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rickysambora55
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human-count-yolov8

Object Detection
TensorBoard
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rickysambora55 commited on
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8149867
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1 Parent(s): 1545715

model update

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  1. runs/detect/train-36/args.yaml +98 -0
  2. runs/detect/train-36/events.out.tfevents.1716859362.19783cab3b86.1944.0 +3 -0
  3. runs/detect/{train-100 → train-36}/labels.jpg +0 -0
  4. runs/detect/{train-100 → train-36}/labels_correlogram.jpg +0 -0
  5. runs/detect/train-36/results.csv +37 -0
  6. runs/detect/{train-100 → train-36}/train_batch0.jpg +0 -0
  7. runs/detect/{train-100 → train-36}/train_batch1.jpg +0 -0
  8. runs/detect/{train-100 → train-36}/train_batch2.jpg +0 -0
  9. runs/detect/train-36/weights/best.pt +3 -0
  10. runs/detect/train-36/weights/last.pt +3 -0
  11. runs/detect/train/args.yaml +4 -4
  12. runs/detect/train/events.out.tfevents.1716859362.19783cab3b86.1944.0 +3 -0
  13. runs/detect/train/results.csv +36 -100
  14. runs/detect/train/weights/best.pt +2 -2
  15. runs/detect/train/weights/last.pt +2 -2
  16. {runs → runs2}/detect/train-100/args.yaml +0 -0
  17. {runs → runs2}/detect/train-100/events.out.tfevents.1716697493.3afb8ba695c7.2167.0 +0 -0
  18. {runs → runs2}/detect/train-100/events.out.tfevents.1716788417.cae1f20d5087.3932.0 +0 -0
  19. {runs → runs2}/detect/train-100/events.out.tfevents.1716788474.cae1f20d5087.4246.0 +0 -0
  20. {runs → runs2}/detect/train-100/events.out.tfevents.1716788519.cae1f20d5087.4476.0 +0 -0
  21. {runs → runs2}/detect/train-100/events.out.tfevents.1716807013.e457cdfd2c72.3827.0 +0 -0
  22. {runs/detect/train-34 → runs2/detect/train-100}/labels.jpg +0 -0
  23. {runs/detect/train-34 → runs2/detect/train-100}/labels_correlogram.jpg +0 -0
  24. {runs → runs2}/detect/train-100/results.csv +0 -0
  25. {runs/detect/train-34 → runs2/detect/train-100}/train_batch0.jpg +0 -0
  26. {runs/detect/train-34 → runs2/detect/train-100}/train_batch1.jpg +0 -0
  27. {runs/detect/train-34 → runs2/detect/train-100}/train_batch2.jpg +0 -0
  28. {runs → runs2}/detect/train-100/weights/best.pt +0 -0
  29. {runs → runs2}/detect/train-100/weights/last.pt +0 -0
  30. {runs → runs2}/detect/train-34/args.yaml +0 -0
  31. {runs → runs2}/detect/train-34/events.out.tfevents.1716697493.3afb8ba695c7.2167.0 +0 -0
  32. {runs/detect/train-68 → runs2/detect/train-34}/labels.jpg +0 -0
  33. {runs/detect/train-68 → runs2/detect/train-34}/labels_correlogram.jpg +0 -0
  34. {runs → runs2}/detect/train-34/results.csv +0 -0
  35. {runs/detect/train-68 → runs2/detect/train-34}/train_batch0.jpg +0 -0
  36. {runs/detect/train-68 → runs2/detect/train-34}/train_batch1.jpg +0 -0
  37. {runs/detect/train-68 → runs2/detect/train-34}/train_batch2.jpg +0 -0
  38. {runs → runs2}/detect/train-34/weights/best.pt +0 -0
  39. {runs → runs2}/detect/train-34/weights/last.pt +0 -0
  40. {runs → runs2}/detect/train-68/args.yaml +0 -0
  41. {runs → runs2}/detect/train-68/events.out.tfevents.1716697493.3afb8ba695c7.2167.0 +0 -0
  42. {runs → runs2}/detect/train-68/events.out.tfevents.1716788417.cae1f20d5087.3932.0 +0 -0
  43. {runs → runs2}/detect/train-68/events.out.tfevents.1716788474.cae1f20d5087.4246.0 +0 -0
  44. {runs → runs2}/detect/train-68/events.out.tfevents.1716788519.cae1f20d5087.4476.0 +0 -0
  45. runs2/detect/train-68/labels.jpg +0 -0
  46. runs2/detect/train-68/labels_correlogram.jpg +0 -0
  47. {runs → runs2}/detect/train-68/results.csv +0 -0
  48. runs2/detect/train-68/train_batch0.jpg +0 -0
  49. runs2/detect/train-68/train_batch1.jpg +0 -0
  50. runs2/detect/train-68/train_batch2.jpg +0 -0
runs/detect/train-36/args.yaml ADDED
@@ -0,0 +1,98 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ task: detect
2
+ mode: train
3
+ model: yolov8n.pt
4
+ data: /content/datasets/Crowd-Detection-7//data.yaml
5
+ epochs: 100
6
+ patience: 50
7
+ batch: 32
8
+ imgsz: 640
9
+ save: true
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+ save_period: -1
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+ cache: true
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+ device: null
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+ workers: 16
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+ project: null
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+ name: null
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+ exist_ok: false
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+ pretrained: true
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+ optimizer: auto
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+ verbose: true
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+ seed: 0
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+ deterministic: true
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+ single_cls: false
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+ rect: false
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+ cos_lr: true
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+ close_mosaic: 10
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+ resume: false
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+ amp: true
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+ fraction: 1.0
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+ profile: false
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+ freeze: 10
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+ overlap_mask: true
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+ mask_ratio: 4
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+ dropout: 0.0
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+ val: true
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+ split: val
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+ save_json: false
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+ save_hybrid: false
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+ conf: null
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+ iou: 0.7
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+ max_det: 300
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+ half: false
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+ dnn: false
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+ plots: true
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+ source: null
45
+ show: false
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+ save_txt: false
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+ save_conf: false
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+ save_crop: false
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+ show_labels: true
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+ show_conf: true
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+ vid_stride: 1
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+ stream_buffer: false
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+ line_width: null
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+ visualize: false
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+ augment: false
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+ agnostic_nms: false
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+ classes: null
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+ retina_masks: false
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+ boxes: true
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+ format: torchscript
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+ keras: false
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+ optimize: false
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+ int8: false
64
+ dynamic: false
65
+ simplify: false
66
+ opset: null
67
+ workspace: 4
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+ nms: false
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+ lr0: 0.01
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+ lrf: 0.01
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+ momentum: 0.937
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+ weight_decay: 0.0005
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+ warmup_epochs: 3.0
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+ warmup_momentum: 0.8
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+ warmup_bias_lr: 0.1
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+ box: 7.5
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+ cls: 0.5
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+ dfl: 1.5
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+ pose: 12.0
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+ kobj: 1.0
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+ label_smoothing: 0.0
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+ nbs: 64
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+ hsv_h: 0.015
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+ hsv_s: 0.7
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+ hsv_v: 0.4
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+ degrees: 0.0
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+ translate: 0.1
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+ scale: 0.5
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+ shear: 0.0
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+ perspective: 0.0
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+ flipud: 0.0
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+ fliplr: 0.5
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+ mosaic: 1.0
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+ mixup: 0.0
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+ copy_paste: 0.0
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+ cfg: null
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+ tracker: botsort.yaml
98
+ save_dir: runs/detect/train
runs/detect/train-36/events.out.tfevents.1716859362.19783cab3b86.1944.0 ADDED
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runs/detect/{train-100 → train-36}/labels.jpg RENAMED
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runs/detect/{train-100 → train-36}/labels_correlogram.jpg RENAMED
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runs/detect/train-36/results.csv ADDED
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+ epoch, train/box_loss, train/cls_loss, train/dfl_loss, metrics/precision(B), metrics/recall(B), metrics/mAP50(B), metrics/mAP50-95(B), val/box_loss, val/cls_loss, val/dfl_loss, lr/pg0, lr/pg1, lr/pg2
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runs/detect/{train-100 → train-36}/train_batch0.jpg RENAMED
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runs/detect/{train-100 → train-36}/train_batch1.jpg RENAMED
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runs/detect/{train-100 → train-36}/train_batch2.jpg RENAMED
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runs/detect/train-36/weights/best.pt ADDED
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runs/detect/train/args.yaml CHANGED
@@ -1,8 +1,8 @@
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  task: detect
2
  mode: train
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- model: /content/runs/detect/train/weights/last.pt
4
- data: /content/datasets/Crowd-Detection-6//data.yaml
5
- epochs: 300
6
  patience: 50
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  batch: 32
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  imgsz: 640
@@ -72,7 +72,7 @@ momentum: 0.937
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  weight_decay: 0.0005
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  warmup_epochs: 3.0
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  warmup_momentum: 0.8
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- warmup_bias_lr: 0.0
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  box: 7.5
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  cls: 0.5
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  dfl: 1.5
 
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  task: detect
2
  mode: train
3
+ model: yolov8n.pt
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+ data: /content/datasets/Crowd-Detection-7//data.yaml
5
+ epochs: 100
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  patience: 50
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  batch: 32
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  imgsz: 640
 
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  weight_decay: 0.0005
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  warmup_epochs: 3.0
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  warmup_momentum: 0.8
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+ warmup_bias_lr: 0.1
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  box: 7.5
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  cls: 0.5
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  dfl: 1.5
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  epoch, train/box_loss, train/cls_loss, train/dfl_loss, metrics/precision(B), metrics/recall(B), metrics/mAP50(B), metrics/mAP50-95(B), val/box_loss, val/cls_loss, val/dfl_loss, lr/pg0, lr/pg1, lr/pg2
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- 1, 1.4335, 1.5766, 1.1566, 0.85784, 0.75756, 0.83502, 0.49834, 1.3431, 0.97709, 1.1574, 0.0033238, 0.0033238, 0.0033238
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- 6, 1.2877, 0.74631, 1.0307, 0.92319, 0.82325, 0.89742, 0.57359, 1.238, 0.66939, 1.0597, 0.0099957, 0.0099957, 0.0099957
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- 9, 1.252, 0.68865, 1.0138, 0.92783, 0.83457, 0.90558, 0.58547, 1.2002, 0.63444, 1.0469, 0.0099867, 0.0099867, 0.0099867
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- 10, 1.2403, 0.68034, 1.0116, 0.92913, 0.83927, 0.90944, 0.59446, 1.1908, 0.61189, 1.035, 0.0099826, 0.0099826, 0.0099826
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- 16, 1.2108, 0.64512, 0.99921, 0.93518, 0.84403, 0.91628, 0.60533, 1.1681, 0.5892, 1.0288, 0.0099469, 0.0099469, 0.0099469
18
- 17, 1.2093, 0.64172, 0.99757, 0.93102, 0.84612, 0.91557, 0.60616, 1.1721, 0.57968, 1.0279, 0.0099391, 0.0099391, 0.0099391
19
- 18, 1.2015, 0.64342, 0.99132, 0.93805, 0.84483, 0.91719, 0.60749, 1.1603, 0.57936, 1.019, 0.0099307, 0.0099307, 0.0099307
20
- 19, 1.1962, 0.63053, 0.98926, 0.9348, 0.84739, 0.91718, 0.60838, 1.162, 0.5778, 1.0252, 0.0099218, 0.0099218, 0.0099218
21
- 20, 1.1995, 0.63097, 0.99083, 0.93405, 0.85285, 0.918, 0.60987, 1.1578, 0.56608, 1.0205, 0.0099123, 0.0099123, 0.0099123
22
- 21, 1.1901, 0.62357, 0.99039, 0.93841, 0.85537, 0.91987, 0.61247, 1.1564, 0.56127, 1.0221, 0.0099023, 0.0099023, 0.0099023
23
- 22, 1.1848, 0.62238, 0.98827, 0.94337, 0.84642, 0.91991, 0.61157, 1.1535, 0.56312, 1.0187, 0.0098918, 0.0098918, 0.0098918
24
- 23, 1.193, 0.62561, 0.98915, 0.94145, 0.85301, 0.91929, 0.61146, 1.1603, 0.55491, 1.0181, 0.0098808, 0.0098808, 0.0098808
25
- 24, 1.1836, 0.61714, 0.98768, 0.94499, 0.8491, 0.92237, 0.61786, 1.1479, 0.55332, 1.0142, 0.0098692, 0.0098692, 0.0098692
26
- 25, 1.1822, 0.62724, 0.98622, 0.93978, 0.85547, 0.92248, 0.6171, 1.1488, 0.55586, 1.0152, 0.0098571, 0.0098571, 0.0098571
27
- 26, 1.1766, 0.61251, 0.98154, 0.94536, 0.85305, 0.9235, 0.61642, 1.1487, 0.55156, 1.0148, 0.0098445, 0.0098445, 0.0098445
28
- 27, 1.1796, 0.61584, 0.98368, 0.94883, 0.85254, 0.92396, 0.61917, 1.145, 0.5479, 1.0133, 0.0098313, 0.0098313, 0.0098313
29
- 28, 1.1737, 0.61072, 0.98185, 0.9466, 0.85349, 0.9231, 0.61872, 1.1409, 0.55104, 1.0114, 0.0098177, 0.0098177, 0.0098177
30
- 29, 1.1717, 0.60952, 0.98021, 0.93914, 0.85844, 0.92405, 0.6195, 1.1441, 0.54759, 1.011, 0.0098035, 0.0098035, 0.0098035
31
- 30, 1.1679, 0.60809, 0.97941, 0.94446, 0.85851, 0.92494, 0.62052, 1.1422, 0.54463, 1.009, 0.0097887, 0.0097887, 0.0097887
32
- 31, 1.1673, 0.60405, 0.97933, 0.9437, 0.8557, 0.92499, 0.62146, 1.1435, 0.54106, 1.011, 0.0097735, 0.0097735, 0.0097735
33
- 32, 1.1708, 0.6032, 0.97908, 0.94569, 0.85906, 0.92521, 0.62214, 1.1377, 0.53609, 1.0095, 0.0097577, 0.0097577, 0.0097577
34
- 33, 1.1648, 0.60485, 0.98036, 0.94526, 0.85694, 0.92546, 0.62048, 1.1392, 0.53429, 1.0109, 0.0097415, 0.0097415, 0.0097415
35
- 34, 1.1678, 0.6022, 0.97632, 0.94857, 0.85323, 0.92615, 0.6224, 1.1374, 0.53202, 1.0062, 0.0097247, 0.0097247, 0.0097247
36
- 35, 1.1663, 0.59825, 0.97873, 0.94471, 0.85942, 0.9262, 0.62308, 1.1359, 0.53112, 1.0064, 0.0097074, 0.0097074, 0.0097074
37
- 36, 1.1581, 0.59683, 0.97449, 0.94696, 0.85713, 0.92603, 0.6215, 1.1403, 0.5345, 1.0079, 0.0096895, 0.0096895, 0.0096895
38
- 37, 1.166, 0.59951, 0.97735, 0.94404, 0.86251, 0.927, 0.62347, 1.1373, 0.52728, 1.0043, 0.0096712, 0.0096712, 0.0096712
39
- 38, 1.1543, 0.59308, 0.97245, 0.94637, 0.85969, 0.92665, 0.62294, 1.1371, 0.52619, 1.0051, 0.0096524, 0.0096524, 0.0096524
40
- 39, 1.1487, 0.59334, 0.97038, 0.94846, 0.86021, 0.92698, 0.6246, 1.1363, 0.52676, 1.0039, 0.0096331, 0.0096331, 0.0096331
41
- 40, 1.1515, 0.59287, 0.97138, 0.94713, 0.86419, 0.92808, 0.62488, 1.1329, 0.52578, 1.0053, 0.0096132, 0.0096132, 0.0096132
42
- 41, 1.151, 0.59163, 0.97243, 0.94609, 0.8641, 0.92799, 0.62562, 1.1351, 0.52323, 1.0054, 0.0095929, 0.0095929, 0.0095929
43
- 42, 1.1439, 0.58638, 0.96987, 0.95225, 0.86054, 0.92838, 0.62734, 1.1312, 0.51917, 1.0031, 0.0095721, 0.0095721, 0.0095721
44
- 43, 1.1491, 0.58643, 0.96781, 0.94408, 0.86605, 0.92764, 0.62608, 1.1324, 0.51921, 1.0027, 0.0095507, 0.0095507, 0.0095507
45
- 44, 1.1475, 0.58444, 0.96805, 0.94441, 0.86446, 0.92809, 0.62678, 1.1272, 0.51821, 0.99918, 0.0095289, 0.0095289, 0.0095289
46
- 45, 1.1449, 0.58773, 0.96674, 0.95093, 0.86183, 0.92919, 0.62647, 1.1282, 0.51844, 0.99949, 0.0095066, 0.0095066, 0.0095066
47
- 46, 1.1413, 0.58333, 0.96878, 0.94415, 0.86295, 0.92887, 0.62883, 1.1264, 0.51817, 1.0003, 0.0094838, 0.0094838, 0.0094838
48
- 47, 1.1427, 0.58319, 0.96457, 0.94855, 0.86172, 0.92954, 0.62926, 1.1262, 0.51681, 0.99813, 0.0094605, 0.0094605, 0.0094605
49
- 48, 1.1409, 0.58524, 0.96505, 0.94464, 0.86543, 0.92982, 0.62916, 1.1249, 0.51479, 0.99799, 0.0094367, 0.0094367, 0.0094367
50
- 49, 1.1419, 0.5836, 0.96537, 0.94877, 0.86295, 0.92958, 0.63015, 1.1263, 0.51439, 1.0001, 0.0094125, 0.0094125, 0.0094125
51
- 50, 1.1409, 0.5817, 0.96731, 0.94698, 0.86622, 0.93053, 0.63041, 1.1242, 0.51375, 0.99847, 0.0093877, 0.0093877, 0.0093877
52
- 51, 1.1439, 0.58302, 0.96668, 0.94852, 0.86525, 0.93023, 0.63023, 1.1232, 0.51488, 0.99708, 0.0093625, 0.0093625, 0.0093625
53
- 52, 1.1374, 0.58483, 0.96245, 0.94608, 0.86667, 0.93066, 0.63, 1.1233, 0.51344, 0.99724, 0.0093368, 0.0093368, 0.0093368
54
- 53, 1.1337, 0.57658, 0.96017, 0.94697, 0.86578, 0.9308, 0.62933, 1.1239, 0.51091, 0.99831, 0.0093107, 0.0093107, 0.0093107
55
- 54, 1.1397, 0.57934, 0.96293, 0.94619, 0.86598, 0.9303, 0.63093, 1.1233, 0.50955, 0.99803, 0.0092841, 0.0092841, 0.0092841
56
- 55, 1.131, 0.57354, 0.96185, 0.95052, 0.86437, 0.93119, 0.63107, 1.1223, 0.50869, 0.99701, 0.009257, 0.009257, 0.009257
57
- 56, 1.1278, 0.57419, 0.96155, 0.95034, 0.86465, 0.93127, 0.63139, 1.1215, 0.5073, 0.99613, 0.0092294, 0.0092294, 0.0092294
58
- 57, 1.1395, 0.5818, 0.96473, 0.9472, 0.86578, 0.93089, 0.63126, 1.1217, 0.5065, 0.99657, 0.0092014, 0.0092014, 0.0092014
59
- 58, 1.1295, 0.57355, 0.96181, 0.94743, 0.86676, 0.93138, 0.63225, 1.1195, 0.50623, 0.99513, 0.009173, 0.009173, 0.009173
60
- 59, 1.129, 0.58871, 0.95986, 0.94848, 0.86684, 0.93168, 0.63249, 1.1226, 0.50641, 0.9967, 0.009144, 0.009144, 0.009144
61
- 60, 1.1258, 0.57093, 0.95832, 0.9515, 0.86587, 0.93136, 0.63188, 1.122, 0.50568, 0.997, 0.0091147, 0.0091147, 0.0091147
62
- 61, 1.1305, 0.57516, 0.95937, 0.95246, 0.86499, 0.93109, 0.63229, 1.1202, 0.50453, 0.99557, 0.0090849, 0.0090849, 0.0090849
63
- 62, 1.1228, 0.5705, 0.95841, 0.95155, 0.86667, 0.93183, 0.63342, 1.1183, 0.50394, 0.99434, 0.0090546, 0.0090546, 0.0090546
64
- 63, 1.1263, 0.57116, 0.95633, 0.95356, 0.86597, 0.93227, 0.63415, 1.1172, 0.50235, 0.99271, 0.0090239, 0.0090239, 0.0090239
65
- 64, 1.1227, 0.57118, 0.95717, 0.95544, 0.86331, 0.93259, 0.63451, 1.1177, 0.50169, 0.994, 0.0089928, 0.0089928, 0.0089928
66
- 65, 1.1226, 0.56792, 0.95924, 0.94836, 0.86879, 0.93207, 0.63389, 1.1182, 0.50114, 0.99463, 0.0089613, 0.0089613, 0.0089613
67
- 66, 1.1263, 0.5693, 0.95827, 0.9504, 0.86711, 0.93199, 0.6341, 1.118, 0.50091, 0.9941, 0.0089293, 0.0089293, 0.0089293
68
- 67, 1.1229, 0.57044, 0.96067, 0.95026, 0.8666, 0.9316, 0.63405, 1.1174, 0.50068, 0.99379, 0.0088969, 0.0088969, 0.0088969
69
- 68, 1.1263, 0.57216, 0.95764, 0.95177, 0.86713, 0.93281, 0.63463, 1.1173, 0.49944, 0.99409, 0.008864, 0.008864, 0.008864
70
- 69, 1.1189, 0.56191, 0.95659, 0.9501, 0.86737, 0.93265, 0.63516, 1.1171, 0.49899, 0.9927, 0.0088308, 0.0088308, 0.0088308
71
- 70, 1.1158, 0.56427, 0.95516, 0.94973, 0.86897, 0.93266, 0.63524, 1.1173, 0.49963, 0.9927, 0.0087971, 0.0087971, 0.0087971
72
- 71, 1.125, 0.5683, 0.95967, 0.94782, 0.87038, 0.93258, 0.6348, 1.1184, 0.49933, 0.99322, 0.008763, 0.008763, 0.008763
73
- 72, 1.117, 0.56491, 0.95721, 0.94924, 0.86994, 0.93344, 0.6355, 1.1182, 0.49932, 0.99347, 0.0087286, 0.0087286, 0.0087286
74
- 73, 1.111, 0.56511, 0.95383, 0.94746, 0.86941, 0.93306, 0.63516, 1.1186, 0.49962, 0.99337, 0.0086937, 0.0086937, 0.0086937
75
- 74, 1.1147, 0.56843, 0.95641, 0.95124, 0.86767, 0.93322, 0.63402, 1.119, 0.49937, 0.9943, 0.0086584, 0.0086584, 0.0086584
76
- 75, 1.115, 0.56479, 0.956, 0.94912, 0.86941, 0.93327, 0.63455, 1.1187, 0.4992, 0.99458, 0.0086227, 0.0086227, 0.0086227
77
- 76, 1.1081, 0.56123, 0.95244, 0.95139, 0.86826, 0.93343, 0.63469, 1.1179, 0.49809, 0.99394, 0.0085866, 0.0085866, 0.0085866
78
- 77, 1.1137, 0.56035, 0.95312, 0.94608, 0.87182, 0.93342, 0.63487, 1.1177, 0.49743, 0.9935, 0.0085502, 0.0085502, 0.0085502
79
- 78, 1.1134, 0.56038, 0.95294, 0.94775, 0.87029, 0.93337, 0.63488, 1.1175, 0.49741, 0.9929, 0.0085133, 0.0085133, 0.0085133
80
- 79, 1.1115, 0.56508, 0.95191, 0.95068, 0.86888, 0.93341, 0.63525, 1.117, 0.49684, 0.99214, 0.0084761, 0.0084761, 0.0084761
81
- 80, 1.109, 0.55935, 0.95279, 0.94913, 0.8702, 0.93345, 0.63579, 1.1164, 0.49607, 0.9916, 0.0084385, 0.0084385, 0.0084385
82
- 81, 1.1077, 0.55876, 0.94903, 0.94979, 0.86981, 0.93322, 0.63569, 1.1163, 0.49607, 0.99139, 0.0084005, 0.0084005, 0.0084005
83
- 82, 1.1092, 0.56171, 0.95076, 0.95049, 0.86915, 0.93324, 0.63516, 1.1162, 0.49571, 0.99119, 0.0083622, 0.0083622, 0.0083622
84
- 83, 1.1096, 0.55977, 0.95022, 0.9517, 0.86905, 0.93269, 0.63529, 1.1164, 0.4953, 0.99113, 0.0083235, 0.0083235, 0.0083235
85
- 84, 1.1088, 0.5587, 0.9525, 0.95258, 0.8685, 0.93279, 0.63512, 1.1159, 0.4956, 0.99099, 0.0082844, 0.0082844, 0.0082844
86
- 85, 1.113, 0.56013, 0.95269, 0.95078, 0.86967, 0.93259, 0.63542, 1.1162, 0.49499, 0.99102, 0.008245, 0.008245, 0.008245
87
- 86, 1.1057, 0.56388, 0.94772, 0.95333, 0.86764, 0.93278, 0.63501, 1.1158, 0.49519, 0.9908, 0.0082052, 0.0082052, 0.0082052
88
- 87, 1.1024, 0.55535, 0.94773, 0.952, 0.86773, 0.93276, 0.63499, 1.116, 0.49513, 0.99099, 0.0081651, 0.0081651, 0.0081651
89
- 88, 1.1095, 0.55862, 0.94901, 0.95148, 0.86871, 0.93279, 0.63521, 1.116, 0.49481, 0.99105, 0.0081247, 0.0081247, 0.0081247
90
- 89, 1.1031, 0.55324, 0.94974, 0.95214, 0.86861, 0.93278, 0.63552, 1.116, 0.49462, 0.99117, 0.0080839, 0.0080839, 0.0080839
91
- 90, 1.0993, 0.55368, 0.94862, 0.95111, 0.86879, 0.9328, 0.63548, 1.1156, 0.49429, 0.99085, 0.0080428, 0.0080428, 0.0080428
92
- 91, 1.11, 0.56141, 0.95152, 0.95146, 0.86932, 0.93273, 0.6355, 1.1153, 0.49377, 0.99078, 0.0080013, 0.0080013, 0.0080013
93
- 92, 1.1018, 0.55436, 0.94907, 0.9507, 0.86976, 0.93285, 0.63539, 1.1148, 0.4936, 0.99064, 0.0079595, 0.0079595, 0.0079595
94
- 93, 1.1, 0.57659, 0.94816, 0.95207, 0.86888, 0.9331, 0.63542, 1.115, 0.49341, 0.99069, 0.0079174, 0.0079174, 0.0079174
95
- 94, 1.0981, 0.55037, 0.94634, 0.95011, 0.86941, 0.93309, 0.63534, 1.115, 0.49337, 0.99074, 0.007875, 0.007875, 0.007875
96
- 95, 1.1029, 0.55712, 0.94884, 0.94987, 0.86951, 0.93319, 0.6352, 1.1151, 0.49306, 0.99079, 0.0078323, 0.0078323, 0.0078323
97
- 96, 1.0961, 0.55087, 0.94835, 0.95013, 0.86915, 0.93315, 0.63532, 1.1149, 0.49306, 0.99073, 0.0077893, 0.0077893, 0.0077893
98
- 97, 1.0981, 0.5528, 0.9464, 0.95043, 0.87003, 0.93332, 0.63532, 1.1145, 0.49295, 0.99056, 0.007746, 0.007746, 0.007746
99
- 98, 1.0961, 0.55306, 0.94534, 0.95053, 0.87029, 0.9333, 0.63593, 1.1144, 0.49302, 0.99042, 0.0077023, 0.0077023, 0.0077023
100
- 99, 1.0978, 0.55084, 0.94864, 0.951, 0.86995, 0.93332, 0.63571, 1.1145, 0.49296, 0.99045, 0.0076584, 0.0076584, 0.0076584
101
- 100, 1.1019, 0.55237, 0.94702, 0.95051, 0.86951, 0.93332, 0.63553, 1.1141, 0.49302, 0.99027, 0.0076142, 0.0076142, 0.0076142
 
1
  epoch, train/box_loss, train/cls_loss, train/dfl_loss, metrics/precision(B), metrics/recall(B), metrics/mAP50(B), metrics/mAP50-95(B), val/box_loss, val/cls_loss, val/dfl_loss, lr/pg0, lr/pg1, lr/pg2
2
+ 1, 1.4335, 1.5766, 1.1566, 0.85784, 0.75756, 0.83502, 0.49836, 1.3464, 0.98022, 1.1587, 0.0033238, 0.0033238, 0.0033238
3
+ 2, 1.352, 0.94795, 1.0645, 0.90854, 0.79751, 0.87897, 0.54487, 1.2742, 0.7818, 1.0735, 0.0066555, 0.0066555, 0.0066555
4
+ 3, 1.3552, 0.86902, 1.0577, 0.8996, 0.79936, 0.8749, 0.54398, 1.2795, 0.79179, 1.0775, 0.0099807, 0.0099807, 0.0099807
5
+ 4, 1.341, 0.81318, 1.0537, 0.91585, 0.80929, 0.89009, 0.55762, 1.2542, 0.7617, 1.0681, 0.009978, 0.009978, 0.009978
6
+ 5, 1.3011, 0.76871, 1.038, 0.90308, 0.82636, 0.88905, 0.56811, 1.2331, 0.74178, 1.062, 0.009978, 0.009978, 0.009978
7
+ 6, 1.2874, 0.74276, 1.0295, 0.92116, 0.81945, 0.89395, 0.57055, 1.232, 0.6811, 1.0559, 0.009961, 0.009961, 0.009961
8
+ 7, 1.2718, 0.71703, 1.0227, 0.9228, 0.83076, 0.90131, 0.58191, 1.2123, 0.64966, 1.0505, 0.0099391, 0.0099391, 0.0099391
9
+ 8, 1.2546, 0.6995, 1.0177, 0.93101, 0.83183, 0.90625, 0.58492, 1.2074, 0.63196, 1.0463, 0.0099123, 0.0099123, 0.0099123
10
+ 9, 1.2511, 0.68853, 1.0134, 0.92611, 0.83444, 0.90522, 0.58513, 1.1969, 0.63898, 1.0437, 0.0098808, 0.0098808, 0.0098808
11
+ 10, 1.2405, 0.68043, 1.0102, 0.92886, 0.83678, 0.90829, 0.59223, 1.1872, 0.61315, 1.031, 0.0098445, 0.0098445, 0.0098445
12
+ 11, 1.2332, 0.68096, 1.007, 0.93163, 0.84244, 0.91086, 0.59711, 1.1779, 0.60133, 1.036, 0.0098035, 0.0098035, 0.0098035
13
+ 12, 1.2222, 0.66248, 1.0042, 0.93834, 0.84085, 0.91504, 0.60376, 1.1766, 0.59292, 1.0312, 0.0097577, 0.0097577, 0.0097577
14
+ 13, 1.2215, 0.65821, 1.001, 0.92985, 0.84721, 0.91345, 0.604, 1.1683, 0.59794, 1.0248, 0.0097074, 0.0097074, 0.0097074
15
+ 14, 1.2176, 0.65427, 0.99964, 0.9321, 0.84801, 0.91408, 0.60588, 1.1647, 0.58937, 1.0265, 0.0096524, 0.0096524, 0.0096524
16
+ 15, 1.2151, 0.64883, 0.99873, 0.9318, 0.8517, 0.91688, 0.60565, 1.1629, 0.58423, 1.0249, 0.0095929, 0.0095929, 0.0095929
17
+ 16, 1.2103, 0.64316, 0.99902, 0.93073, 0.84943, 0.91722, 0.60672, 1.1664, 0.58206, 1.0271, 0.0095289, 0.0095289, 0.0095289
18
+ 17, 1.2062, 0.63888, 0.99602, 0.92929, 0.85518, 0.91741, 0.60617, 1.1702, 0.5773, 1.0251, 0.0094605, 0.0094605, 0.0094605
19
+ 18, 1.1999, 0.64226, 0.99198, 0.93651, 0.85035, 0.91817, 0.60921, 1.1609, 0.57825, 1.0226, 0.0093877, 0.0093877, 0.0093877
20
+ 19, 1.1925, 0.62746, 0.98806, 0.93778, 0.84562, 0.91744, 0.60841, 1.1594, 0.57257, 1.02, 0.0093107, 0.0093107, 0.0093107
21
+ 20, 1.1965, 0.62692, 0.98806, 0.9356, 0.85597, 0.92008, 0.61337, 1.154, 0.563, 1.0164, 0.0092294, 0.0092294, 0.0092294
22
+ 21, 1.1868, 0.62074, 0.9875, 0.93494, 0.85759, 0.92054, 0.61165, 1.1562, 0.56269, 1.0179, 0.009144, 0.009144, 0.009144
23
+ 22, 1.1818, 0.6189, 0.98639, 0.93504, 0.85685, 0.92101, 0.6129, 1.1508, 0.5605, 1.0164, 0.0090546, 0.0090546, 0.0090546
24
+ 23, 1.1904, 0.62386, 0.98785, 0.9407, 0.84997, 0.91875, 0.61211, 1.1541, 0.55777, 1.0165, 0.0089613, 0.0089613, 0.0089613
25
+ 24, 1.1782, 0.61503, 0.98395, 0.93677, 0.85674, 0.92294, 0.61878, 1.1415, 0.55298, 1.0066, 0.008864, 0.008864, 0.008864
26
+ 25, 1.1771, 0.63213, 0.98145, 0.94044, 0.85491, 0.92355, 0.6188, 1.1445, 0.54717, 1.008, 0.008763, 0.008763, 0.008763
27
+ 26, 1.172, 0.60827, 0.97814, 0.9394, 0.85931, 0.92427, 0.61735, 1.1395, 0.54684, 1.0091, 0.0086584, 0.0086584, 0.0086584
28
+ 27, 1.1746, 0.61041, 0.98049, 0.94758, 0.85514, 0.92486, 0.62072, 1.1411, 0.54249, 1.0094, 0.0085502, 0.0085502, 0.0085502
29
+ 28, 1.1675, 0.60553, 0.97908, 0.94497, 0.85358, 0.92414, 0.62053, 1.1411, 0.54318, 1.0118, 0.0084385, 0.0084385, 0.0084385
30
+ 29, 1.1661, 0.60246, 0.97773, 0.9438, 0.85632, 0.92369, 0.61954, 1.1422, 0.54275, 1.0109, 0.0083235, 0.0083235, 0.0083235
31
+ 30, 1.1609, 0.60121, 0.97517, 0.94236, 0.86286, 0.92576, 0.62281, 1.1409, 0.53693, 1.0065, 0.0082052, 0.0082052, 0.0082052
32
+ 31, 1.1601, 0.59629, 0.97636, 0.94574, 0.85535, 0.92544, 0.62537, 1.1353, 0.53387, 1.0062, 0.0080839, 0.0080839, 0.0080839
33
+ 32, 1.1627, 0.59603, 0.97512, 0.94365, 0.85862, 0.92518, 0.62409, 1.1331, 0.53255, 1.0048, 0.0079595, 0.0079595, 0.0079595
34
+ 33, 1.1565, 0.59495, 0.97627, 0.94131, 0.86355, 0.92729, 0.62469, 1.134, 0.52987, 1.0069, 0.0078323, 0.0078323, 0.0078323
35
+ 34, 1.1611, 0.59372, 0.97357, 0.95521, 0.85223, 0.92556, 0.62388, 1.1331, 0.52801, 1.0046, 0.0077023, 0.0077023, 0.0077023
36
+ 35, 1.1538, 0.58989, 0.97267, 0.94214, 0.86393, 0.92721, 0.62549, 1.1302, 0.52361, 1.0032, 0.0075698, 0.0075698, 0.0075698
37
+ 36, 1.1525, 0.58747, 0.97047, 0.9457, 0.86075, 0.92779, 0.62681, 1.1313, 0.52577, 1.0032, 0.0074347, 0.0074347, 0.0074347
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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