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I used the coco128 and unzip into the datasets folder. The command is python tools/train.py -f exps/example/custom/yolox_s.py -d 1 -b 32 --fp16 -o -c C:\Users\olivi\YOLOX\pretrained\yolox_s.pth However, the APs have a lot of nan and 0. The log details are below.
2024-03-03 15:06:04 | INFO | yolox.core.trainer:364 - Save weights to ./YOLOX_outputs\yolox_s
2024-03-03 15:06:05 | INFO | yolox.core.trainer:364 - Save weights to ./YOLOX_outputs\yolox_s
2024-03-03 15:06:05 | INFO | yolox.core.trainer:203 - ---> start train epoch2
2024-03-03 15:06:07 | INFO | yolox.core.trainer:364 - Save weights to ./YOLOX_outputs\yolox_s
100%|####################################################################################| 1/1 [00:05<00:00, 5.63s/it]
2024-03-03 15:06:13 | INFO | yolox.evaluators.coco_evaluator:259 - Evaluate in main process...
2024-03-03 15:06:13 | INFO | yolox.evaluators.coco_evaluator:292 - Loading and preparing results...
2024-03-03 15:06:13 | INFO | yolox.evaluators.coco_evaluator:292 - DONE (t=0.01s)
2024-03-03 15:06:13 | INFO | pycocotools.coco:366 - creating index...
2024-03-03 15:06:13 | INFO | pycocotools.coco:366 - index created!
2024-03-03 15:06:13 | INFO | yolox.evaluators.coco_evaluator:302 - Running per image evaluation...
2024-03-03 15:06:13 | INFO | yolox.evaluators.coco_evaluator:302 - Evaluate annotation type bbox
2024-03-03 15:06:13 | INFO | yolox.evaluators.coco_evaluator:302 - DONE (t=0.01s).
2024-03-03 15:06:13 | INFO | yolox.evaluators.coco_evaluator:303 - Accumulating evaluation results...
2024-03-03 15:06:13 | INFO | yolox.evaluators.coco_evaluator:303 - DONE (t=0.03s).
2024-03-03 15:06:13 | INFO | yolox.core.trainer:354 -
Average forward time: 0.00 ms, Average NMS time: 0.00 ms, Average inference time: 0.00 ms
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.115
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.154
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.154
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.120
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.100
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.115
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.115
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.115
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.120
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.100
per class AP:
class
AP
class
AP
class
AP
0
nan
1
nan
2
nan
3
nan
4
nan
5
nan
6
nan
7
0.000
8
nan
9
nan
10
nan
11
nan
12
nan
13
nan
14
nan
15
nan
16
nan
17
nan
18
0.000
19
nan
20
nan
21
nan
22
nan
23
nan
24
nan
25
nan
26
0.000
27
nan
28
nan
29
nan
30
nan
31
nan
32
0.000
33
nan
34
0.000
35
nan
36
nan
37
0.000
38
nan
39
nan
40
nan
41
nan
42
nan
43
nan
44
0.000
45
nan
46
nan
47
nan
48
nan
49
90.000
50
nan
51
nan
52
nan
53
nan
54
nan
55
nan
56
0.000
57
0.000
58
nan
59
nan
60
nan
61
60.000
62
nan
63
nan
64
nan
65
nan
66
nan
67
nan
68
nan
69
0.000
70
0.000
per class AR:
class
AR
class
AR
class
AR
:--------
:------
:--------
:-------
:--------
:------
0
nan
1
nan
2
nan
3
nan
4
nan
5
nan
6
nan
7
0.000
8
nan
9
nan
10
nan
11
nan
12
nan
13
nan
14
nan
15
nan
16
nan
17
nan
18
0.000
19
nan
20
nan
21
nan
22
nan
23
nan
24
nan
25
nan
26
0.000
27
nan
28
nan
29
nan
30
nan
31
nan
32
0.000
33
nan
34
0.000
35
nan
36
nan
37
0.000
38
nan
39
nan
40
nan
41
nan
42
nan
43
nan
44
0.000
45
nan
46
nan
47
nan
48
nan
49
90.000
50
nan
51
nan
52
nan
53
nan
54
nan
55
nan
56
0.000
57
0.000
58
nan
59
nan
60
nan
61
60.000
62
nan
63
nan
64
nan
65
nan
66
nan
67
nan
68
nan
69
0.000
70
0.000
2024-03-03 15:06:13 | INFO | yolox.core.trainer:364 - Save weights to ./YOLOX_outputs\yolox_s
2024-03-03 15:06:14 | INFO | yolox.core.trainer:364 - Save weights to ./YOLOX_outputs\yolox_s
2024-03-03 15:06:14 | INFO | yolox.core.trainer:203 - ---> start train epoch3
2024-03-03 15:06:20 | INFO | yolox.core.trainer:364 - Save weights to ./YOLOX_outputs\yolox_s
100%|####################################################################################| 1/1 [00:04<00:00, 4.98s/it]
2024-03-03 15:06:25 | INFO | yolox.evaluators.coco_evaluator:259 - Evaluate in main process...
2024-03-03 15:06:25 | INFO | yolox.evaluators.coco_evaluator:292 - Loading and preparing results...
2024-03-03 15:06:25 | INFO | yolox.evaluators.coco_evaluator:292 - DONE (t=0.01s)
2024-03-03 15:06:25 | INFO | pycocotools.coco:366 - creating index...
2024-03-03 15:06:25 | INFO | pycocotools.coco:366 - index created!
2024-03-03 15:06:25 | INFO | yolox.evaluators.coco_evaluator:302 - Running per image evaluation...
2024-03-03 15:06:25 | INFO | yolox.evaluators.coco_evaluator:302 - Evaluate annotation type bbox
2024-03-03 15:06:25 | INFO | yolox.evaluators.coco_evaluator:302 - DONE (t=0.01s).
2024-03-03 15:06:25 | INFO | yolox.evaluators.coco_evaluator:303 - Accumulating evaluation results...
2024-03-03 15:06:25 | INFO | yolox.evaluators.coco_evaluator:303 - DONE (t=0.02s).
2024-03-03 15:06:25 | INFO | yolox.core.trainer:354 -
Average forward time: 0.00 ms, Average NMS time: 0.00 ms, Average inference time: 0.00 ms
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.031
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.038
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.038
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.044
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.062
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.062
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.062
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.089
per class AP:
class
AP
class
AP
class
AP
0
nan
1
nan
2
nan
3
nan
4
nan
5
nan
6
nan
7
0.000
8
nan
9
nan
10
nan
11
nan
12
nan
13
nan
14
nan
15
nan
16
nan
17
nan
18
0.000
19
nan
20
nan
21
nan
22
nan
23
nan
24
nan
25
nan
26
0.000
27
nan
28
nan
29
nan
30
nan
31
nan
32
0.000
33
nan
34
0.000
35
nan
36
nan
37
0.000
38
nan
39
nan
40
nan
41
nan
42
nan
43
nan
44
0.000
45
nan
46
nan
47
nan
48
nan
49
40.000
50
nan
51
nan
52
nan
53
nan
54
nan
55
nan
56
0.000
57
0.000
58
nan
59
nan
60
nan
61
0.000
62
nan
63
nan
64
nan
65
nan
66
nan
67
nan
68
nan
69
0.000
70
0.000
per class AR:
class
AR
class
AR
class
AR
:--------
:------
:--------
:-------
:--------
:------
0
nan
1
nan
2
nan
3
nan
4
nan
5
nan
6
nan
7
0.000
8
nan
9
nan
10
nan
11
nan
12
nan
13
nan
14
nan
15
nan
16
nan
17
nan
18
0.000
19
nan
20
nan
21
nan
22
nan
23
nan
24
nan
25
nan
26
0.000
27
nan
28
nan
29
nan
30
nan
31
nan
32
0.000
33
nan
34
0.000
35
nan
36
nan
37
0.000
38
nan
39
nan
40
nan
41
nan
42
nan
43
nan
44
0.000
45
nan
46
nan
47
nan
48
nan
49
80.000
50
nan
51
nan
52
nan
53
nan
54
nan
55
nan
56
0.000
57
0.000
58
nan
59
nan
60
nan
61
0.000
62
nan
63
nan
64
nan
65
nan
66
nan
67
nan
68
nan
69
0.000
70
0.000
2024-03-03 15:06:25 | INFO | yolox.core.trainer:364 - Save weights to ./YOLOX_outputs\yolox_s
2024-03-03 15:06:25 | INFO | yolox.core.trainer:364 - Save weights to ./YOLOX_outputs\yolox_s
2024-03-03 15:06:25 | INFO | yolox.core.trainer:203 - ---> start train epoch4
2024-03-03 15:06:28 | INFO | yolox.core.trainer:364 - Save weights to ./YOLOX_outputs\yolox_s
100%|####################################################################################| 1/1 [00:05<00:00, 5.17s/it]
2024-03-03 15:06:33 | INFO | yolox.evaluators.coco_evaluator:259 - Evaluate in main process...
2024-03-03 15:06:33 | INFO | yolox.evaluators.coco_evaluator:292 - Loading and preparing results...
2024-03-03 15:06:33 | INFO | yolox.evaluators.coco_evaluator:292 - DONE (t=0.01s)
2024-03-03 15:06:33 | INFO | pycocotools.coco:366 - creating index...
2024-03-03 15:06:33 | INFO | pycocotools.coco:366 - index created!
2024-03-03 15:06:33 | INFO | yolox.evaluators.coco_evaluator:302 - Running per image evaluation...
2024-03-03 15:06:33 | INFO | yolox.evaluators.coco_evaluator:302 - Evaluate annotation type bbox
2024-03-03 15:06:33 | INFO | yolox.evaluators.coco_evaluator:302 - DONE (t=0.01s).
2024-03-03 15:06:33 | INFO | yolox.evaluators.coco_evaluator:303 - Accumulating evaluation results...
2024-03-03 15:06:33 | INFO | yolox.evaluators.coco_evaluator:303 - DONE (t=0.02s).
2024-03-03 15:06:33 | INFO | yolox.core.trainer:354 -
Average forward time: 0.00 ms, Average NMS time: 0.00 ms, Average inference time: 0.00 ms
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
per class AP:
class
AP
class
AP
class
AP
0
nan
1
nan
2
nan
3
nan
4
nan
5
nan
6
nan
7
0.000
8
nan
9
nan
10
nan
11
nan
12
nan
13
nan
14
nan
15
nan
16
nan
17
nan
18
0.000
19
nan
20
nan
21
nan
22
nan
23
nan
24
nan
25
nan
26
0.000
27
nan
28
nan
29
nan
30
nan
31
nan
32
0.000
33
nan
34
0.000
35
nan
36
nan
37
0.000
38
nan
39
nan
40
nan
41
nan
42
nan
43
nan
44
0.000
45
nan
46
nan
47
nan
48
nan
49
0.000
50
nan
51
nan
52
nan
53
nan
54
nan
55
nan
56
0.000
57
0.000
58
nan
59
nan
60
nan
61
0.000
62
nan
63
nan
64
nan
65
nan
66
nan
67
nan
68
nan
69
0.000
70
0.000
per class AR:
class
AR
class
AR
class
AR
:--------
:------
:--------
:------
:--------
:------
0
nan
1
nan
2
nan
3
nan
4
nan
5
nan
6
nan
7
0.000
8
nan
9
nan
10
nan
11
nan
12
nan
13
nan
14
nan
15
nan
16
nan
17
nan
18
0.000
19
nan
20
nan
21
nan
22
nan
23
nan
24
nan
25
nan
26
0.000
27
nan
28
nan
29
nan
30
nan
31
nan
32
0.000
33
nan
34
0.000
35
nan
36
nan
37
0.000
38
nan
39
nan
40
nan
41
nan
42
nan
43
nan
44
0.000
45
nan
46
nan
47
nan
48
nan
49
0.000
50
nan
51
nan
52
nan
53
nan
54
nan
55
nan
56
0.000
57
0.000
58
nan
59
nan
60
nan
61
0.000
62
nan
63
nan
64
nan
65
nan
66
nan
67
nan
68
nan
69
0.000
70
0.000
2024-03-03 15:06:33 | INFO | yolox.core.trainer:364 - Save weights to ./YOLOX_outputs\yolox_s
2024-03-03 15:06:33 | INFO | yolox.core.trainer:364 - Save weights to ./YOLOX_outputs\yolox_s
2024-03-03 15:06:33 | INFO | yolox.core.trainer:203 - ---> start train epoch5
2024-03-03 15:06:36 | INFO | yolox.core.trainer:364 - Save weights to ./YOLOX_outputs\yolox_s
100%|####################################################################################| 1/1 [00:05<00:00, 5.09s/it]
2024-03-03 15:06:41 | INFO | yolox.evaluators.coco_evaluator:259 - Evaluate in main process...
2024-03-03 15:06:41 | INFO | yolox.evaluators.coco_evaluator:292 - Loading and preparing results...
2024-03-03 15:06:41 | INFO | yolox.evaluators.coco_evaluator:292 - DONE (t=0.01s)
2024-03-03 15:06:41 | INFO | pycocotools.coco:366 - creating index...
2024-03-03 15:06:41 | INFO | pycocotools.coco:366 - index created!
2024-03-03 15:06:41 | INFO | yolox.evaluators.coco_evaluator:302 - Running per image evaluation...
2024-03-03 15:06:41 | INFO | yolox.evaluators.coco_evaluator:302 - Evaluate annotation type bbox
2024-03-03 15:06:41 | INFO | yolox.evaluators.coco_evaluator:302 - DONE (t=0.02s).
2024-03-03 15:06:41 | INFO | yolox.evaluators.coco_evaluator:303 - Accumulating evaluation results...
2024-03-03 15:06:41 | INFO | yolox.evaluators.coco_evaluator:303 - DONE (t=0.03s).
2024-03-03 15:06:41 | INFO | yolox.core.trainer:354 -
Average forward time: 0.00 ms, Average NMS time: 0.00 ms, Average inference time: 0.00 ms
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
per class AP:
class
AP
class
AP
class
AP
0
nan
1
nan
2
nan
3
nan
4
nan
5
nan
6
nan
7
0.000
8
nan
9
nan
10
nan
11
nan
12
nan
13
nan
14
nan
15
nan
16
nan
17
nan
18
0.000
19
nan
20
nan
21
nan
22
nan
23
nan
24
nan
25
nan
26
0.000
27
nan
28
nan
29
nan
30
nan
31
nan
32
0.000
33
nan
34
0.000
35
nan
36
nan
37
0.000
38
nan
39
nan
40
nan
41
nan
42
nan
43
nan
44
0.000
45
nan
46
nan
47
nan
48
nan
49
0.000
50
nan
51
nan
52
nan
53
nan
54
nan
55
nan
56
0.000
57
0.000
58
nan
59
nan
60
nan
61
0.000
62
nan
63
nan
64
nan
65
nan
66
nan
67
nan
68
nan
69
0.000
70
0.000
per class AR:
class
AR
class
AR
class
AR
:--------
:------
:--------
:------
:--------
:------
0
nan
1
nan
2
nan
3
nan
4
nan
5
nan
6
nan
7
0.000
8
nan
9
nan
10
nan
11
nan
12
nan
13
nan
14
nan
15
nan
16
nan
17
nan
18
0.000
19
nan
20
nan
21
nan
22
nan
23
nan
24
nan
25
nan
26
0.000
27
nan
28
nan
29
nan
30
nan
31
nan
32
0.000
33
nan
34
0.000
35
nan
36
nan
37
0.000
38
nan
39
nan
40
nan
41
nan
42
nan
43
nan
44
0.000
45
nan
46
nan
47
nan
48
nan
49
0.000
50
nan
51
nan
52
nan
53
nan
54
nan
55
nan
56
0.000
57
0.000
58
nan
59
nan
60
nan
61
0.000
62
nan
63
nan
64
nan
65
nan
66
nan
67
nan
68
nan
69
0.000
70
0.000
2024-03-03 15:06:41 | INFO | yolox.core.trainer:364 - Save weights to ./YOLOX_outputs\yolox_s
2024-03-03 15:06:42 | INFO | yolox.core.trainer:364 - Save weights to ./YOLOX_outputs\yolox_s
2024-03-03 15:06:42 | INFO | yolox.core.trainer:203 - ---> start train epoch6
The text was updated successfully, but these errors were encountered:
I used the coco128 and unzip into the datasets folder. The command is python tools/train.py -f exps/example/custom/yolox_s.py -d 1 -b 32 --fp16 -o -c C:\Users\olivi\YOLOX\pretrained\yolox_s.pth However, the APs have a lot of nan and 0. The log details are below.
2024-03-03 15:05:28 | INFO | yolox.core.trainer:130 - args: Namespace(experiment_name='yolox_s', name=None, dist_backend='nccl', dist_url=None, batch_size=32, devices=1, exp_file='exps/example/custom/yolox_s.py', resume=False, ckpt='C:\Users\olivi\YOLOX\pretrained\yolox_s.pth', start_epoch=None, num_machines=1, machine_rank=0, fp16=True, cache=None, occupy=True, logger='tensorboard', opts=[])
2024-03-03 15:05:28 | INFO | yolox.core.trainer:131 - exp value:
╒═══════════════════╤════════════════════════════╕
│ keys │ values │
╞═══════════════════╪════════════════════════════╡
│ seed │ None │
├───────────────────┼────────────────────────────┤
│ output_dir │ './YOLOX_outputs' │
├───────────────────┼────────────────────────────┤
│ print_interval │ 10 │
├───────────────────┼────────────────────────────┤
│ eval_interval │ 1 │
├───────────────────┼────────────────────────────┤
│ dataset │ None │
├───────────────────┼────────────────────────────┤
│ num_classes │ 71 │
├───────────────────┼────────────────────────────┤
│ depth │ 0.33 │
├───────────────────┼────────────────────────────┤
│ width │ 0.5 │
├───────────────────┼────────────────────────────┤
│ act │ 'silu' │
├───────────────────┼────────────────────────────┤
│ data_num_workers │ 4 │
├───────────────────┼────────────────────────────┤
│ input_size │ (640, 640) │
├───────────────────┼────────────────────────────┤
│ multiscale_range │ 5 │
├───────────────────┼────────────────────────────┤
│ data_dir │ 'datasets/coco128' │
├───────────────────┼────────────────────────────┤
│ train_ann │ 'instances_train2017.json' │
├───────────────────┼────────────────────────────┤
│ val_ann │ 'instances_val2017.json' │
├───────────────────┼────────────────────────────┤
│ test_ann │ 'instances_test2017.json' │
├───────────────────┼────────────────────────────┤
│ mosaic_prob │ 1.0 │
├───────────────────┼────────────────────────────┤
│ mixup_prob │ 1.0 │
├───────────────────┼────────────────────────────┤
│ hsv_prob │ 1.0 │
├───────────────────┼────────────────────────────┤
│ flip_prob │ 0.5 │
├───────────────────┼────────────────────────────┤
│ degrees │ 10.0 │
├───────────────────┼────────────────────────────┤
│ translate │ 0.1 │
├───────────────────┼────────────────────────────┤
│ mosaic_scale │ (0.1, 2) │
├───────────────────┼────────────────────────────┤
│ enable_mixup │ True │
├───────────────────┼────────────────────────────┤
│ mixup_scale │ (0.5, 1.5) │
├───────────────────┼────────────────────────────┤
│ shear │ 2.0 │
├───────────────────┼────────────────────────────┤
│ warmup_epochs │ 5 │
├───────────────────┼────────────────────────────┤
│ max_epoch │ 300 │
├───────────────────┼────────────────────────────┤
│ warmup_lr │ 0 │
├───────────────────┼────────────────────────────┤
│ min_lr_ratio │ 0.05 │
├───────────────────┼────────────────────────────┤
│ basic_lr_per_img │ 0.00015625 │
├───────────────────┼────────────────────────────┤
│ scheduler │ 'yoloxwarmcos' │
├───────────────────┼────────────────────────────┤
│ no_aug_epochs │ 15 │
├───────────────────┼────────────────────────────┤
│ ema │ True │
├───────────────────┼────────────────────────────┤
│ weight_decay │ 0.0005 │
├───────────────────┼────────────────────────────┤
│ momentum │ 0.9 │
├───────────────────┼────────────────────────────┤
│ save_history_ckpt │ True │
├───────────────────┼────────────────────────────┤
│ exp_name │ 'yolox_s' │
├───────────────────┼────────────────────────────┤
│ test_size │ (640, 640) │
├───────────────────┼────────────────────────────┤
│ test_conf │ 0.01 │
├───────────────────┼────────────────────────────┤
│ nmsthre │ 0.65 │
╘═══════════════════╧════════════════════════════╛
qt.qpa.fonts: Unable to open default EUDC font: "EUDC.TTE"
2024-03-03 15:05:34 | INFO | yolox.core.trainer:136 - Model Summary: Params: 8.96M, Gflops: 26.91
2024-03-03 15:05:34 | INFO | yolox.core.trainer:319 - loading checkpoint for fine tuning
2024-03-03 15:05:35 | WARNING | yolox.utils.checkpoint:24 - Shape of head.cls_preds.0.weight in checkpoint is torch.Size([80, 128, 1, 1]), while shape of head.cls_preds.0.weight in model is torch.Size([71, 128, 1, 1]).
2024-03-03 15:05:35 | WARNING | yolox.utils.checkpoint:24 - Shape of head.cls_preds.0.bias in checkpoint is torch.Size([80]), while shape of head.cls_preds.0.bias in model is torch.Size([71]).
2024-03-03 15:05:35 | WARNING | yolox.utils.checkpoint:24 - Shape of head.cls_preds.1.weight in checkpoint is torch.Size([80, 128, 1, 1]), while shape of head.cls_preds.1.weight in model is torch.Size([71, 128, 1, 1]).
2024-03-03 15:05:35 | WARNING | yolox.utils.checkpoint:24 - Shape of head.cls_preds.1.bias in checkpoint is torch.Size([80]), while shape of head.cls_preds.1.bias in model is torch.Size([71]).
2024-03-03 15:05:35 | WARNING | yolox.utils.checkpoint:24 - Shape of head.cls_preds.2.weight in checkpoint is torch.Size([80, 128, 1, 1]), while shape of head.cls_preds.2.weight in model is torch.Size([71, 128, 1, 1]).
2024-03-03 15:05:35 | WARNING | yolox.utils.checkpoint:24 - Shape of head.cls_preds.2.bias in checkpoint is torch.Size([80]), while shape of head.cls_preds.2.bias in model is torch.Size([71]).
2024-03-03 15:05:35 | INFO | yolox.data.datasets.coco:63 - loading annotations into memory...
2024-03-03 15:05:35 | INFO | yolox.data.datasets.coco:63 - Done (t=0.00s)
2024-03-03 15:05:35 | INFO | pycocotools.coco:86 - creating index...
2024-03-03 15:05:35 | INFO | pycocotools.coco:86 - index created!
2024-03-03 15:05:35 | INFO | yolox.core.trainer:155 - init prefetcher, this might take one minute or less...
C:\Users\olivi\yolox\yolox\utils\metric.py:43: UserWarning: The torch.cuda.DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=, device='cuda') to create tensors. (Triggered internally at ..\torch\csrc\tensor\python_tensor.cpp:85.)
x = torch.cuda.FloatTensor(256, 1024, block_mem)
2024-03-03 15:05:52 | INFO | yolox.data.datasets.coco:63 - loading annotations into memory...
2024-03-03 15:05:52 | INFO | yolox.data.datasets.coco:63 - Done (t=0.01s)
2024-03-03 15:05:52 | INFO | pycocotools.coco:86 - creating index...
2024-03-03 15:05:52 | INFO | pycocotools.coco:86 - index created!
2024-03-03 15:05:52 | INFO | yolox.core.trainer:191 - Training start...
2024-03-03 15:05:52 | INFO | yolox.core.trainer:192 -
YOLOX(
(backbone): YOLOPAFPN(
(backbone): CSPDarknet(
(stem): Focus(
(conv): BaseConv(
(conv): Conv2d(12, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
)
(dark2): Sequential(
(0): BaseConv(
(conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(1): CSPLayer(
(conv1): BaseConv(
(conv): Conv2d(64, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv2): BaseConv(
(conv): Conv2d(64, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv3): BaseConv(
(conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(m): Sequential(
(0): Bottleneck(
(conv1): BaseConv(
(conv): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv2): BaseConv(
(conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
)
)
)
)
(dark3): Sequential(
(0): BaseConv(
(conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(1): CSPLayer(
(conv1): BaseConv(
(conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv2): BaseConv(
(conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv3): BaseConv(
(conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(m): Sequential(
(0): Bottleneck(
(conv1): BaseConv(
(conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv2): BaseConv(
(conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
)
(1): Bottleneck(
(conv1): BaseConv(
(conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv2): BaseConv(
(conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
)
(2): Bottleneck(
(conv1): BaseConv(
(conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv2): BaseConv(
(conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
)
)
)
)
(dark4): Sequential(
(0): BaseConv(
(conv): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(1): CSPLayer(
(conv1): BaseConv(
(conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv2): BaseConv(
(conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv3): BaseConv(
(conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(m): Sequential(
(0): Bottleneck(
(conv1): BaseConv(
(conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv2): BaseConv(
(conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
)
(1): Bottleneck(
(conv1): BaseConv(
(conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv2): BaseConv(
(conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
)
(2): Bottleneck(
(conv1): BaseConv(
(conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv2): BaseConv(
(conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
)
)
)
)
(dark5): Sequential(
(0): BaseConv(
(conv): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(1): SPPBottleneck(
(conv1): BaseConv(
(conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(m): ModuleList(
(0): MaxPool2d(kernel_size=5, stride=1, padding=2, dilation=1, ceil_mode=False)
(1): MaxPool2d(kernel_size=9, stride=1, padding=4, dilation=1, ceil_mode=False)
(2): MaxPool2d(kernel_size=13, stride=1, padding=6, dilation=1, ceil_mode=False)
)
(conv2): BaseConv(
(conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
)
(2): CSPLayer(
(conv1): BaseConv(
(conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv2): BaseConv(
(conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv3): BaseConv(
(conv): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(m): Sequential(
(0): Bottleneck(
(conv1): BaseConv(
(conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv2): BaseConv(
(conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
)
)
)
)
)
(upsample): Upsample(scale_factor=2.0, mode='nearest')
(lateral_conv0): BaseConv(
(conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(C3_p4): CSPLayer(
(conv1): BaseConv(
(conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv2): BaseConv(
(conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv3): BaseConv(
(conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(m): Sequential(
(0): Bottleneck(
(conv1): BaseConv(
(conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv2): BaseConv(
(conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
)
)
)
(reduce_conv1): BaseConv(
(conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(C3_p3): CSPLayer(
(conv1): BaseConv(
(conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv2): BaseConv(
(conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv3): BaseConv(
(conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(m): Sequential(
(0): Bottleneck(
(conv1): BaseConv(
(conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv2): BaseConv(
(conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
)
)
)
(bu_conv2): BaseConv(
(conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(C3_n3): CSPLayer(
(conv1): BaseConv(
(conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv2): BaseConv(
(conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv3): BaseConv(
(conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(m): Sequential(
(0): Bottleneck(
(conv1): BaseConv(
(conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv2): BaseConv(
(conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
)
)
)
(bu_conv1): BaseConv(
(conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(C3_n4): CSPLayer(
(conv1): BaseConv(
(conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv2): BaseConv(
(conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv3): BaseConv(
(conv): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(m): Sequential(
(0): Bottleneck(
(conv1): BaseConv(
(conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(conv2): BaseConv(
(conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
)
)
)
)
(head): YOLOXHead(
(cls_convs): ModuleList(
(0-2): 3 x Sequential(
(0): BaseConv(
(conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(1): BaseConv(
(conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
)
)
(reg_convs): ModuleList(
(0-2): 3 x Sequential(
(0): BaseConv(
(conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(1): BaseConv(
(conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
)
)
(cls_preds): ModuleList(
(0-2): 3 x Conv2d(128, 71, kernel_size=(1, 1), stride=(1, 1))
)
(reg_preds): ModuleList(
(0-2): 3 x Conv2d(128, 4, kernel_size=(1, 1), stride=(1, 1))
)
(obj_preds): ModuleList(
(0-2): 3 x Conv2d(128, 1, kernel_size=(1, 1), stride=(1, 1))
)
(stems): ModuleList(
(0): BaseConv(
(conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(1): BaseConv(
(conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
(2): BaseConv(
(conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)
(act): SiLU(inplace=True)
)
)
(l1_loss): L1Loss()
(bcewithlog_loss): BCEWithLogitsLoss()
(iou_loss): IOUloss()
)
)
2024-03-03 15:05:52 | INFO | yolox.core.trainer:203 - ---> start train epoch1
2024-03-03 15:05:59 | INFO | yolox.core.trainer:364 - Save weights to ./YOLOX_outputs\yolox_s
100%|####################################################################################| 1/1 [00:05<00:00, 5.11s/it]
2024-03-03 15:06:04 | INFO | yolox.evaluators.coco_evaluator:259 - Evaluate in main process...
2024-03-03 15:06:04 | INFO | yolox.evaluators.coco_evaluator:292 - Loading and preparing results...
2024-03-03 15:06:04 | INFO | yolox.evaluators.coco_evaluator:292 - DONE (t=0.01s)
2024-03-03 15:06:04 | INFO | pycocotools.coco:366 - creating index...
2024-03-03 15:06:04 | INFO | pycocotools.coco:366 - index created!
2024-03-03 15:06:04 | INFO | yolox.evaluators.coco_evaluator:302 - Running per image evaluation...
2024-03-03 15:06:04 | INFO | yolox.evaluators.coco_evaluator:302 - Evaluate annotation type bbox
2024-03-03 15:06:04 | INFO | yolox.evaluators.coco_evaluator:302 - DONE (t=0.01s).
2024-03-03 15:06:04 | INFO | yolox.evaluators.coco_evaluator:303 - Accumulating evaluation results...
2024-03-03 15:06:04 | INFO | yolox.evaluators.coco_evaluator:303 - DONE (t=0.03s).
2024-03-03 15:06:04 | INFO | yolox.core.trainer:354 -
Average forward time: 0.00 ms, Average NMS time: 0.00 ms, Average inference time: 0.00 ms
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.092
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.103
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.103
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.060
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.100
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.069
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.138
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.138
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.180
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.100
per class AP:
2024-03-03 15:06:04 | INFO | yolox.core.trainer:364 - Save weights to ./YOLOX_outputs\yolox_s
2024-03-03 15:06:05 | INFO | yolox.core.trainer:364 - Save weights to ./YOLOX_outputs\yolox_s
2024-03-03 15:06:05 | INFO | yolox.core.trainer:203 - ---> start train epoch2
2024-03-03 15:06:07 | INFO | yolox.core.trainer:364 - Save weights to ./YOLOX_outputs\yolox_s
100%|####################################################################################| 1/1 [00:05<00:00, 5.63s/it]
2024-03-03 15:06:13 | INFO | yolox.evaluators.coco_evaluator:259 - Evaluate in main process...
2024-03-03 15:06:13 | INFO | yolox.evaluators.coco_evaluator:292 - Loading and preparing results...
2024-03-03 15:06:13 | INFO | yolox.evaluators.coco_evaluator:292 - DONE (t=0.01s)
2024-03-03 15:06:13 | INFO | pycocotools.coco:366 - creating index...
2024-03-03 15:06:13 | INFO | pycocotools.coco:366 - index created!
2024-03-03 15:06:13 | INFO | yolox.evaluators.coco_evaluator:302 - Running per image evaluation...
2024-03-03 15:06:13 | INFO | yolox.evaluators.coco_evaluator:302 - Evaluate annotation type bbox
2024-03-03 15:06:13 | INFO | yolox.evaluators.coco_evaluator:302 - DONE (t=0.01s).
2024-03-03 15:06:13 | INFO | yolox.evaluators.coco_evaluator:303 - Accumulating evaluation results...
2024-03-03 15:06:13 | INFO | yolox.evaluators.coco_evaluator:303 - DONE (t=0.03s).
2024-03-03 15:06:13 | INFO | yolox.core.trainer:354 -
Average forward time: 0.00 ms, Average NMS time: 0.00 ms, Average inference time: 0.00 ms
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.115
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.154
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.154
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.120
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.100
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.115
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.115
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.115
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.120
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.100
per class AP:
2024-03-03 15:06:13 | INFO | yolox.core.trainer:364 - Save weights to ./YOLOX_outputs\yolox_s
2024-03-03 15:06:14 | INFO | yolox.core.trainer:364 - Save weights to ./YOLOX_outputs\yolox_s
2024-03-03 15:06:14 | INFO | yolox.core.trainer:203 - ---> start train epoch3
2024-03-03 15:06:20 | INFO | yolox.core.trainer:364 - Save weights to ./YOLOX_outputs\yolox_s
100%|####################################################################################| 1/1 [00:04<00:00, 4.98s/it]
2024-03-03 15:06:25 | INFO | yolox.evaluators.coco_evaluator:259 - Evaluate in main process...
2024-03-03 15:06:25 | INFO | yolox.evaluators.coco_evaluator:292 - Loading and preparing results...
2024-03-03 15:06:25 | INFO | yolox.evaluators.coco_evaluator:292 - DONE (t=0.01s)
2024-03-03 15:06:25 | INFO | pycocotools.coco:366 - creating index...
2024-03-03 15:06:25 | INFO | pycocotools.coco:366 - index created!
2024-03-03 15:06:25 | INFO | yolox.evaluators.coco_evaluator:302 - Running per image evaluation...
2024-03-03 15:06:25 | INFO | yolox.evaluators.coco_evaluator:302 - Evaluate annotation type bbox
2024-03-03 15:06:25 | INFO | yolox.evaluators.coco_evaluator:302 - DONE (t=0.01s).
2024-03-03 15:06:25 | INFO | yolox.evaluators.coco_evaluator:303 - Accumulating evaluation results...
2024-03-03 15:06:25 | INFO | yolox.evaluators.coco_evaluator:303 - DONE (t=0.02s).
2024-03-03 15:06:25 | INFO | yolox.core.trainer:354 -
Average forward time: 0.00 ms, Average NMS time: 0.00 ms, Average inference time: 0.00 ms
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.031
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.038
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.038
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.044
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.062
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.062
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.062
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.089
per class AP:
2024-03-03 15:06:25 | INFO | yolox.core.trainer:364 - Save weights to ./YOLOX_outputs\yolox_s
2024-03-03 15:06:25 | INFO | yolox.core.trainer:364 - Save weights to ./YOLOX_outputs\yolox_s
2024-03-03 15:06:25 | INFO | yolox.core.trainer:203 - ---> start train epoch4
2024-03-03 15:06:28 | INFO | yolox.core.trainer:364 - Save weights to ./YOLOX_outputs\yolox_s
100%|####################################################################################| 1/1 [00:05<00:00, 5.17s/it]
2024-03-03 15:06:33 | INFO | yolox.evaluators.coco_evaluator:259 - Evaluate in main process...
2024-03-03 15:06:33 | INFO | yolox.evaluators.coco_evaluator:292 - Loading and preparing results...
2024-03-03 15:06:33 | INFO | yolox.evaluators.coco_evaluator:292 - DONE (t=0.01s)
2024-03-03 15:06:33 | INFO | pycocotools.coco:366 - creating index...
2024-03-03 15:06:33 | INFO | pycocotools.coco:366 - index created!
2024-03-03 15:06:33 | INFO | yolox.evaluators.coco_evaluator:302 - Running per image evaluation...
2024-03-03 15:06:33 | INFO | yolox.evaluators.coco_evaluator:302 - Evaluate annotation type bbox
2024-03-03 15:06:33 | INFO | yolox.evaluators.coco_evaluator:302 - DONE (t=0.01s).
2024-03-03 15:06:33 | INFO | yolox.evaluators.coco_evaluator:303 - Accumulating evaluation results...
2024-03-03 15:06:33 | INFO | yolox.evaluators.coco_evaluator:303 - DONE (t=0.02s).
2024-03-03 15:06:33 | INFO | yolox.core.trainer:354 -
Average forward time: 0.00 ms, Average NMS time: 0.00 ms, Average inference time: 0.00 ms
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
per class AP:
2024-03-03 15:06:33 | INFO | yolox.core.trainer:364 - Save weights to ./YOLOX_outputs\yolox_s
2024-03-03 15:06:33 | INFO | yolox.core.trainer:364 - Save weights to ./YOLOX_outputs\yolox_s
2024-03-03 15:06:33 | INFO | yolox.core.trainer:203 - ---> start train epoch5
2024-03-03 15:06:36 | INFO | yolox.core.trainer:364 - Save weights to ./YOLOX_outputs\yolox_s
100%|####################################################################################| 1/1 [00:05<00:00, 5.09s/it]
2024-03-03 15:06:41 | INFO | yolox.evaluators.coco_evaluator:259 - Evaluate in main process...
2024-03-03 15:06:41 | INFO | yolox.evaluators.coco_evaluator:292 - Loading and preparing results...
2024-03-03 15:06:41 | INFO | yolox.evaluators.coco_evaluator:292 - DONE (t=0.01s)
2024-03-03 15:06:41 | INFO | pycocotools.coco:366 - creating index...
2024-03-03 15:06:41 | INFO | pycocotools.coco:366 - index created!
2024-03-03 15:06:41 | INFO | yolox.evaluators.coco_evaluator:302 - Running per image evaluation...
2024-03-03 15:06:41 | INFO | yolox.evaluators.coco_evaluator:302 - Evaluate annotation type bbox
2024-03-03 15:06:41 | INFO | yolox.evaluators.coco_evaluator:302 - DONE (t=0.02s).
2024-03-03 15:06:41 | INFO | yolox.evaluators.coco_evaluator:303 - Accumulating evaluation results...
2024-03-03 15:06:41 | INFO | yolox.evaluators.coco_evaluator:303 - DONE (t=0.03s).
2024-03-03 15:06:41 | INFO | yolox.core.trainer:354 -
Average forward time: 0.00 ms, Average NMS time: 0.00 ms, Average inference time: 0.00 ms
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000
per class AP:
2024-03-03 15:06:41 | INFO | yolox.core.trainer:364 - Save weights to ./YOLOX_outputs\yolox_s
2024-03-03 15:06:42 | INFO | yolox.core.trainer:364 - Save weights to ./YOLOX_outputs\yolox_s
2024-03-03 15:06:42 | INFO | yolox.core.trainer:203 - ---> start train epoch6
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