv [64, 128, 3, 2]
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4 -1 2 115712 models.common.C3 [128, 128, 2]
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5 -1 1 295424 models.common.Conv [128, 256, 3, 2]
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6 -1 3 625152 models.common.C3 [256, 256, 3]
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7 -1 1 1180672 models.common.Conv [256, 512, 3, 2]
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8 -1 1 1182720 models.common.C3 [512, 512, 1]
|
|||
|
|
9 -1 1 656896 models.common.SPPF [512, 512, 5]
|
|||
|
|
10 -1 1 131584 models.common.Conv [512, 256, 1, 1]
|
|||
|
|
11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
|
|||
|
|
12 [-1, 6] 1 0 models.common.Concat [1]
|
|||
|
|
13 -1 1 361984 models.common.C3 [512, 256, 1, False]
|
|||
|
|
14 -1 1 33024 models.common.Conv [256, 128, 1, 1]
|
|||
|
|
15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
|
|||
|
|
16 [-1, 4] 1 0 models.common.Concat [1]
|
|||
|
|
17 -1 1 90880 models.common.C3 [256, 128, 1, False]
|
|||
|
|
18 -1 1 147712 models.common.Conv [128, 128, 3, 2]
|
|||
|
|
19 [-1, 14] 1 0 models.common.Concat [1]
|
|||
|
|
20 -1 1 296448 models.common.C3 [256, 256, 1, False]
|
|||
|
|
21 -1 1 590336 models.common.Conv [256, 256, 3, 2]
|
|||
|
|
22 [-1, 10] 1 0 models.common.Concat [1]
|
|||
|
|
23 -1 1 1182720 models.common.C3 [512, 512, 1, False]
|
|||
|
|
24 [17, 20, 23] 1 18879 models.yolo.Detect [2, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]
|
|||
|
|
YOLOv5s summary: 270 layers, 7025023 parameters, 7025023 gradients, 16.0 GFLOPs
|
|||
|
|
|
|||
|
|
Transferred 342/349 items from weights/yolov5s.pt
|
|||
|
|
[34m[1mAMP: [0mchecks passed ✅
|
|||
|
|
Scaled weight_decay = 0.0005
|
|||
|
|
[34m[1moptimizer:[0m SGD with parameter groups 57 weight (no decay), 60 weight, 60 bias
|
|||
|
|
WARNING: DP not recommended, use torch.distributed.run for best DDP Multi-GPU results.
|
|||
|
|
See Multi-GPU Tutorial at https://github.com/ultralytics/yolov5/issues/475 to get started.
|
|||
|
|
[34m[1mgithub: [0mskipping check (Docker image), for updates see https://github.com/ultralytics/yolov5
|
|||
|
|
[34m[1mtrain: [0mScanning '/host/zjc/yolov5/VOCdevkit/labels/train.cache' images and labels... 890 found, 0 missing, 1 empty, 0 corrupt: 100%|██████████| 890/890 [00:00<?, ?it/s]
[34m[1mtrain: [0mScanning '/host/zjc/yolov5/VOCdevkit/labels/train.cache' images and labels... 890 found, 0 missing, 1 empty, 0 corrupt: 100%|██████████| 890/890 [00:00<?, ?it/s]
|
|||
|
|
[34m[1mval: [0mScanning '/host/zjc/yolov5/VOCdevkit/labels/val.cache' images and labels... 98 found, 0 missing, 0 empty, 0 corrupt: 100%|██████████| 98/98 [00:00<?, ?it/s]
[34m[1mval: [0mScanning '/host/zjc/yolov5/VOCdevkit/labels/val.cache' images and labels... 98 found, 0 missing, 0 empty, 0 corrupt: 100%|██████████| 98/98 [00:00<?, ?it/s]
|
|||
|
|
Plotting labels to runs/train/exp19/labels.jpg...
|
|||
|
|
|
|||
|
|
[34m[1mAutoAnchor: [0m3.68 anchors/target, 0.993 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅
|
|||
|
|
Image sizes 640 train, 640 val
|
|||
|
|
Using 8 dataloader workers
|
|||
|
|
Logging results to [1mruns/train/exp19[0m
|
|||
|
|
Starting training for 100 epochs...
|
|||
|
|
|
|||
|
|
Epoch gpu_mem box obj cls labels img_size
|
|||
|
|
0%| | 0/28 [00:00<?, ?it/s]
0/99 3.75G 0.1267 0.03905 0.0302 252 640: 0%| | 0/28 [01:45<?, ?it/s]
0/99 3.75G 0.1267 0.03905 0.0302 252 640: 4%|▎ | 1/28 [01:46<47:57, 106.57s/it]
0/99 3.82G 0.1261 0.03978 0.03018 279 640: 4%|▎ | 1/28 [01:47<47:57, 106.57s/it]
0/99 3.82G 0.1261 0.03978 0.03018 279 640: 7%|▋ | 2/28 [01:47<19:22, 44.72s/it]
0/99 3.82G 0.1261 0.03956 0.03017 318 640: 7%|▋ | 2/28 [01:48<19:22, 44.72s/it]
0/99 3.82G 0.1261 0.03956 0.03017 318 640: 11%|█ | 3/28 [01:48<10:10, 24.44s/it]
0/99 3.82G 0.1263 0.03946 0.03026 336 640: 11%|█ | 3/28 [01:48<10:10, 24.44s/it]
0/99 3.82G 0.1263 0.03946 0.03026 336 640: 14%|█▍ | 4/28 [01:48<05:58, 14.92s/it]
0/99 3.82G 0.126 0.03939 0.03019 312 640: 14%|█▍ | 4/28 [01:48<05:58, 14.92s/it]
0/99 3.82G 0.126 0.03939 0.03019 312 640: 18%|█▊ | 5/28 [01:48<03:41, 9.61s/it]
0/99 3.82G 0.1262 0.03894 0.03004 338 640: 18%|█▊ | 5/28 [01:49<03:41, 9.61s/it]
0/99 3.82G 0.1262 0.03894 0.03004 338 640: 21%|██▏ | 6/28 [01:49<02:21, 6.41s/it]
0/99 3.82G 0.1258 0.03844 0.02992 240 640: 21%|██▏ | 6/28 [01:49<02:21, 6.41s/it]
0/99 3.82G 0.1258 0.03844 0.02992 240 640: 25%|██▌ | 7/28 [01:49<01:32, 4.38s/it]
0/99 3.82G 0.1255 0.03816 0.02979 269 640: 25%|██▌ | 7/28 [01:49<01:32, 4.38s/it]
0/99 3.82G 0.1255 0.03816 0.02979 269 640: 29%|██▊ | 8/28 [01:49<01:01, 3.05s/it]
0/99 3.82G 0.1253 0.03783 0.02967 264 640: 29%|██▊ | 8/28 [02:58<01:01, 3.05s/it]
0/99 3.82G 0.1253 0.03783 0.02967 264 640: 32%|███▏ | 9/28 [02:58<07:30, 23.70s/it]
0/99 3.82G 0.125 0.03756 0.02956 289 640: 32%|███▏ | 9/28 [03:02<07:30, 23.70s/it]
0/99 3.82G 0.125 0.03756 0.02956 289 640: 36%|███▌ | 10/28 [03:02<05:18, 17.67s/it]
0/99 3.82G 0.1246 0.03744 0.02942 305 640: 36%|███▌ | 10/28 [03:02<05:18, 17.67s/it]
0/99 3.82G 0.1246 0.03744 0.02942 305 640: 39%|███▉ | 11/28 [03:02<03:29, 12.33s/it]
0/99 3.82G 0.1243 0.03725 0.02926 310 640: 39%|███▉ | 11/28 [03:03<03:29, 12.33s/it]
0/99 3.82G 0.1243 0.03725 0.02926 310 640: 43%|████▎ | 12/28 [03:03<02:18, 8.64s/it]
0/99 3.82G 0.1237 0.03749 0.02911 295 640: 43%|████▎ | 12/28 [03:03<02:18, 8.64s/it]
0/99 3.82G 0.1237 0.03749 0.02911 295 640: 46%|████▋ | 13/28 [03:03<01:31, 6.08s/it]
0/99 3.82G 0.1232 0.03749 0.0289 307 640: 46%|████▋ | 13/28 [03:03<01:31, 6.08s/it]
0/99 3.82G 0.1232 0.03749 0.0289 307 640: 50%|█████ | 14/28 [03:03<01:00, 4.31s/it]
0/99 3.82G 0.1228 0.03728 0.02866 332 640: 50%|█████ | 14/28 [03:03<01:00, 4.31s/it]
0/99 3.82G 0.1228 0.03728 0.02866 332 640: 54%|█████▎ | 15/28 [03:03<00:39, 3.07s/it]
0/99 3.82G 0.1224 0.03707 0.02846 293 640: 54%|█████▎ | 15/28 [03:03<00:39, 3.07s/it]
0/99 3.82G 0.1224 0.03707 0.02846 293 640: 57%|█████▋ | 16/28 [03:03<00:26, 2.21s/it]
0/99 3.82G 0.1219
|
|||
|
|
Class Images Labels P R mAP@.5 mAP@.5:.95: 0%| | 0/2 [00:00<?, ?it/s]
Class Images Labels P R mAP@.5 mAP@.5:.95: 50%|█████ | 1/2 [00:00<00:00, 2.84it/s]
Class Images Labels P R mAP@.5 mAP@.5:.95: 100%|██████████| 2/2 [00:00<00:00, 3.85it/s]
Class Images Labels P R mAP@.5 mAP@.5:.95: 100%|██████████| 2/2 [00:00<00:00, 3.65it/s]
|
|||
|
|
all 98 501 0.00118 0.172 0.00368 0.00102
|
|||
|
|
|
|||
|
|
Epoch gpu_mem box obj cls labels img_size
|
|||
|
|
0%| | 0/28 [00:00<?, ?it/s]
1/99 4.9G 0.1056 0.0352 0.02019 223 640: 0%| | 0/28 [00:00<?, ?it/s]
1/99 4.9G 0.1056 0.0352 0.02019 223 640: 4%|▎ | 1/28 [00:00<00:05, 4.86it/s]
1/99 4.9G 0.1049 0.03591 0.01928 252 640: 4%|▎ | 1/28 [00:00<00:05, 4.86it/s]
1/99 4.9G 0.1049 0.03591 0.01928 252 640: 7%|▋ | 2/28 [00:00<00:05, 4.91it/s]
1/99 4.9G 0.1046 0.0379 0.01912 278 640: 7%|▋ | 2/28 [00:00<00:05, 4.91it/s]
1/99 4.9G 0.1046 0.0379 0.01912 278 640: 11%|█ | 3/28 [00:00<00:05, 4.92it/s]
1/99 4.9G 0.1044 0.03591 0.01913 204 640: 11%|█ | 3/28 [00:00<00:05, 4.92it/s]
1/99 4.9G 0.1044 0.03591 0.01913 204 640: 14%|█▍ | 4/28 [00:00<00:04, 4.93it/s]
1/99 4.9G 0.104 0.0363 0.01893 255 640: 14%|█▍ | 4/28 [01:03<00:04, 4.93it/s]
1/99 4.9G 0.104 0.0363 0.01893 255 640: 18%|█▊ | 5/28 [01:03<08:41, 22.66s/it]
1/99 4.9G 0.1041 0.03651 0.01861 305 640: 18%|█▊ | 5/28 [01:12<08:41, 22.66s/it]
1/99 4.9G 0.1041 0.03651 0.01861 305 640: 21%|██▏ | 6/28 [01:12<06:38, 18.12s/it]
1/99 4.9G 0.1037 0.03682 0.01855 276 640: 21%|██▏ | 6/28 [01:12<06:38, 18.12s/it]
1/99 4.9G 0.1037 0.03682 0.01855 276 640: 25%|██▌ | 7/28 [01:12<04:17, 12.26s/it]
1/99 4.9G 0.1032 0.03768 0.01836 308 640: 25%|██▌ | 7/28 [01:12<04:17, 12.26s/it]
1/99 4.9G 0.1032 0.03768 0.01836 308 640: 29%|██▊ | 8/28 [01:12<02:48, 8.42s/it]
1/99 4.9G 0.1028 0.03837 0.01809 301 640: 29%|██▊ | 8/28 [01:13<02:48, 8.42s/it]
1/99 4.9G 0.1028 0.03837 0.01809 301 640: 32%|███▏ | 9/28 [01:13<01:51, 5.85s/it]
1/99 4.9G 0.1021 0.03883 0.01778 247 640: 32%|███▏ | 9/28 [01:13<01:51, 5.85s/it]
1/99 4.9G 0.1021 0.03883 0.01778 247 640: 36%|███▌ | 10/28 [01:13<01:13, 4.11s/it]
1/99 4.9G 0.1021 0.03866 0.0176 270 640: 36%|███▌ | 10/28 [01:13<01:13, 4.11s/it]
1/99 4.9G 0.1021 0.03866 0.0176 270 640: 39%|███▉ | 11/28 [01:13<00:49, 2.91s/it]
1/99 4.9G 0.1018 0.03892 0.01756 265 640: 39%|███▉ | 11/28 [01:13<00:49, 2.91s/it]
1/99 4.9G 0.1018 0.03892 0.01756 265 640: 43%|████▎ | 12/28 [01:13<00:33, 2.09s/it]
1/99 4.9G 0.1013 0.03921 0.01738 253 640: 43%|████▎ | 12/28 [02:11<00:33, 2.09s/it]
1/99 4.9G 0.1013 0.03921 0.01738 253 640: 46%|████▋ | 13/28 [02:11<04:45, 19.03s/it]
1/99 4.9G 0.1008 0.03947 0.01723 258 640: 46%|████▋ | 13/28 [02:21<04:45, 19.03s/it]
1/99 4.9G 0.1008 0.03947 0.01723 258 640: 50%|█████ | 14/28 [02:21<03:47, 16.28s/it]
1/99 4.9G 0.1004 0.03931 0.01709 234 640: 50%|█████ | 14/28 [02:21<03:47, 16.28s/it]
1/99 4.9G 0.1004 0.03931 0.01709 234 640: 54%|█████▎ | 15/28 [02:21<02:28, 11.43s/it]
1/99 4.9G 0.09994 0.0395 0.017 236 640: 54%|█████▎ | 15/28 [02:22<02:28, 11.43s/it]
1/99 4.9G 0.09994 0.0395 0.017 236 640: 57%|█████▋ | 16/28 [02:22<01:36, 8.05s/it]
1/99 4.9G 0.09961 0
|
|||
|
|
Class Images Labels P R mAP@.5 mAP@.5:.95: 0%| | 0/2 [00:00<?, ?it/s]
Class Images Labels P R mAP@.5 mAP@.5:.95: 50%|█████ | 1/2 [00:00<00:00, 2.64it/s]
Class Images Labels P R mAP@.5 mAP@.5:.95: 100%|██████████| 2/2 [00:00<00:00, 3.49it/s]
Class Images Labels P R mAP@.5 mAP@.5:.95: 100%|██████████| 2/2 [00:00<00:00, 3.32it/s]
|
|||
|
|
all 98 501 0.532 0.0395 0.025 0.00657
|
|||
|
|
|
|||
|
|
Epoch gpu_mem box obj cls labels img_size
|
|||
|
|
0%| | 0/28 [00:00<?, ?it/s]
2/99 4.9G 0.09622 0.03935 0.01393 257 640: 0%| | 0/28 [01:09<?, ?it/s]
2/99 4.9G 0.09622 0.03935 0.01393 257 640: 4%|▎ | 1/28 [01:09<31:24, 69.80s/it]
2/99 4.9G 0.09625 0.03907 0.01236 255 640: 4%|▎ | 1/28 [01:12<31:24, 69.80s/it]
2/99 4.9G 0.09625 0.03907 0.01236 255 640: 7%|▋ | 2/28 [01:12<13:07, 30.30s/it]
2/99 4.9G 0.09395 0.03993 0.01313 267 640: 7%|▋ | 2/28 [01:12<13:07, 30.30s/it]
2/99 4.9G 0.09395 0.03993 0.01313 267 640: 11%|█ | 3/28 [01:12<06:53, 16.54s/it]
2/99 4.9G 0.09244 0.04017 0.01298 260 640: 11%|█ | 3/28 [01:12<06:53, 16.54s/it]
2/99 4.9G 0.09244 0.04017 0.01298 260 640: 14%|█▍ | 4/28 [01:12<04:02, 10.09s/it]
2/99 4.9G 0.0915 0.04117 0.01278 278 640: 14%|█▍ | 4/28 [01:12<04:02, 10.09s/it]
2/99 4.9G 0.0915 0.04117 0.01278 278 640: 18%|█▊ | 5/28 [01:12<02:29, 6.52s/it]
2/99 4.9G 0.09125 0.04211 0.01255 294 640: 18%|█▊ | 5/28 [01:13<02:29, 6.52s/it]
2/99 4.9G 0.09125 0.04211 0.01255 294 640: 21%|██▏ | 6/28 [01:13<01:36, 4.37s/it]
2/99 4.9G 0.09128 0.04237 0.01256 300 640: 21%|██▏ | 6/28 [01:13<01:36, 4.37s/it]
2/99 4.9G 0.09128 0.04237 0.01256 300 640: 25%|██▌ | 7/28 [01:13<01:02, 3.00s/it]
2/99 4.9G 0.09106 0.04254 0.01251 277 640: 25%|██▌ | 7/28 [01:13<01:02, 3.00s/it]
2/99 4.9G 0.09106 0.04254 0.01251 277 640: 29%|██▊ | 8/28 [01:13<00:42, 2.11s/it]Traceback (most recent call last):
|
|||
|
|
File "train.py", line 40, in <module>
|
|||
|
|
import val # for end-of-epoch mAP
|
|||
|
|
File "/host/zjc/yolov5/val.py", line 37, in <module>
|
|||
|
|
from models.common import DetectMultiBackend
|
|||
|
|
File "/host/zjc/yolov5/models/common.py", line 16, in <module>
|
|||
|
|
import pandas as pd
|
|||
|
|
ModuleNotFoundError: No module named 'pandas'
|
|||
|
|
[34m[1mtrain: [0mweights=./weights/yolov5s.pt, cfg=./models/yolov5s.yaml, data=./data/forest.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=110, batch_size=32, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=None, 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, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest
|
|||
|
|
/bin/sh: 1: git: not found
|
|||
|
|
YOLOv5 🚀 2022-7-12 Python-3.9.12 torch-1.7.1+cu110 CUDA:0 (GeForce RTX 2080 Ti, 11016MiB)
|
|||
|
|
|
|||
|
|
[34m[1mhyperparameters: [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
|
|||
|
|
[34m[1mWeights & Biases: [0mrun 'pip install wandb' to automatically track and visualize YOLOv5 🚀 runs (RECOMMENDED)
|
|||
|
|
[34m[1mTensorBoard: [0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/
|
|||
|
|
Overriding model.yaml nc=80 with nc=2
|
|||
|
|
|
|||
|
|
from n params module arguments
|
|||
|
|
0 -1 1 3520 models.common.Conv [3, 32, 6, 2, 2]
|
|||
|
|
1 -1 1 18560 models.common.Conv [32, 64, 3, 2]
|
|||
|
|
2 -1 1 18816 models.common.C3 [64, 64, 1]
|
|||
|
|
3 -1 1 73984 models.common.Conv [64, 128, 3, 2]
|
|||
|
|
4 -1 2 115712 models.common.C3 [128, 128, 2]
|
|||
|
|
5 -1 1 295424 models.common.Conv [128, 256, 3, 2]
|
|||
|
|
6 -1 3 625152 models.common.C3 [256, 256, 3]
|
|||
|
|
7 -1 1 1180672 models.common.Conv [256, 512, 3, 2]
|
|||
|
|
8 -1 1 1182720 models.common.C3 [512, 512, 1]
|
|||
|
|
9 -1 1 656896 models.common.SPPF [512, 512, 5]
|
|||
|
|
10 -1 1 131584 models.common.Conv [512, 256, 1, 1]
|
|||
|
|
11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
|
|||
|
|
12 [-1, 6] 1 0 models.common.Concat [1]
|
|||
|
|
13 -1 1 361984 models.common.C3 [512, 256, 1, False]
|
|||
|
|
14 -1 1 33024 models.common.Conv [256, 128, 1, 1]
|
|||
|
|
15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
|
|||
|
|
16 [-1, 4] 1 0 models.common.Concat [1]
|
|||
|
|
17 -1 1 90880 models.common.C3 [256, 128, 1, False]
|
|||
|
|
18 -1 1 147712 models.common.Conv [128, 128, 3, 2]
|
|||
|
|
19 [-1, 14] 1 0 models.common.Concat [1]
|
|||
|
|
20 -1 1 296448 models.common.C3 [256, 256, 1, False]
|
|||
|
|
21 -1 1 590336 models.common.Conv [256, 256, 3, 2]
|
|||
|
|
22 [-1, 10] 1 0 models.common.Concat [1]
|
|||
|
|
23 -1 1 1182720 models.common.C3 [512, 512, 1, False]
|
|||
|
|
24 [17, 20, 23] 1 18879 models.yolo.Detect [2, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]
|
|||
|
|
YOLOv5s summary: 270 layers, 7025023 parameters, 7025023 gradients, 16.0 GFLOPs
|
|||
|
|
|
|||
|
|
Transferred 342/349 items from weights/yolov5s.pt
|
|||
|
|
[34m[1mAMP: [0mchecks passed ✅
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Scaled weight_decay = 0.0005
|
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[34m[1moptimizer:[0m SGD with parameter groups 57 weight (no decay), 60 weight, 60 bias
|
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|
WARNING: DP not recommended, use torch.distributed.run for best DDP Multi-GPU results.
|
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|
See Multi-GPU Tutorial at https://github.com/ultralytics/yolov5/issues/475 to get started.
|
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|
|
[34m[1mgithub: [0mskipping check (Docker image), for updates see https://github.com/ultralytics/yolov5
|
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|
|
[34m[1mtrain: [0mScanning '/host/zjc/yolov5/VOCdevkit/labels/train.cache' images and labels... 890 found, 0 missing, 1 empty, 0 corrupt: 100%|██████████| 890/890 [00:00<?, ?it/s]
[34m[1mtrain: [0mScanning '/host/zjc/yolov5/VOCdevkit/labels/train.cache' images and labels... 890 found, 0 missing, 1 empty, 0 corrupt: 100%|██████████| 890/890 [00:00<?, ?it/s]
|
|||
|
|
[34m[1mval: [0mScanning '/host/zjc/yolov5/VOCdevkit/labels/val.cache' images and labels... 98 found, 0 missing, 0 empty, 0 corrupt: 100%|██████████| 98/98 [00:00<?, ?it/s]
[34m[1mval: [0mScanning '/host/zjc/yolov5/VOCdevkit/labels/val.cache' images and labels... 98 found, 0 missing, 0 empty, 0 corrupt: 100%|██████████| 98/98 [00:00<?, ?it/s]
|
|||
|
|
Plotting labels to runs/train/exp20/labels.jpg...
|
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|
|
|||
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[34m[1mAutoAnchor: [0m3.68 anchors/target, 0.993 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅
|
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|
|
Image sizes 640 train, 640 val
|
|||
|
|
Using 8 dataloader workers
|
|||
|
|
Logging results to [1mruns/train/exp20[0m
|
|||
|
|
Starting training for 110 epochs...
|
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|
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|
|
Epoch gpu_mem box obj cls labels img_size
|
|||
|
|
0%| | 0/28 [00:00<?, ?it/s]
0/109 3.75G 0.1267 0.03905 0.0302 252 640: 0%| | 0/28 [01:38<?, ?it/s]
0/109 3.75G 0.1267 0.03905 0.0302 252 640: 4%|▎ | 1/28 [01:40<45:04, 100.17s/it]
0/109 3.82G 0.1261 0.03978 0.03018 279 640: 4%|▎ | 1/28 [01:40<45:04, 100.17s/it]
0/109 3.82G 0.1261 0.03978 0.03018 279 640: 7%|▋ | 2/28 [01:40<18:03, 41.66s/it]
0/109 3.82G 0.1261 0.03956 0.03017 318 640: 7%|▋ | 2/28 [01:41<18:03, 41.66s/it]
0/109 3.82G 0.1261 0.03956 0.03017 318 640: 11%|█ | 3/28 [01:41<09:35, 23.01s/it]
0/109 3.82G 0.1263 0.03946 0.03026 336 640: 11%|█ | 3/28 [01:41<09:35, 23.01s/it]
0/109 3.82G 0.1263 0.03946 0.03026 336 640: 14%|█▍ | 4/28 [01:41<05:37, 14.05s/it]
0/109 3.82G 0.126 0.03939 0.03019 312 640: 14%|█▍ | 4/28 [01:45<05:37, 14.05s/it]
0/109 3.82G 0.126 0.03939 0.03019 312 640: 18%|█▊ | 5/28 [01:45<03:54, 10.22s/it]
0/109 3.82G 0.1262 0.03894 0.03003 338 640: 18%|█▊ | 5/28 [01:45<03:54, 10.22s/it]
0/109 3.82G 0.1262 0.03894 0.03003 338 640: 21%|██▏ | 6/28 [01:45<02:29, 6.81s/it]
0/109 3.82G 0.1258 0.03845 0.02992 240 640: 21%|██▏ | 6/28 [01:47<02:29, 6.81s/it]
0/109 3.82G 0.1258 0.03845 0.02992 240 640: 25%|██▌ | 7/28 [01:47<01:52, 5.35s/it]
0/109 3.82G 0.1255 0.03816 0.02979 269 640: 25%|██▌ | 7/28 [01:48<01:52, 5.35s/it]
0/109 3.82G 0.1255 0.03816 0.02979 269 640: 29%|██▊ | 8/28 [01:48<01:14, 3.71s/it]
0/109 3.82G 0.1253 0.03783 0.02967 264 640: 29%|██▊ | 8/28 [02:50<01:14, 3.71s/it]
0/109 3.82G 0.1253 0.03783 0.02967 264 640: 32%|███▏ | 9/28 [02:50<07:00, 22.16s/it]
0/109 3.82G 0.125 0.03756 0.02956 289 640: 32%|███▏ | 9/28 [02:56<07:00, 22.16s/it]
0/109 3.82G 0.125 0.03756 0.02956 289 640: 36%|███▌ | 10/28 [02:56<05:07, 17.09s/it]
0/109 3.82G 0.1246 0.03744 0.02942 305 640: 36%|███▌ | 10/28 [02:58<05:07, 17.09s/it]
0/109 3.82G 0.1246 0.03744 0.02942 305 640: 39%|███▉ | 11/28 [02:58<03:29, 12.35s/it]
0/109 3.82G 0.1243 0.03725 0.02926 310 640: 39%|███▉ | 11/28 [02:58<03:29, 12.35s/it]
0/109 3.82G 0.1243 0.03725 0.02926 310 640: 43%|████▎ | 12/28 [02:58<02:18, 8.65s/it]
0/109 3.82G 0.1237 0.03749 0.02911 295 640: 43%|████▎ | 12/28 [02:58<02:18, 8.65s/it]
0/109 3.82G 0.1237 0.03749 0.02911 295 640: 46%|████▋ | 13/28 [02:58<01:32, 6.18s/it]
0/109 3.82G 0.1232 0.03749 0.0289 307 640: 46%|████▋ | 13/28 [02:59<01:32, 6.18s/it]
0/109 3.82G 0.1232 0.03749 0.0289 307 640: 50%|█████ | 14/28 [02:59<01:01, 4.37s/it]
0/109 3.82G 0.1228 0.03728 0.02867 332 640: 50%|█████ | 14/28 [02:59<01:01, 4.37s/it]
0/109 3.82G 0.1228 0.03728 0.02867 332 640: 54%|█████▎ | 15/28 [02:59<00:42, 3.30s/it]
0/109 3.82G 0.1224 0.03707 0.02846 293 640: 54%|█████▎ | 15/28 [03:00<00:42, 3.30s/it]
0/109 3.82G 0.1224 0.03707 0.02846 293 640: 57%|█████▋ | 16/28 [03:00<00:28, 2.37s/it]
0/109 3.82G 0.1219
|
|||
|
|
Class Images Labels P R mAP@.5 mAP@.5:.95: 0%| | 0/2 [00:00<?, ?it/s]
Class Images Labels P R mAP@.5 mAP@.5:.95: 50%|█████ | 1/2 [00:00<00:00, 2.70it/s]
Class Images Labels P R mAP@.5 mAP@.5:.95: 100%|██████████| 2/2 [00:00<00:00, 3.69it/s]
Class Images Labels P R mAP@.5 mAP@.5:.95: 100%|██████████| 2/2 [00:00<00:00, 3.50it/s]
|
|||
|
|
all 98 501 0.00119 0.175 0.00449 0.00133
|
|||
|
|
|
|||
|
|
Epoch gpu_mem box obj cls labels img_size
|
|||
|
|
0%| | 0/28 [00:00<?, ?it/s]
1/109 4.9G 0.1057 0.03522 0.02021 223 640: 0%| | 0/28 [00:00<?, ?it/s]
1/109 4.9G 0.1057 0.03522 0.02021 223 640: 4%|▎ | 1/28 [00:00<00:05, 4.87it/s]
1/109 4.9G 0.1048 0.03604 0.0193 252 640: 4%|▎ | 1/28 [00:00<00:05, 4.87it/s]
1/109 4.9G 0.1048 0.03604 0.0193 252 640: 7%|▋ | 2/28 [00:00<00:05, 4.93it/s]
1/109 4.9G 0.1045 0.03801 0.01913 278 640: 7%|▋ | 2/28 [00:00<00:05, 4.93it/s]
1/109 4.9G 0.1045 0.03801 0.01913 278 640: 11%|█ | 3/28 [00:00<00:08, 2.84it/s]
1/109 4.9G 0.1042 0.03592 0.0192 204 640: 11%|█ | 3/28 [00:01<00:08, 2.84it/s]
1/109 4.9G 0.1042 0.03592 0.0192 204 640: 14%|█▍ | 4/28 [00:01<00:07, 3.40it/s]
1/109 4.9G 0.1037 0.03629 0.01898 255 640: 14%|█▍ | 4/28 [01:22<00:07, 3.40it/s]
1/109 4.9G 0.1037 0.03629 0.01898 255 640: 18%|█▊ | 5/28 [01:22<11:16, 29.40s/it]
1/109 4.9G 0.1036 0.03657 0.01863 305 640: 18%|█▊ | 5/28 [01:22<11:16, 29.40s/it]
1/109 4.9G 0.1036 0.03657 0.01863 305 640: 21%|██▏ | 6/28 [01:22<07:08, 19.47s/it]
1/109 4.9G 0.1031 0.03689 0.01857 276 640: 21%|██▏ | 6/28 [01:22<07:08, 19.47s/it]
1/109 4.9G 0.1031 0.03689 0.01857 276 640: 25%|██▌ | 7/28 [01:22<04:36, 13.17s/it]
1/109 4.9G 0.1028 0.03771 0.01835 308 640: 25%|██▌ | 7/28 [01:22<04:36, 13.17s/it]
1/109 4.9G 0.1028 0.03771 0.01835 308 640: 29%|██▊ | 8/28 [01:22<03:00, 9.04s/it]
1/109 4.9G 0.1024 0.03837 0.01808 301 640: 29%|██▊ | 8/28 [01:22<03:00, 9.04s/it]
1/109 4.9G 0.1024 0.03837 0.01808 301 640: 32%|███▏ | 9/28 [01:22<01:59, 6.28s/it]
1/109 4.9G 0.1018 0.03877 0.01778 247 640: 32%|███▏ | 9/28 [01:23<01:59, 6.28s/it]
1/109 4.9G 0.1018 0.03877 0.01778 247 640: 36%|███▌ | 10/28 [01:23<01:19, 4.40s/it]
1/109 4.9G 0.1018 0.03859 0.0176 270 640: 36%|███▌ | 10/28 [01:23<01:19, 4.40s/it]
1/109 4.9G 0.1018 0.03859 0.0176 270 640: 39%|███▉ | 11/28 [01:23<00:52, 3.12s/it]
1/109 4.9G 0.1015 0.03884 0.01754 265 640: 39%|███▉ | 11/28 [01:23<00:52, 3.12s/it]
1/109 4.9G 0.1015 0.03884 0.01754 265 640: 43%|████▎ | 12/28 [01:23<00:35, 2.23s/it]
1/109 4.9G 0.1011 0.03914 0.01735 253 640: 43%|████▎ | 12/28 [02:29<00:35, 2.23s/it]
1/109 4.9G 0.1011 0.03914 0.01735 253 640: 46%|████▋ | 13/28 [02:29<05:22, 21.53s/it]
1/109 4.9G 0.1006 0.03937 0.01718 258 640: 46%|████▋ | 13/28 [02:29<05:22, 21.53s/it]
1/109 4.9G 0.1006 0.03937 0.01718 258 640: 50%|█████ | 14/28 [02:29<03:31, 15.09s/it]
1/109 4.9G 0.1003 0.03917 0.01703 234 640: 50%|█████ | 14/28 [02:29<03:31, 15.09s/it]
1/109 4.9G 0.1003 0.03917 0.01703 234 640: 54%|█████▎ | 15/28 [02:29<02:17, 10.60s/it]
1/109 4.9G 0.09983 0.03938 0.01693 236 640: 54%|█████▎ | 15/28 [02:30<02:17, 10.60s/it]
1/109 4.9G 0.09983 0.03938 0.01693 236 640: 57%|█████▋ | 16/28 [02:30<01:29, 7.47s/it]
1/109 4.9G 0.09949 0
|
|||
|
|
Class Images Labels P R mAP@.5 mAP@.5:.95: 0%| | 0/2 [00:00<?, ?it/s]
Class Images Labels P R mAP@.5 mAP@.5:.95: 50%|█████ | 1/2 [00:00<00:00, 2.49it/s]
Class Images Labels P R mAP@.5 mAP@.5:.95: 100%|██████████| 2/2 [00:00<00:00, 3.35it/s]
Class Images Labels P R mAP@.5 mAP@.5:.95: 100%|██████████| 2/2 [00:00<00:00, 3.18it/s]
|
|||
|
|
all 98 501 0.524 0.0508 0.0248 0.00622
|
|||
|
|
|
|||
|
|
Epoch gpu_mem box obj cls labels img_size
|
|||
|
|
0%| | 0/28 [00:00<?, ?it/s]
2/109 4.9G 0.1039 0.03738 0.01415 257 640: 0%| | 0/28 [01:20<?, ?it/s]
2/109 4.9G 0.1039 0.03738 0.01415 257 640: 4%|▎ | 1/28 [01:20<36:20, 80.77s/it]
2/109 4.9G 0.104 0.03697 0.01225 255 640: 4%|▎ | 1/28 [01:20<36:20, 80.77s/it]
2/109 4.9G 0.104 0.03697 0.01225 255 640: 7%|▋ | 2/28 [01:20<14:27, 33.38s/it]
2/109 4.9G 0.1016 0.03811 0.01288 267 640: 7%|▋ | 2/28 [01:21<14:27, 33.38s/it]
2/109 4.9G 0.1016 0.03811 0.01288 267 640: 11%|█ | 3/28 [01:21<07:35, 18.22s/it]
2/109 4.9G 0.1002 0.03845 0.01268 260 640: 11%|█ | 3/28 [01:21<07:35, 18.22s/it]
2/109 4.9G 0.1002 0.03845 0.01268 260 640: 14%|█▍ | 4/28 [01:21<04:26, 11.11s/it]
2/109 4.9G 0.09898 0.0394 0.01246 278 640: 14%|█▍ | 4/28 [01:21<04:26, 11.11s/it]
2/109 4.9G 0.09898 0.0394 0.01246 278 640: 18%|█▊ | 5/28 [01:21<02:44, 7.16s/it]
2/109 4.9G 0.09854 0.04022 0.01221 294 640: 18%|█▊ | 5/28 [01:21<02:44, 7.16s/it]
2/109 4.9G 0.09854 0.04022 0.01221 294 640: 21%|██▏ | 6/28 [01:21<01:45, 4.80s/it]
2/109 4.9G 0.0978 0.04073 0.01223 300 640: 21%|██▏ | 6/28 [01:21<01:45, 4.80s/it]
2/109 4.9G 0.0978 0.04073 0.01223 300 640: 25%|██▌ | 7/28 [01:21<01:08, 3.29s/it]
2/109 4.9G 0.09709 0.041 0.01221 277 640: 25%|██▌ | 7/28 [01:22<01:08, 3.29s/it]
2/109 4.9G 0.09709 0.041 0.01221 277 640: 29%|██▊ | 8/28 [01:22<00:46, 2.30s/it] |