Hugging Face's logo

atonus
/
peura

object detection
yolo
machine learning
Model card Files Community
1
atonus commited on
Commit
bc96d27
•
1 Parent(s): 6c6c3f3

Upload 25 files

Browse files
Files changed (25) hide show
  1. train/F1_curve.png +0 -0
  2. train/PR_curve.png +0 -0
  3. train/P_curve.png +0 -0
  4. train/R_curve.png +0 -0
  5. train/args.yaml +17 -9
  6. train/confusion_matrix.png +0 -0
  7. train/confusion_matrix_normalized.png +0 -0
  8. train/labels.jpg +0 -0
  9. train/labels_correlogram.jpg +0 -0
  10. train/results.csv +100 -54
  11. train/results.png +0 -0
  12. train/train_batch0.jpg +0 -0
  13. train/train_batch1.jpg +0 -0
  14. train/train_batch11250.jpg +0 -0
  15. train/train_batch11251.jpg +0 -0
  16. train/train_batch11252.jpg +0 -0
  17. train/train_batch2.jpg +0 -0
  18. train/val_batch0_labels.jpg +0 -0
  19. train/val_batch0_pred.jpg +0 -0
  20. train/val_batch1_labels.jpg +0 -0
  21. train/val_batch1_pred.jpg +0 -0
  22. train/val_batch2_labels.jpg +0 -0
  23. train/val_batch2_pred.jpg +0 -0
  24. train/weights/best.pt +2 -2
  25. train/weights/last.pt +2 -2
train/F1_curve.png CHANGED
train/PR_curve.png CHANGED
train/P_curve.png CHANGED
train/R_curve.png CHANGED
train/args.yaml CHANGED
@@ -1,8 +1,9 @@
1
  task: detect
2
  mode: train
3
  model: /workspace/yolov8n.pt
4
- data: /deer_v6_batched/deer_v6_batch_1/data.yml
5
  epochs: 100
 
6
  patience: 10
7
  batch: 16
8
  imgsz: 640
@@ -28,6 +29,7 @@ amp: true
28
  fraction: 1.0
29
  profile: false
30
  freeze: null
 
31
  overlap_mask: true
32
  mask_ratio: 4
33
  dropout: 0.0
@@ -42,21 +44,23 @@ half: false
42
  dnn: false
43
  plots: true
44
  source: null
45
- show: false
46
- save_txt: false
47
- save_conf: false
48
- save_crop: false
49
- show_labels: true
50
- show_conf: true
51
  vid_stride: 1
52
  stream_buffer: false
53
- line_width: null
54
  visualize: false
55
  augment: false
56
  agnostic_nms: false
57
  classes: null
58
  retina_masks: false
59
- boxes: true
 
 
 
 
 
 
 
 
 
60
  format: torchscript
61
  keras: false
62
  optimize: false
@@ -90,9 +94,13 @@ shear: 0.0
90
  perspective: 0.0
91
  flipud: 0.0
92
  fliplr: 0.5
 
93
  mosaic: 1.0
94
  mixup: 0.0
95
  copy_paste: 0.0
 
 
 
96
  cfg: null
97
  tracker: botsort.yaml
98
  save_dir: /workspace/runs/train_run_1/train
 
1
  task: detect
2
  mode: train
3
  model: /workspace/yolov8n.pt
4
+ data: /deer_v7_batched/deer_v7_batch_1/data.yml
5
  epochs: 100
6
+ time: null
7
  patience: 10
8
  batch: 16
9
  imgsz: 640
 
29
  fraction: 1.0
30
  profile: false
31
  freeze: null
32
+ multi_scale: false
33
  overlap_mask: true
34
  mask_ratio: 4
35
  dropout: 0.0
 
44
  dnn: false
45
  plots: true
46
  source: null
 
 
 
 
 
 
47
  vid_stride: 1
48
  stream_buffer: false
 
49
  visualize: false
50
  augment: false
51
  agnostic_nms: false
52
  classes: null
53
  retina_masks: false
54
+ embed: null
55
+ show: false
56
+ save_frames: false
57
+ save_txt: false
58
+ save_conf: false
59
+ save_crop: false
60
+ show_labels: true
61
+ show_conf: true
62
+ show_boxes: true
63
+ line_width: null
64
  format: torchscript
65
  keras: false
66
  optimize: false
 
94
  perspective: 0.0
95
  flipud: 0.0
96
  fliplr: 0.5
97
+ bgr: 0.0
98
  mosaic: 1.0
99
  mixup: 0.0
100
  copy_paste: 0.0
101
+ auto_augment: randaugment
102
+ erasing: 0.4
103
+ crop_fraction: 1.0
104
  cfg: null
105
  tracker: botsort.yaml
106
  save_dir: /workspace/runs/train_run_1/train
train/confusion_matrix.png CHANGED
train/confusion_matrix_normalized.png CHANGED
train/labels.jpg CHANGED
train/labels_correlogram.jpg CHANGED
train/results.csv CHANGED
@@ -1,55 +1,101 @@
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, 1.4022, 1.9869, 1.602, 0.49242, 0.35966, 0.36613, 0.17133, 1.8564, 2.8766, 2.3258, 0.00066032, 0.00066032, 0.00066032
3
- 2, 1.5159, 1.6898, 1.6693, 0.67758, 0.58985, 0.63666, 0.29797, 1.8018, 2.0623, 2.0906, 0.0013137, 0.0013137, 0.0013137
4
- 3, 1.5811, 1.595, 1.7205, 0.52419, 0.49412, 0.47777, 0.22989, 1.8458, 2.3771, 2.0768, 0.0019538, 0.0019538, 0.0019538
5
- 4, 1.5444, 1.466, 1.6873, 0.58286, 0.50252, 0.52354, 0.27907, 1.7378, 2.0047, 1.9542, 0.00194, 0.00194, 0.00194
6
- 5, 1.5172, 1.3901, 1.6596, 0.72378, 0.63414, 0.70667, 0.39196, 1.5993, 1.4542, 1.8117, 0.00194, 0.00194, 0.00194
7
- 6, 1.5224, 1.3681, 1.6564, 0.65639, 0.52973, 0.60126, 0.33722, 1.6528, 1.6858, 1.8654, 0.00192, 0.00192, 0.00192
8
- 7, 1.4431, 1.2733, 1.606, 0.82891, 0.70843, 0.79735, 0.47648, 1.4644, 1.1365, 1.6775, 0.0019001, 0.0019001, 0.0019001
9
- 8, 1.4417, 1.2162, 1.6, 0.7921, 0.75126, 0.81159, 0.47208, 1.4922, 1.1433, 1.6828, 0.0018801, 0.0018801, 0.0018801
10
- 9, 1.4181, 1.1742, 1.5889, 0.87717, 0.81008, 0.85561, 0.51097, 1.4188, 1.0247, 1.6415, 0.0018601, 0.0018601, 0.0018601
11
- 10, 1.37, 1.1242, 1.5515, 0.87959, 0.79328, 0.84023, 0.50059, 1.4293, 1.0708, 1.647, 0.0018401, 0.0018401, 0.0018401
12
- 11, 1.3733, 1.1187, 1.5376, 0.82881, 0.7479, 0.81171, 0.4773, 1.4998, 1.1129, 1.6903, 0.0018201, 0.0018201, 0.0018201
13
- 12, 1.3582, 1.109, 1.5371, 0.81731, 0.7479, 0.82079, 0.48707, 1.4728, 1.0386, 1.637, 0.0018001, 0.0018001, 0.0018001
14
- 13, 1.3254, 1.061, 1.5217, 0.90118, 0.79702, 0.87057, 0.52683, 1.3896, 0.93789, 1.608, 0.0017801, 0.0017801, 0.0017801
15
- 14, 1.3351, 1.0765, 1.5309, 0.90551, 0.75698, 0.85719, 0.51818, 1.4016, 0.96081, 1.5739, 0.0017601, 0.0017601, 0.0017601
16
- 15, 1.2845, 1.0365, 1.4921, 0.89055, 0.80679, 0.8716, 0.54512, 1.3188, 0.90927, 1.5838, 0.0017401, 0.0017401, 0.0017401
17
- 16, 1.2875, 1.0188, 1.4957, 0.87866, 0.84034, 0.87732, 0.54921, 1.3314, 0.87526, 1.5633, 0.0017201, 0.0017201, 0.0017201
18
- 17, 1.2847, 0.97791, 1.4889, 0.8889, 0.82024, 0.87516, 0.53727, 1.3909, 0.90252, 1.5712, 0.0017002, 0.0017002, 0.0017002
19
- 18, 1.2625, 0.96971, 1.467, 0.80187, 0.68403, 0.75473, 0.46015, 1.4362, 1.161, 1.6749, 0.0016802, 0.0016802, 0.0016802
20
- 19, 1.2892, 0.99695, 1.4972, 0.91164, 0.85042, 0.89519, 0.55526, 1.3422, 0.82048, 1.5511, 0.0016602, 0.0016602, 0.0016602
21
- 20, 1.233, 0.92399, 1.4526, 0.90322, 0.81008, 0.87384, 0.54602, 1.3088, 0.85425, 1.5509, 0.0016402, 0.0016402, 0.0016402
22
- 21, 1.2389, 0.92448, 1.4534, 0.92486, 0.8275, 0.88883, 0.57321, 1.313, 0.79565, 1.5329, 0.0016202, 0.0016202, 0.0016202
23
- 22, 1.2374, 0.91973, 1.4606, 0.93455, 0.83994, 0.89432, 0.57838, 1.276, 0.77076, 1.4844, 0.0016002, 0.0016002, 0.0016002
24
- 23, 1.2126, 0.89915, 1.4364, 0.90066, 0.82857, 0.89838, 0.58633, 1.2445, 0.77945, 1.4901, 0.0015802, 0.0015802, 0.0015802
25
- 24, 1.198, 0.89593, 1.4331, 0.9048, 0.81345, 0.87424, 0.56558, 1.2745, 0.79531, 1.5123, 0.0015602, 0.0015602, 0.0015602
26
- 25, 1.1987, 0.87272, 1.4271, 0.91395, 0.85546, 0.90272, 0.58771, 1.2413, 0.73536, 1.4613, 0.0015402, 0.0015402, 0.0015402
27
- 26, 1.1933, 0.88773, 1.4303, 0.90524, 0.85089, 0.8947, 0.58394, 1.2104, 0.76973, 1.4655, 0.0015202, 0.0015202, 0.0015202
28
- 27, 1.1721, 0.86922, 1.4196, 0.92561, 0.88403, 0.92345, 0.59353, 1.2327, 0.72791, 1.4812, 0.0015002, 0.0015002, 0.0015002
29
- 28, 1.1866, 0.85015, 1.4247, 0.9296, 0.82185, 0.9012, 0.58725, 1.2591, 0.76265, 1.4839, 0.0014803, 0.0014803, 0.0014803
30
- 29, 1.1965, 0.86947, 1.4234, 0.92244, 0.85951, 0.90545, 0.58995, 1.2385, 0.71622, 1.4831, 0.0014603, 0.0014603, 0.0014603
31
- 30, 1.1525, 0.82079, 1.3968, 0.92743, 0.83529, 0.91153, 0.59497, 1.2575, 0.74439, 1.4615, 0.0014403, 0.0014403, 0.0014403
32
- 31, 1.1533, 0.83974, 1.3918, 0.9462, 0.84538, 0.91415, 0.60413, 1.2105, 0.69743, 1.4736, 0.0014203, 0.0014203, 0.0014203
33
- 32, 1.1462, 0.81224, 1.3814, 0.93874, 0.87562, 0.9222, 0.60705, 1.206, 0.69147, 1.4408, 0.0014003, 0.0014003, 0.0014003
34
- 33, 1.1246, 0.81799, 1.381, 0.91616, 0.87059, 0.91192, 0.59867, 1.2455, 0.70027, 1.4936, 0.0013803, 0.0013803, 0.0013803
35
- 34, 1.1219, 0.79834, 1.3732, 0.9266, 0.8605, 0.91687, 0.60165, 1.2305, 0.70597, 1.4893, 0.0013603, 0.0013603, 0.0013603
36
- 35, 1.1275, 0.80298, 1.396, 0.92623, 0.84706, 0.90242, 0.59448, 1.2182, 0.71913, 1.4678, 0.0013403, 0.0013403, 0.0013403
37
- 36, 1.1146, 0.80059, 1.3681, 0.9274, 0.87059, 0.91451, 0.6127, 1.2157, 0.6902, 1.4752, 0.0013203, 0.0013203, 0.0013203
38
- 37, 1.1062, 0.79163, 1.3714, 0.92267, 0.85378, 0.91299, 0.61014, 1.1995, 0.69226, 1.4567, 0.0013004, 0.0013004, 0.0013004
39
- 38, 1.1268, 0.78697, 1.3759, 0.85058, 0.83025, 0.89471, 0.5831, 1.2363, 0.80012, 1.4541, 0.0012804, 0.0012804, 0.0012804
40
- 39, 1.1214, 0.78269, 1.3684, 0.93576, 0.85684, 0.91722, 0.6195, 1.1691, 0.67836, 1.4282, 0.0012604, 0.0012604, 0.0012604
41
- 40, 1.0883, 0.77343, 1.3583, 0.93545, 0.88403, 0.92622, 0.62589, 1.2136, 0.65422, 1.467, 0.0012404, 0.0012404, 0.0012404
42
- 41, 1.0779, 0.75971, 1.3516, 0.92519, 0.87297, 0.91932, 0.61484, 1.181, 0.66976, 1.4356, 0.0012204, 0.0012204, 0.0012204
43
- 42, 1.0828, 0.74471, 1.3442, 0.95202, 0.86697, 0.92161, 0.61385, 1.1833, 0.65513, 1.4317, 0.0012004, 0.0012004, 0.0012004
44
- 43, 1.0886, 0.77801, 1.3581, 0.96051, 0.85836, 0.9203, 0.61386, 1.1957, 0.6452, 1.4305, 0.0011804, 0.0011804, 0.0011804
45
- 44, 1.0809, 0.74482, 1.3442, 0.93465, 0.88945, 0.9259, 0.63948, 1.1604, 0.62151, 1.3923, 0.0011604, 0.0011604, 0.0011604
46
- 45, 1.0788, 0.73858, 1.3415, 0.92683, 0.8605, 0.92208, 0.62566, 1.1642, 0.64843, 1.4213, 0.0011404, 0.0011404, 0.0011404
47
- 46, 1.0609, 0.73258, 1.334, 0.91769, 0.86891, 0.92056, 0.61995, 1.155, 0.6471, 1.4171, 0.0011204, 0.0011204, 0.0011204
48
- 47, 1.0588, 0.71404, 1.3243, 0.90214, 0.87059, 0.91457, 0.61935, 1.1903, 0.65616, 1.4538, 0.0011004, 0.0011004, 0.0011004
49
- 48, 1.0529, 0.73404, 1.3248, 0.94592, 0.85042, 0.91578, 0.61942, 1.1822, 0.65899, 1.4596, 0.0010805, 0.0010805, 0.0010805
50
- 49, 1.068, 0.72478, 1.3311, 0.91075, 0.86723, 0.91071, 0.6227, 1.1753, 0.63218, 1.417, 0.0010605, 0.0010605, 0.0010605
51
- 50, 1.0143, 0.70671, 1.2985, 0.93885, 0.88235, 0.9277, 0.63441, 1.1702, 0.63744, 1.4099, 0.0010405, 0.0010405, 0.0010405
52
- 51, 1.0343, 0.70221, 1.3024, 0.91576, 0.89523, 0.92488, 0.637, 1.1669, 0.61616, 1.4287, 0.0010205, 0.0010205, 0.0010205
53
- 52, 1.0452, 0.71684, 1.3193, 0.92586, 0.88155, 0.91904, 0.63805, 1.1497, 0.61456, 1.4077, 0.0010005, 0.0010005, 0.0010005
54
- 53, 1.0155, 0.70001, 1.3042, 0.92149, 0.88773, 0.9216, 0.62819, 1.1767, 0.63093, 1.4187, 0.00098051, 0.00098051, 0.00098051
55
- 54, 1.0223, 0.69477, 1.3089, 0.91163, 0.88908, 0.91844, 0.62991, 1.1461, 0.6253, 1.4056, 0.00096052, 0.00096052, 0.00096052
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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, 1.3319, 1.9389, 1.5572, 0.66437, 0.51574, 0.57525, 0.28466, 1.5432, 2.2896, 1.7922, 0.00066133, 0.00066133, 0.00066133
3
+ 2, 1.4503, 1.6458, 1.6456, 0.58779, 0.56777, 0.58637, 0.29895, 1.7695, 2.0758, 2.0873, 0.0013147, 0.0013147, 0.0013147
4
+ 3, 1.4837, 1.505, 1.6676, 0.65056, 0.5512, 0.59051, 0.27176, 1.8934, 1.6368, 2.0316, 0.0019548, 0.0019548, 0.0019548
5
+ 4, 1.4742, 1.4377, 1.6535, 0.76204, 0.65211, 0.71095, 0.38159, 1.613, 1.7156, 1.8098, 0.00194, 0.00194, 0.00194
6
+ 5, 1.4436, 1.3329, 1.6229, 0.70811, 0.6138, 0.63616, 0.32627, 1.7326, 1.6136, 1.8997, 0.00192, 0.00192, 0.00192
7
+ 6, 1.4162, 1.2883, 1.6068, 0.8353, 0.71235, 0.79082, 0.40202, 1.7221, 1.3139, 1.8792, 0.0019001, 0.0019001, 0.0019001
8
+ 7, 1.3578, 1.2017, 1.5542, 0.8356, 0.71954, 0.7939, 0.42871, 1.5642, 1.1996, 1.7318, 0.0018801, 0.0018801, 0.0018801
9
+ 8, 1.3795, 1.1859, 1.5733, 0.85289, 0.75753, 0.82535, 0.48419, 1.4731, 1.1071, 1.6581, 0.0018601, 0.0018601, 0.0018601
10
+ 9, 1.328, 1.1385, 1.5299, 0.85182, 0.78765, 0.84764, 0.50074, 1.4434, 1.0358, 1.596, 0.0018401, 0.0018401, 0.0018401
11
+ 10, 1.3056, 1.0945, 1.5098, 0.87109, 0.76323, 0.84347, 0.50211, 1.44, 1.0225, 1.6233, 0.0018201, 0.0018201, 0.0018201
12
+ 11, 1.2995, 1.1002, 1.5105, 0.83467, 0.80873, 0.86351, 0.51792, 1.3729, 1.0322, 1.5746, 0.0018001, 0.0018001, 0.0018001
13
+ 12, 1.2755, 1.0501, 1.4925, 0.82779, 0.71536, 0.79584, 0.4422, 1.5377, 1.1848, 1.7186, 0.0017801, 0.0017801, 0.0017801
14
+ 13, 1.2691, 1.0404, 1.4961, 0.87969, 0.74699, 0.84741, 0.52667, 1.37, 0.95022, 1.5667, 0.0017601, 0.0017601, 0.0017601
15
+ 14, 1.2237, 0.99533, 1.4577, 0.89408, 0.81325, 0.87567, 0.5437, 1.34, 0.9001, 1.5343, 0.0017401, 0.0017401, 0.0017401
16
+ 15, 1.2304, 0.98767, 1.4774, 0.88232, 0.82831, 0.88297, 0.54429, 1.3675, 0.85753, 1.5657, 0.0017201, 0.0017201, 0.0017201
17
+ 16, 1.2063, 0.95478, 1.4433, 0.92573, 0.82596, 0.89358, 0.56527, 1.2983, 0.81623, 1.501, 0.0017002, 0.0017002, 0.0017002
18
+ 17, 1.193, 0.94033, 1.4333, 0.90835, 0.82229, 0.88183, 0.55209, 1.3506, 0.82356, 1.5663, 0.0016802, 0.0016802, 0.0016802
19
+ 18, 1.1762, 0.91876, 1.4211, 0.93227, 0.86145, 0.91883, 0.58322, 1.2944, 0.77196, 1.465, 0.0016602, 0.0016602, 0.0016602
20
+ 19, 1.1766, 0.89996, 1.4273, 0.88375, 0.8358, 0.88575, 0.54884, 1.3057, 0.84002, 1.5216, 0.0016402, 0.0016402, 0.0016402
21
+ 20, 1.1663, 0.89129, 1.4204, 0.91018, 0.85459, 0.92199, 0.58784, 1.2428, 0.75788, 1.4358, 0.0016202, 0.0016202, 0.0016202
22
+ 21, 1.1488, 0.87247, 1.404, 0.88688, 0.83133, 0.87583, 0.54372, 1.3351, 0.76871, 1.5392, 0.0016002, 0.0016002, 0.0016002
23
+ 22, 1.1282, 0.85634, 1.3964, 0.89432, 0.85693, 0.90005, 0.57259, 1.2556, 0.78641, 1.4653, 0.0015802, 0.0015802, 0.0015802
24
+ 23, 1.122, 0.83773, 1.3815, 0.92305, 0.8509, 0.90502, 0.58526, 1.2636, 0.75162, 1.4748, 0.0015602, 0.0015602, 0.0015602
25
+ 24, 1.1529, 0.87412, 1.4047, 0.92811, 0.87499, 0.92486, 0.61492, 1.2026, 0.69445, 1.4086, 0.0015402, 0.0015402, 0.0015402
26
+ 25, 1.1204, 0.8215, 1.3786, 0.91869, 0.87651, 0.92202, 0.60921, 1.2123, 0.70228, 1.4267, 0.0015202, 0.0015202, 0.0015202
27
+ 26, 1.1108, 0.83268, 1.3781, 0.90403, 0.83283, 0.90531, 0.58423, 1.2607, 0.78015, 1.4531, 0.0015002, 0.0015002, 0.0015002
28
+ 27, 1.1091, 0.82368, 1.3757, 0.92232, 0.87616, 0.92691, 0.60369, 1.2177, 0.6925, 1.4254, 0.0014803, 0.0014803, 0.0014803
29
+ 28, 1.0772, 0.80484, 1.3531, 0.94044, 0.83886, 0.91844, 0.60243, 1.2137, 0.71425, 1.4197, 0.0014603, 0.0014603, 0.0014603
30
+ 29, 1.0886, 0.79289, 1.3518, 0.93182, 0.87048, 0.92517, 0.61245, 1.198, 0.69431, 1.4314, 0.0014403, 0.0014403, 0.0014403
31
+ 30, 1.0808, 0.81306, 1.3562, 0.93818, 0.86851, 0.91973, 0.60768, 1.2042, 0.68076, 1.4199, 0.0014203, 0.0014203, 0.0014203
32
+ 31, 1.0541, 0.76236, 1.3351, 0.94171, 0.87589, 0.93404, 0.61088, 1.2033, 0.65307, 1.4515, 0.0014003, 0.0014003, 0.0014003
33
+ 32, 1.0461, 0.7607, 1.3405, 0.95169, 0.89012, 0.92513, 0.60954, 1.2216, 0.65525, 1.4279, 0.0013803, 0.0013803, 0.0013803
34
+ 33, 1.0405, 0.76315, 1.3173, 0.94051, 0.86898, 0.9253, 0.60309, 1.2132, 0.66961, 1.4088, 0.0013603, 0.0013603, 0.0013603
35
+ 34, 1.0537, 0.76513, 1.3262, 0.94901, 0.89157, 0.93683, 0.61974, 1.1821, 0.6361, 1.3885, 0.0013403, 0.0013403, 0.0013403
36
+ 35, 1.0364, 0.74774, 1.3137, 0.95, 0.88698, 0.93346, 0.62166, 1.1639, 0.62096, 1.386, 0.0013203, 0.0013203, 0.0013203
37
+ 36, 1.0375, 0.75455, 1.3219, 0.90028, 0.88705, 0.92154, 0.62715, 1.1578, 0.66632, 1.3846, 0.0013004, 0.0013004, 0.0013004
38
+ 37, 1.0348, 0.73579, 1.3174, 0.93157, 0.88705, 0.93624, 0.63515, 1.1485, 0.61699, 1.3809, 0.0012804, 0.0012804, 0.0012804
39
+ 38, 1.0362, 0.72428, 1.3183, 0.93287, 0.87651, 0.92179, 0.62797, 1.1491, 0.63005, 1.3902, 0.0012604, 0.0012604, 0.0012604
40
+ 39, 0.9951, 0.71699, 1.2976, 0.94785, 0.87599, 0.93424, 0.64226, 1.1366, 0.62394, 1.3764, 0.0012404, 0.0012404, 0.0012404
41
+ 40, 1.0286, 0.73329, 1.3192, 0.94696, 0.86295, 0.93192, 0.62027, 1.1531, 0.65479, 1.3848, 0.0012204, 0.0012204, 0.0012204
42
+ 41, 0.99408, 0.69377, 1.2959, 0.94441, 0.87952, 0.93655, 0.64236, 1.1281, 0.60461, 1.3764, 0.0012004, 0.0012004, 0.0012004
43
+ 42, 0.98241, 0.68742, 1.2776, 0.93415, 0.88855, 0.93312, 0.6435, 1.1133, 0.59133, 1.3584, 0.0011804, 0.0011804, 0.0011804
44
+ 43, 0.97985, 0.70068, 1.2927, 0.92997, 0.89307, 0.93209, 0.62546, 1.1875, 0.61591, 1.3906, 0.0011604, 0.0011604, 0.0011604
45
+ 44, 1.0005, 0.6981, 1.296, 0.94236, 0.91107, 0.93997, 0.64518, 1.1049, 0.59164, 1.3586, 0.0011404, 0.0011404, 0.0011404
46
+ 45, 0.9878, 0.69831, 1.2833, 0.95094, 0.87578, 0.93467, 0.65068, 1.1129, 0.59774, 1.3432, 0.0011204, 0.0011204, 0.0011204
47
+ 46, 0.99315, 0.68826, 1.2971, 0.94434, 0.89608, 0.93404, 0.64314, 1.1345, 0.59672, 1.3651, 0.0011004, 0.0011004, 0.0011004
48
+ 47, 0.98394, 0.67581, 1.2867, 0.93328, 0.88705, 0.92861, 0.64548, 1.1003, 0.57873, 1.3558, 0.0010805, 0.0010805, 0.0010805
49
+ 48, 0.94958, 0.67277, 1.2631, 0.9366, 0.88988, 0.92701, 0.63939, 1.1163, 0.59788, 1.3604, 0.0010605, 0.0010605, 0.0010605
50
+ 49, 0.94926, 0.6719, 1.2596, 0.93922, 0.88102, 0.93569, 0.64946, 1.0985, 0.58132, 1.3427, 0.0010405, 0.0010405, 0.0010405
51
+ 50, 0.96627, 0.65725, 1.2672, 0.95063, 0.88253, 0.93601, 0.64978, 1.0963, 0.57288, 1.3604, 0.0010205, 0.0010205, 0.0010205
52
+ 51, 0.93638, 0.65394, 1.2542, 0.95005, 0.88102, 0.93848, 0.64735, 1.0872, 0.57092, 1.3354, 0.0010005, 0.0010005, 0.0010005
53
+ 52, 0.95541, 0.66516, 1.2649, 0.91461, 0.88717, 0.92964, 0.64037, 1.1362, 0.61545, 1.379, 0.00098051, 0.00098051, 0.00098051
54
+ 53, 0.94663, 0.63978, 1.2653, 0.95479, 0.875, 0.93873, 0.6471, 1.1087, 0.5885, 1.3729, 0.00096052, 0.00096052, 0.00096052
55
+ 54, 0.93826, 0.6342, 1.2647, 0.94367, 0.88297, 0.93765, 0.65868, 1.1025, 0.58733, 1.3526, 0.00094053, 0.00094053, 0.00094053
56
+ 55, 0.92148, 0.62356, 1.2452, 0.93676, 0.89006, 0.93833, 0.65184, 1.1361, 0.58628, 1.3708, 0.00092054, 0.00092054, 0.00092054
57
+ 56, 0.94256, 0.63751, 1.2642, 0.94328, 0.90163, 0.94404, 0.65295, 1.0917, 0.56268, 1.3527, 0.00090055, 0.00090055, 0.00090055
58
+ 57, 0.93246, 0.63583, 1.2526, 0.93614, 0.90515, 0.94025, 0.65183, 1.0904, 0.57427, 1.3857, 0.00088056, 0.00088056, 0.00088056
59
+ 58, 0.92829, 0.62547, 1.2537, 0.95068, 0.89985, 0.94038, 0.65068, 1.09, 0.57144, 1.3484, 0.00086057, 0.00086057, 0.00086057
60
+ 59, 0.91279, 0.61362, 1.2392, 0.94549, 0.88814, 0.93805, 0.65967, 1.0807, 0.56447, 1.3499, 0.00084058, 0.00084058, 0.00084058
61
+ 60, 0.90413, 0.62406, 1.2336, 0.95183, 0.8928, 0.94162, 0.66447, 1.0822, 0.5538, 1.3472, 0.00082059, 0.00082059, 0.00082059
62
+ 61, 0.90536, 0.61694, 1.2381, 0.95787, 0.89029, 0.94043, 0.65505, 1.0942, 0.55624, 1.3542, 0.0008006, 0.0008006, 0.0008006
63
+ 62, 0.87961, 0.60812, 1.2262, 0.93794, 0.89608, 0.92748, 0.64996, 1.1086, 0.56629, 1.3794, 0.00078061, 0.00078061, 0.00078061
64
+ 63, 0.87036, 0.58885, 1.2155, 0.94793, 0.89307, 0.93379, 0.65885, 1.0846, 0.54267, 1.3491, 0.00076062, 0.00076062, 0.00076062
65
+ 64, 0.87382, 0.58893, 1.2168, 0.95493, 0.89355, 0.94819, 0.6715, 1.0748, 0.55735, 1.3361, 0.00074063, 0.00074063, 0.00074063
66
+ 65, 0.88006, 0.58724, 1.2138, 0.94674, 0.9102, 0.94689, 0.65812, 1.1036, 0.5506, 1.3471, 0.00072064, 0.00072064, 0.00072064
67
+ 66, 0.88766, 0.58998, 1.2187, 0.93954, 0.90813, 0.94545, 0.65942, 1.0788, 0.53711, 1.3273, 0.00070065, 0.00070065, 0.00070065
68
+ 67, 0.87421, 0.57441, 1.2051, 0.96279, 0.89628, 0.94583, 0.66176, 1.1002, 0.54171, 1.3444, 0.00068066, 0.00068066, 0.00068066
69
+ 68, 0.87971, 0.5827, 1.2185, 0.93937, 0.90512, 0.94814, 0.65634, 1.0967, 0.546, 1.3503, 0.00066067, 0.00066067, 0.00066067
70
+ 69, 0.85838, 0.57282, 1.1943, 0.94977, 0.91117, 0.94533, 0.6648, 1.1031, 0.53447, 1.3639, 0.00064068, 0.00064068, 0.00064068
71
+ 70, 0.85315, 0.56511, 1.2001, 0.96022, 0.90892, 0.95485, 0.66769, 1.0828, 0.53359, 1.348, 0.00062069, 0.00062069, 0.00062069
72
+ 71, 0.86474, 0.5826, 1.2171, 0.95041, 0.89478, 0.94002, 0.66189, 1.0692, 0.53754, 1.3521, 0.0006007, 0.0006007, 0.0006007
73
+ 72, 0.81885, 0.54574, 1.1857, 0.95275, 0.89608, 0.94652, 0.67172, 1.0635, 0.53495, 1.3406, 0.00058071, 0.00058071, 0.00058071
74
+ 73, 0.82888, 0.56118, 1.1885, 0.94647, 0.90543, 0.94526, 0.67382, 1.0595, 0.52944, 1.3422, 0.00056072, 0.00056072, 0.00056072
75
+ 74, 0.81734, 0.54483, 1.1845, 0.95082, 0.90265, 0.94598, 0.67337, 1.0672, 0.53474, 1.3354, 0.00054073, 0.00054073, 0.00054073
76
+ 75, 0.83908, 0.54928, 1.2002, 0.94465, 0.90211, 0.94151, 0.66637, 1.0686, 0.53234, 1.345, 0.00052074, 0.00052074, 0.00052074
77
+ 76, 0.81866, 0.53604, 1.1807, 0.95673, 0.9006, 0.94468, 0.67302, 1.0515, 0.52123, 1.344, 0.00050075, 0.00050075, 0.00050075
78
+ 77, 0.82191, 0.53904, 1.1758, 0.94633, 0.91114, 0.94587, 0.66746, 1.071, 0.5304, 1.3436, 0.00048076, 0.00048076, 0.00048076
79
+ 78, 0.80042, 0.5252, 1.1679, 0.95124, 0.91089, 0.94954, 0.67373, 1.0467, 0.50964, 1.3335, 0.00046077, 0.00046077, 0.00046077
80
+ 79, 0.808, 0.52938, 1.1692, 0.95917, 0.89759, 0.94061, 0.67831, 1.0487, 0.51605, 1.3452, 0.00044078, 0.00044078, 0.00044078
81
+ 80, 0.78654, 0.51889, 1.1722, 0.96708, 0.8991, 0.94873, 0.67791, 1.0465, 0.51037, 1.3351, 0.00042079, 0.00042079, 0.00042079
82
+ 81, 0.80758, 0.53654, 1.1702, 0.95245, 0.90494, 0.94529, 0.673, 1.0598, 0.51563, 1.3441, 0.0004008, 0.0004008, 0.0004008
83
+ 82, 0.78199, 0.50917, 1.1536, 0.95568, 0.90211, 0.94753, 0.67363, 1.0498, 0.51088, 1.3454, 0.00038081, 0.00038081, 0.00038081
84
+ 83, 0.7792, 0.50774, 1.1621, 0.96001, 0.90384, 0.95187, 0.68448, 1.0385, 0.50381, 1.3267, 0.00036082, 0.00036082, 0.00036082
85
+ 84, 0.78113, 0.50577, 1.1479, 0.96166, 0.90652, 0.95033, 0.68063, 1.053, 0.50589, 1.3395, 0.00034083, 0.00034083, 0.00034083
86
+ 85, 0.77674, 0.51404, 1.151, 0.94349, 0.91566, 0.95109, 0.67947, 1.0445, 0.50468, 1.3308, 0.00032084, 0.00032084, 0.00032084
87
+ 86, 0.79311, 0.52062, 1.1665, 0.95497, 0.90663, 0.95151, 0.6833, 1.043, 0.50616, 1.3417, 0.00030085, 0.00030085, 0.00030085
88
+ 87, 0.76435, 0.50203, 1.1492, 0.9472, 0.91566, 0.95502, 0.68569, 1.0446, 0.49888, 1.3438, 0.00028086, 0.00028086, 0.00028086
89
+ 88, 0.77065, 0.50064, 1.1522, 0.95602, 0.91668, 0.95431, 0.68404, 1.0298, 0.498, 1.3321, 0.00026087, 0.00026087, 0.00026087
90
+ 89, 0.72368, 0.48203, 1.1275, 0.96458, 0.91566, 0.95318, 0.68537, 1.0418, 0.50315, 1.3473, 0.00024088, 0.00024088, 0.00024088
91
+ 90, 0.75847, 0.50445, 1.1423, 0.96042, 0.91368, 0.95354, 0.6875, 1.0431, 0.502, 1.3456, 0.00022089, 0.00022089, 0.00022089
92
+ 91, 0.71651, 0.3934, 1.1427, 0.96363, 0.90663, 0.94836, 0.6696, 1.0555, 0.51315, 1.3555, 0.0002009, 0.0002009, 0.0002009
93
+ 92, 0.67522, 0.36113, 1.1121, 0.96004, 0.90455, 0.94801, 0.67579, 1.0607, 0.52278, 1.3623, 0.00018091, 0.00018091, 0.00018091
94
+ 93, 0.67981, 0.36274, 1.1086, 0.95343, 0.90512, 0.95026, 0.68512, 1.0599, 0.51072, 1.3552, 0.00016092, 0.00016092, 0.00016092
95
+ 94, 0.66726, 0.35318, 1.0998, 0.948, 0.91114, 0.95, 0.68621, 1.0549, 0.51119, 1.362, 0.00014093, 0.00014093, 0.00014093
96
+ 95, 0.65902, 0.34472, 1.1051, 0.96506, 0.89307, 0.95143, 0.68727, 1.0408, 0.5027, 1.3495, 0.00012094, 0.00012094, 0.00012094
97
+ 96, 0.6542, 0.34101, 1.0967, 0.95465, 0.90663, 0.95092, 0.68507, 1.0418, 0.5031, 1.3514, 0.00010095, 0.00010095, 0.00010095
98
+ 97, 0.64514, 0.34047, 1.086, 0.95706, 0.90512, 0.95248, 0.68645, 1.0447, 0.5041, 1.3534, 8.096e-05, 8.096e-05, 8.096e-05
99
+ 98, 0.63979, 0.33715, 1.0742, 0.95381, 0.90196, 0.95255, 0.68842, 1.043, 0.50853, 1.3554, 6.097e-05, 6.097e-05, 6.097e-05
100
+ 99, 0.64051, 0.3345, 1.0865, 0.9526, 0.90512, 0.9527, 0.68805, 1.0422, 0.5018, 1.3549, 4.098e-05, 4.098e-05, 4.098e-05
101
+ 100, 0.6264, 0.33138, 1.0779, 0.95132, 0.90512, 0.95375, 0.69118, 1.0417, 0.50098, 1.3542, 2.099e-05, 2.099e-05, 2.099e-05
train/results.png CHANGED
train/train_batch0.jpg CHANGED
train/train_batch1.jpg CHANGED
train/train_batch11250.jpg ADDED
train/train_batch11251.jpg ADDED
train/train_batch11252.jpg ADDED
train/train_batch2.jpg CHANGED
train/val_batch0_labels.jpg CHANGED
train/val_batch0_pred.jpg CHANGED
train/val_batch1_labels.jpg CHANGED
train/val_batch1_pred.jpg CHANGED
train/val_batch2_labels.jpg CHANGED
train/val_batch2_pred.jpg CHANGED
train/weights/best.pt CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:f3d19cd15d9a537af44cb18b1c0878bb3d165fee74412167aeccddf416895c84
3
- size 6234094
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:ab4f2eb05949c6ae6eb4b81f356c58dce6de6a216e21ab1ab7d4a9b4a43d5310
3
+ size 6241262
train/weights/last.pt CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:385536630f5fe8ede2444af5c3d94fad8ead874d1a23659d6958056058021967
3
- size 6235310
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:8dad8be627a8a4929df348a66d92b1b14a32971977d709ab524493f436dcc3e9
3
+ size 6241262