from n params module arguments
0 -1 1 8800 models.common.Focus [3, 80, 3]
1 -1 1 115520 models.common.Conv [80, 160, 3, 2]
2 -1 1 315680 models.common.BottleneckCSP [160, 160, 4]
3 -1 1 461440 models.common.Conv [160, 320, 3, 2]
4 -1 1 3311680 models.common.BottleneckCSP [320, 320, 12]
5 -1 1 1844480 models.common.Conv [320, 640, 3, 2]
6 -1 1 13228160 models.common.BottleneckCSP [640, 640, 12]
7 -1 1 7375360 models.common.Conv [640, 1280, 3, 2]
8 -1 1 4099840 models.common.SPP [1280, 1280, [5, 9, 13]]
9 -1 1 20087040 models.common.BottleneckCSP [1280, 1280, 4, False]
10 -1 1 820480 models.common.Conv [1280, 640, 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 5435520 models.common.BottleneckCSP [1280, 640, 4, False]
14 -1 1 205440 models.common.Conv [640, 320, 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 1360960 models.common.BottleneckCSP [640, 320, 4, False]
18 -1 1 5778 torch.nn.modules.conv.Conv2d [320, 18, 1, 1]
19 -2 1 922240 models.common.Conv [320, 320, 3, 2]
20 [-1, 14] 1 0 models.common.Concat [1]
21 -1 1 5025920 models.common.BottleneckCSP [640, 640, 4, False]
22 -1 1 11538 torch.nn.modules.conv.Conv2d [640, 18, 1, 1]
23 -2 1 3687680 models.common.Conv [640, 640, 3, 2]
24 [-1, 10] 1 0 models.common.Concat [1]
25 -1 1 20087040 models.common.BottleneckCSP [1280, 1280, 4, False]
26 -1 1 23058 torch.nn.modules.conv.Conv2d [1280, 18, 1, 1]
27 [-1, 22, 18] 1 0 models.yolo.Detect [1, [[116, 90, 156, 198, 373, 326], [30, 61, 62, 45, 59, 119], [10, 13, 16, 30, 33, 23]]]
Model Summary: 407 layers, 8.84337e+07 parameters, 8.84337e+07 gradients
Optimizer groups: 134 .bias, 142 conv.weight, 131 other
Reading image shapes: 100% 3241/3241 [00:00<00:00, 13426.34it/s]
Caching labels convertor/fold0/labels/train2017.npy (3241 found, 0 missing, 0 empty, 0 duplicate, for 3241 images): 100% 3241/3241 [00:00<00:00, 4624.23it/s]
Saving labels to convertor/fold0/labels/train2017.npy for faster future loading
Reading image shapes: 100% 1216/1216 [00:00<00:00, 14743.19it/s]
Caching labels convertor/fold0/labels/val2017 (1216 found, 0 missing, 0 empty, 0 duplicate, for 1216 images): 100% 1216/1216 [00:00<00:00, 4898.86it/s]
Saving labels to convertor/fold0/labels/val2017.npy for faster future loading
Analyzing anchors... Best Possible Recall (BPR) = 0.9991
Image sizes 1024 train, 1024 test
Using 2 dataloader workers
Starting training for 1 epochs...
Epoch gpu_mem GIoU obj cls total targets img_size
0/0 9.12G 0.08411 0.1946 0 0.2787 38 1024: 100% 1081/1081 [25:29<00:00, 1.41s/it]
Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 406/406 [05:55<00:00, 1.14it/s]
all 1.22e+03 5.32e+04 0.187 0.748 0.474 0.142
Optimizer stripped from weights/last_yolov5x_fold0.pt
1 epochs completed in 0.524 hours.
- https://www.kaggle.com/nvnnghia/yolov5-pseudo-labeling
- https://www.kaggle.com/orkatz2/yolov5-train
- https://www.kaggle.com/c/global-wheat-detection/
- https://www.kaggle.com/qiaoyuanfang/yolov5