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Perception365
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VehicleNet-RFDETR-n

Object Detection
TensorBoard
English
indian-traffic
inference-efficiency
multi-vehicle-detection
gpu-hungry
roboflow
Model card Files
xet
 Metrics Community
Subh775 commited on
Commit
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Files added - 8 files

Browse files
Files changed (19) hide show
  1. .gitattributes +1 -0
  2. checkpoint_best_ema.pth +3 -0
  3. checkpoint_best_regular.pth +3 -0
  4. checkpoint_best_total.pth +3 -0
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  8. metrics.csv +203 -0
  9. wandb/debug-internal.log +15 -0
  10. wandb/debug.log +21 -0
  11. wandb/run-20260416_084308-rucpcwrn/files/config.yaml +90 -0
  12. wandb/run-20260416_084308-rucpcwrn/files/output.log +334 -0
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  15. wandb/run-20260416_084308-rucpcwrn/files/wandb-summary.json +1 -0
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  17. wandb/run-20260416_084308-rucpcwrn/logs/debug-internal.log +15 -0
  18. wandb/run-20260416_084308-rucpcwrn/logs/debug.log +21 -0
  19. wandb/run-20260416_084308-rucpcwrn/run-rucpcwrn.wandb +3 -0
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1
+ /usr/local/lib/python3.12/dist-packages/lightning_fabric/loggers/csv_logs.py:268: Experiment logs directory output/ exists and is not empty. Previous log files in this directory will be deleted when the new ones are saved!
2
+ [2026-04-16 08:43:27] [INFO] rf-detr - Building Roboflow train dataset with square resize at resolution 384
3
+ [2026-04-16 08:43:27] [INFO] rf-detr - Using multi-scale training with square resize and scales: [544]
4
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5
+ [2026-04-16 08:43:27] [INFO] rf-detr - Built 1 Albumentations transforms from config
6
+ loading annotations into memory...
7
+ Done (t=1.08s)
8
+ creating index...
9
+ index created!
10
+ [2026-04-16 08:43:29] [INFO] rf-detr - Building Roboflow val dataset with square resize at resolution 384
11
+ [2026-04-16 08:43:29] [INFO] rf-detr - Using multi-scale training with square resize and scales: [544]
12
+ [2026-04-16 08:43:29] [INFO] rf-detr - Built 1 Albumentations transforms from config
13
+ loading annotations into memory...
14
+ Done (t=0.29s)
15
+ creating index...
16
+ index created!
17
+ /usr/local/lib/python3.12/dist-packages/pytorch_lightning/callbacks/model_checkpoint.py:881: Checkpoint directory /kaggle/working/output exists and is not empty.
18
+ LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]
19
+ Loading `train_dataloader` to estimate number of stepping batches.
20
+ /usr/local/lib/python3.12/dist-packages/pytorch_lightning/utilities/_pytree.py:21: `isinstance(treespec, LeafSpec)` is deprecated, use `isinstance(treespec, TreeSpec) and treespec.is_leaf()` instead.
21
+ /usr/local/lib/python3.12/dist-packages/pytorch_lightning/utilities/model_summary/model_summary.py:242: Precision bf16-mixed is not supported by the model summary. Estimated model size in MB will not be accurate. Using 32 bits instead.
22
+ ┏━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━┳━━━━━━━┓
23
+ ┃   ┃ Name  ┃ Type  ┃ Params ┃ Mode  ┃ FLOPs ┃
24
+ ┡━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━╇━━━━━━━┩
25
+ │ 0 │ model │ LWDETR │ 30.2 M │ train │ 0 │
26
+ │ 1 │ criterion │ SetCriterion │ 0 │ train │ 0 │
27
+ │ 2 │ postprocess │ PostProcess │ 0 │ train │ 0 │
28
+ └───┴─────────────┴──────────────┴────────┴───────┴───────┘
29
+ Trainable params: 30.2 M
30
+ Non-trainable params: 0
31
+ Total params: 30.2 M
32
+ Total estimated model params size (MB): 120
33
+ Modules in train mode: 449
34
+ Modules in eval mode: 0
35
+ Total FLOPs: 0
36
+ Sanity Checking DataLoader 0: 100%|███████████████| 2/2 [00:03<00:00, 0.57it/s] Val — Overall Metrics
37
+ `use_return_dict` is deprecated! Use `return_dict` instead!
38
+ /usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/connectors/logger_connector/result.py:433: It is recommended to use `self.log('val/mAP_50_95', ..., sync_dist=True)` when logging on epoch level in distributed setting to accumulate the metric across devices.
39
+ /usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/connectors/logger_connector/result.py:433: It is recommended to use `self.log('val/mAP_50', ..., sync_dist=True)` when logging on epoch level in distributed setting to accumulate the metric across devices.
40
+ /usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/connectors/logger_connector/result.py:433: It is recommended to use `self.log('val/mAP_75', ..., sync_dist=True)` when logging on epoch level in distributed setting to accumulate the metric across devices.
41
+ /usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/connectors/logger_connector/result.py:433: It is recommended to use `self.log('val/mAR', ..., sync_dist=True)` when logging on epoch level in distributed setting to accumulate the metric across devices.
42
+ /usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/connectors/logger_connector/result.py:433: It is recommended to use `self.log('val/ema_mAP_50_95', ..., sync_dist=True)` when logging on epoch level in distributed setting to accumulate the metric across devices.
43
+ /usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/connectors/logger_connector/result.py:433: It is recommended to use `self.log('val/ema_mAP_50', ..., sync_dist=True)` when logging on epoch level in distributed setting to accumulate the metric across devices.
44
+ /usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/connectors/logger_connector/result.py:433: It is recommended to use `self.log('val/ema_mAR', ..., sync_dist=True)` when logging on epoch level in distributed setting to accumulate the metric across devices.
45
+ /usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/connectors/logger_connector/result.py:433: It is recommended to use `self.log('val/F1', ..., sync_dist=True)` when logging on epoch level in distributed setting to accumulate the metric across devices.
46
+ /usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/connectors/logger_connector/result.py:433: It is recommended to use `self.log('val/precision', ..., sync_dist=True)` when logging on epoch level in distributed setting to accumulate the metric across devices.
47
+ /usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/connectors/logger_connector/result.py:433: It is recommended to use `self.log('val/recall', ..., sync_dist=True)` when logging on epoch level in distributed setting to accumulate the metric across devices.
48
+ ┏━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━┓
49
+ ┃ mAP ┃ mAR ┃ F1 sweep ┃
50
+ ┡━━━━━━━━┯━━━━━━━━┯━━━━━━━━╇━━━━━━━━╇━━━━━━━━┯━━━━━━━━┯━━━━━━━━┩
51
+ │ 50:95 │ 50 │ 75 │ @500 │ F1 │ Prec │ Recall │
52
+ ├────────┼────────┼────────┼────────┼────────┼────────┼────────┤
53
+ │ 0.0760 │ 0.1066 │ 0.0667 │ 0.1993 │ 0.0718 │ 0.0729 │ 0.1545 │
54
+ └────────┴────────┴────────┴────────┴────────┴────────┴────────┘
55
+  Val — Per-class Metrics 
56
+ ┏━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━┓
57
+ ┃ Class  ┃ AP 50:95 ┃  AR ┃  F1 ┃ Precision ┃ Recall ┃
58
+ ┡━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━┩
59
+ │ Hatchback  │ 0.0000 │ 0.0000 │ 0.0000 │ 0.0000 │ 0.0000 │
60
+ │ Sedan  │ 0.0000 │ 0.0000 │ 0.0000 │ 0.0000 │ 0.0000 │
61
+ │ SUV  │ 0.0000 │ 0.0000 │ 0.0000 │ 0.0000 │ 0.0000 │
62
+ │ MUV  │ 0.3020 │ 0.9000 │ 0.1176 │ 0.0625 │ 1.0000 │
63
+ │ Bus  │ 0.0000 │ 0.0000 │ 0.0000 │ 0.0000 │ 0.0000 │
64
+ │ Truck  │ 0.0000 │ 0.0000 │ 0.0000 │ 0.0000 │ 0.0000 │
65
+ │ Three-wheeler │ 0.1000 │ 0.3857 │ 0.0000 │ 0.0000 │ 0.0000 │
66
+ │ Two-wheeler  │ 0.0000 │ 0.0000 │ 0.0000 │ 0.0000 │ 0.0000 │
67
+ │ LCV  │ 0.3568 │ 0.6818 │ 0.6000 │ 0.6667 │ 0.5455 │
68
+ │ Bicycle  │ 0.0009 │ 0.0250 │ 0.0000 │ 0.0000 │ 0.0000 │
69
+ └───────────────┴──────────┴────────┴────────┴───────────┴────────┘
70
+ /usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/connectors/logger_connector/result.py:433: It is recommended to use `self.log('val/AP/Hatchback', ..., sync_dist=True)` when logging on epoch level in distributed setting to accumulate the metric across devices.
71
+ /usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/connectors/logger_connector/result.py:433: It is recommended to use `self.log('val/AP/Sedan', ..., sync_dist=True)` when logging on epoch level in distributed setting to accumulate the metric across devices.
72
+ /usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/connectors/logger_connector/result.py:433: It is recommended to use `self.log('val/AP/SUV', ..., sync_dist=True)` when logging on epoch level in distributed setting to accumulate the metric across devices.
73
+ /usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/connectors/logger_connector/result.py:433: It is recommended to use `self.log('val/AP/MUV', ..., sync_dist=True)` when logging on epoch level in distributed setting to accumulate the metric across devices.
74
+ /usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/connectors/logger_connector/result.py:433: It is recommended to use `self.log('val/AP/Bus', ..., sync_dist=True)` when logging on epoch level in distributed setting to accumulate the metric across devices.
75
+ /usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/connectors/logger_connector/result.py:433: It is recommended to use `self.log('val/AP/Truck', ..., sync_dist=True)` when logging on epoch level in distributed setting to accumulate the metric across devices.
76
+ /usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/connectors/logger_connector/result.py:433: It is recommended to use `self.log('val/AP/Three-wheeler', ..., sync_dist=True)` when logging on epoch level in distributed setting to accumulate the metric across devices.
77
+ /usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/connectors/logger_connector/result.py:433: It is recommended to use `self.log('val/AP/Two-wheeler', ..., sync_dist=True)` when logging on epoch level in distributed setting to accumulate the metric across devices.
78
+ /usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/connectors/logger_connector/result.py:433: It is recommended to use `self.log('val/AP/LCV', ..., sync_dist=True)` when logging on epoch level in distributed setting to accumulate the metric across devices.
79
+ /usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/connectors/logger_connector/result.py:433: It is recommended to use `self.log('val/AP/Bicycle', ..., sync_dist=True)` when logging on epoch level in distributed setting to accumulate the metric across devices.
80
+ [2026-04-16 08:43:36] [INFO] rf-detr - Best EMA mAP improved to 0.0752 (epoch 0)
81
+ Epoch 0: 100%|█| 2332/2332 [26:45<00:00, 1.45it/s, v_num=cwrn, train/lr=0.0001, Val — Overall Metrics
82
+ /usr/local/lib/python3.12/dist-packages/torch/autograd/graph.py:865: UserWarning: The AccumulateGrad node's stream does not match the stream of the node that produced the incoming gradient. This may incur unnecessary synchronization and break CUDA graph capture if the AccumulateGrad node's stream is the default stream. This mismatch is caused by an AccumulateGrad node created prior to the current iteration being kept alive. This can happen if the autograd graph is still being kept alive by tensors such as the loss, or if you are using DDP, which will stash a reference to the node. To resolve the mismatch, delete all references to the autograd graph or ensure that DDP initialization is performed under the same stream as subsequent forwards. If the mismatch is intentional, you can use torch.autograd.graph.set_warn_on_accumulate_grad_stream_mismatch(False) to suppress this warning. (Triggered internally at /pytorch/torch/csrc/autograd/input_buffer.cpp:240.)
83
+ return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
84
+ ┏━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━┓0:00, 2.57it/s]
85
+ ┃ mAP ┃ mAR ┃ F1 sweep ┃
86
+ ┡━━━━━━━━┯━━━━━━━━┯━━━━━━━━╇━━━━━━━━╇━━━━━━━━┯━━━━━━━━┯━━━━━━━━┩
87
+ │ 50:95 │ 50 │ 75 │ @500 │ F1 │ Prec │ Recall │
88
+ ├────────┼────────┼────────┼────────┼────────┼────────┼────────┤
89
+ │ 0.4693 │ 0.5917 │ 0.5193 │ 0.7808 │ 0.5581 │ 0.5532 │ 0.6098 │
90
+ └────────┴────────┴────────┴────────┴────────┴────────┴────────┘
91
+  Val — Per-class Metrics 
92
+ ┏━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━┓
93
+ ┃ Class  ┃ AP 50:95 ┃  AR ┃  F1 ┃ Precision ┃ Recall ┃
94
+ ┡━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━┩
95
+ │ Hatchback  │ 0.4720 │ 0.8008 │ 0.5870 │ 0.4795 │ 0.7565 │
96
+ │ Sedan  │ 0.5116 │ 0.8319 │ 0.5240 │ 0.3965 │ 0.7725 │
97
+ │ SUV  │ 0.4164 │ 0.8221 │ 0.4666 │ 0.3549 │ 0.6808 │
98
+ │ MUV  │ 0.3728 │ 0.8478 │ 0.4264 │ 0.3618 │ 0.5190 │
99
+ │ Bus  │ 0.6229 │ 0.7646 │ 0.7381 │ 0.7936 │ 0.6898 │
100
+ │ Truck  │ 0.5133 │ 0.7824 │ 0.6299 │ 0.6586 │ 0.6036 │
101
+ │ Three-wheeler  │ 0.6544 │ 0.7414 │ 0.8136 │ 0.8196 │ 0.8078 │
102
+ │ Two-wheeler  │ 0.5623 │ 0.6686 │ 0.7787 │ 0.7652 │ 0.7927 │
103
+ │ LCV  │ 0.5347 │ 0.7746 │ 0.6280 │ 0.5433 │ 0.7439 │
104
+ │ Mini-bus  │ 0.1654 │ 0.7459 │ 0.2463 │ 0.3367 │ 0.1941 │
105
+ │ Tempo-traveller │ 0.6034 │ 0.8414 │ 0.6275 │ 0.5783 │ 0.6857 │
106
+ │ Bicycle  │ 0.4120 │ 0.7033 │ 0.5488 │ 0.5732 │ 0.5265 │
107
+ │ Van  │ 0.2593 │ 0.8263 │ 0.2400 │ 0.5308 │ 0.1551 │
108
+ └─────────────────┴──────────┴────────┴────────┴───────────┴────────┘
109
+ /usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/connectors/logger_connector/result.py:433: It is recommended to use `self.log('val/AP/Mini-bus', ..., sync_dist=True)` when logging on epoch level in distributed setting to accumulate the metric across devices.
110
+ /usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/connectors/logger_connector/result.py:433: It is recommended to use `self.log('val/AP/Tempo-traveller', ..., sync_dist=True)` when logging on epoch level in distributed setting to accumulate the metric across devices.
111
+ /usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/connectors/logger_connector/result.py:433: It is recommended to use `self.log('val/AP/Van', ..., sync_dist=True)` when logging on epoch level in distributed setting to accumulate the metric across devices.
112
+ Epoch 0: 100%|█| 2332/2332 [36:05<00:00, 1.08it/s, v_num=cwrn, train/lr=0.0001,[2026-04-16 09:19:41] [INFO] rf-detr - Best regular mAP saved to /kaggle/working/output/checkpoint_best_regular.pth (epoch 0)
113
+ [2026-04-16 09:19:42] [INFO] rf-detr - Best EMA mAP improved to 0.4785 (epoch 0)
114
+ [rank: 0] Metric __rfdetr_effective_map__ improved. New best score: 0.479
115
+ /usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/connectors/logger_connector/result.py:433: It is recommended to use `self.log('train/loss_ce', ..., sync_dist=True)` when logging on epoch level in distributed setting to accumulate the metric across devices.
116
+ /usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/connectors/logger_connector/result.py:433: It is recommended to use `self.log('train/class_error', ..., sync_dist=True)` when logging on epoch level in distributed setting to accumulate the metric across devices.
117
+ /usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/connectors/logger_connector/result.py:433: It is recommended to use `self.log('train/loss_bbox', ..., sync_dist=True)` when logging on epoch level in distributed setting to accumulate the metric across devices.
118
+ /usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/connectors/logger_connector/result.py:433: It is recommended to use `self.log('train/loss_giou', ..., sync_dist=True)` when logging on epoch level in distributed setting to accumulate the metric across devices.
119
+ /usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/connectors/logger_connector/result.py:433: It is recommended to use `self.log('train/cardinality_error', ..., sync_dist=True)` when logging on epoch level in distributed setting to accumulate the metric across devices.
120
+ /usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/connectors/logger_connector/result.py:433: It is recommended to use `self.log('train/loss_ce_0', ..., sync_dist=True)` when logging on epoch level in distributed setting to accumulate the metric across devices.
121
+ /usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/connectors/logger_connector/result.py:433: It is recommended to use `self.log('train/loss_bbox_0', ..., sync_dist=True)` when logging on epoch level in distributed setting to accumulate the metric across devices.
122
+ /usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/connectors/logger_connector/result.py:433: It is recommended to use `self.log('train/loss_giou_0', ..., sync_dist=True)` when logging on epoch level in distributed setting to accumulate the metric across devices.
123
+ /usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/connectors/logger_connector/result.py:433: It is recommended to use `self.log('train/cardinality_error_0', ..., sync_dist=True)` when logging on epoch level in distributed setting to accumulate the metric across devices.
124
+ /usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/connectors/logger_connector/result.py:433: It is recommended to use `self.log('train/loss_ce_enc', ..., sync_dist=True)` when logging on epoch level in distributed setting to accumulate the metric across devices.
125
+ /usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/connectors/logger_connector/result.py:433: It is recommended to use `self.log('train/loss_bbox_enc', ..., sync_dist=True)` when logging on epoch level in distributed setting to accumulate the metric across devices.
126
+ /usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/connectors/logger_connector/result.py:433: It is recommended to use `self.log('train/loss_giou_enc', ..., sync_dist=True)` when logging on epoch level in distributed setting to accumulate the metric across devices.
127
+ /usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/connectors/logger_connector/result.py:433: It is recommended to use `self.log('train/cardinality_error_enc', ..., sync_dist=True)` when logging on epoch level in distributed setting to accumulate the metric across devices.
128
+ /usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/connectors/logger_connector/result.py:433: It is recommended to use `self.log('train/loss', ..., sync_dist=True)` when logging on epoch level in distributed setting to accumulate the metric across devices.
129
+ Epoch 1: 100%|█| 2332/2332 [26:22<00:00, 1.47it/s, v_num=cwrn, train/lr=0.0001, Val — Overall Metrics
130
+ ┏━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━┓0:00, 2.65it/s]
131
+ ┃ mAP ┃ mAR ┃ F1 sweep ┃
132
+ ┡━━━━━━━━┯━━━━━━━━┯━━━━━━━━╇━━━━━━━━╇━━━━━━━━┯━━━━━━━━┯━━━━━━━━┩
133
+ │ 50:95 │ 50 │ 75 │ @500 │ F1 │ Prec │ Recall │
134
+ ├────────┼────────┼────────┼────────┼────────┼────────┼────────┤
135
+ │ 0.4958 │ 0.6231 │ 0.5498 │ 0.7890 │ 0.5968 │ 0.6143 │ 0.5979 │
136
+ └────────┴────────┴────────┴────────┴────────┴────────┴────────┘
137
+  Val — Per-class Metrics 
138
+ ┏━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━┓
139
+ ┃ Class  ┃ AP 50:95 ┃  AR ┃  F1 ┃ Precision ┃ Recall ┃
140
+ ┡━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━┩
141
+ │ Hatchback  │ 0.4950 │ 0.8035 │ 0.6038 │ 0.5371 │ 0.6894 │
142
+ │ Sedan  │ 0.5250 │ 0.8287 │ 0.5648 │ 0.6190 │ 0.5194 │
143
+ │ SUV  │ 0.4351 │ 0.8195 │ 0.5030 │ 0.5268 │ 0.4814 │
144
+ │ MUV  │ 0.4067 │ 0.8517 │ 0.4479 │ 0.3815 │ 0.5422 │
145
+ │ Bus  │ 0.6422 │ 0.7790 │ 0.7396 │ 0.7103 │ 0.7713 │
146
+ │ Truck  │ 0.5419 │ 0.7937 │ 0.6322 │ 0.5802 │ 0.6944 │
147
+ │ Three-wheeler  │ 0.6694 │ 0.7488 │ 0.8330 │ 0.8595 │ 0.8080 │
148
+ │ Two-wheeler  │ 0.5734 │ 0.6739 │ 0.7990 │ 0.8474 │ 0.7558 │
149
+ │ LCV  │ 0.5620 │ 0.7840 │ 0.6779 │ 0.6447 │ 0.7148 │
150
+ │ Mini-bus  │ 0.1980 │ 0.7765 │ 0.2905 │ 0.3413 │ 0.2529 │
151
+ │ Tempo-traveller │ 0.6346 │ 0.8514 │ 0.6604 │ 0.6206 │ 0.7057 │
152
+ │ Bicycle  │ 0.4340 │ 0.7031 │ 0.5576 │ 0.8324 │ 0.4192 │
153
+ │ Van  │ 0.3278 │ 0.8431 │ 0.4493 │ 0.4856 │ 0.4180 │
154
+ └─────────────────┴──────────┴────────┴────────┴───────────┴────────┘
155
+ Epoch 1: 100%|█| 2332/2332 [35:34<00:00, 1.09it/s, v_num=cwrn, train/lr=0.0001,[2026-04-16 09:55:19] [INFO] rf-detr - Best regular mAP saved to /kaggle/working/output/checkpoint_best_regular.pth (epoch 1)
156
+ [2026-04-16 09:55:19] [INFO] rf-detr - Best EMA mAP improved to 0.5131 (epoch 1)
157
+ [rank: 0] Metric __rfdetr_effective_map__ improved by 0.035 >= min_delta = 0.001. New best score: 0.513
158
+ Epoch 2: 100%|█| 2332/2332 [26:44<00:00, 1.45it/s, v_num=cwrn, train/lr=0.0001, Val — Overall Metrics
159
+ ┏━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━┓0:00, 2.58it/s]
160
+ ┃ mAP ┃ mAR ┃ F1 sweep ┃
161
+ ┡━━━━━━━━┯━━━━━━━━┯━━━━━━━━╇━━━━━━━━╇━━━━━━━━┯━━━━━━━━┯━━━━━━━━┩
162
+ │ 50:95 │ 50 │ 75 │ @500 │ F1 │ Prec │ Recall │
163
+ ├────────┼────────┼────────┼────────┼────────┼────────┼────────┤
164
+ │ 0.5111 │ 0.6380 │ 0.5660 │ 0.7944 │ 0.6154 │ 0.6272 │ 0.6089 │
165
+ └────────┴────────┴────────┴────────┴────────┴────────┴────────┘
166
+  Val — Per-class Metrics 
167
+ ┏━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━┓
168
+ ┃ Class  ┃ AP 50:95 ┃  AR ┃  F1 ┃ Precision ┃ Recall ┃
169
+ ┡━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━┩
170
+ │ Hatchback  │ 0.5210 │ 0.8102 │ 0.6155 │ 0.5750 │ 0.6622 │
171
+ │ Sedan  │ 0.5487 │ 0.8402 │ 0.5903 │ 0.5773 │ 0.6039 │
172
+ │ SUV  │ 0.4556 │ 0.8269 │ 0.5279 │ 0.4983 │ 0.5612 │
173
+ │ MUV  │ 0.4354 │ 0.8509 │ 0.4966 │ 0.5259 │ 0.4705 │
174
+ │ Bus  │ 0.6458 │ 0.7811 │ 0.7581 │ 0.8159 │ 0.7079 │
175
+ │ Truck  │ 0.5601 │ 0.7980 │ 0.6533 │ 0.6112 │ 0.7016 │
176
+ │ Three-wheeler  │ 0.6710 │ 0.7521 │ 0.8376 │ 0.8952 │ 0.7870 │
177
+ │ Two-wheeler  │ 0.5848 │ 0.6860 │ 0.7957 │ 0.7874 │ 0.8042 │
178
+ │ LCV  │ 0.5675 │ 0.7840 │ 0.6821 │ 0.6554 │ 0.7109 │
179
+ │ Mini-bus  │ 0.2183 │ 0.7712 │ 0.3009 │ 0.3221 │ 0.2824 │
180
+ │ Tempo-traveller │ 0.6454 │ 0.8529 │ 0.7103 │ 0.7399 │ 0.6829 │
181
+ │ Bicycle  │ 0.4399 │ 0.7256 │ 0.5841 │ 0.6846 │ 0.5093 │
182
+ │ Van  │ 0.3510 │ 0.8479 │ 0.4476 │ 0.4649 │ 0.4315 │
183
+ └─────────────────┴──────────┴────────┴────────┴───────────┴────────┘
184
+ Epoch 2: 100%|█| 2332/2332 [36:03<00:00, 1.08it/s, v_num=cwrn, train/lr=0.0001,[2026-04-16 10:31:26] [INFO] rf-detr - Best regular mAP saved to /kaggle/working/output/checkpoint_best_regular.pth (epoch 2)
185
+ [2026-04-16 10:31:26] [INFO] rf-detr - Best EMA mAP improved to 0.5267 (epoch 2)
186
+ [rank: 0] Metric __rfdetr_effective_map__ improved by 0.014 >= min_delta = 0.001. New best score: 0.527
187
+ Epoch 3: 100%|█| 2332/2332 [26:44<00:00, 1.45it/s, v_num=cwrn, train/lr=0.0001, Val — Overall Metrics
188
+ ┏━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━┓0:00, 2.63it/s]
189
+ ┃ mAP ┃ mAR ┃ F1 sweep ┃
190
+ ┡━━━━━━━━┯━━━━━━━━┯━━━━━━━━╇━━━��━━━━╇━━━━━━━━┯━━━━━━━━┯━━━━━━━━┩
191
+ │ 50:95 │ 50 │ 75 │ @500 │ F1 │ Prec │ Recall │
192
+ ├────────┼────────┼────────┼────────┼────────┼────────┼────────┤
193
+ │ 0.5187 │ 0.6488 │ 0.5766 │ 0.7951 │ 0.6199 │ 0.6315 │ 0.6150 │
194
+ └────────┴────────┴────────┴────────┴────────┴────────┴────────┘
195
+  Val — Per-class Metrics 
196
+ ┏━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━┓
197
+ ┃ Class  ┃ AP 50:95 ┃  AR ┃  F1 ┃ Precision ┃ Recall ┃
198
+ ┡━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━┩
199
+ │ Hatchback  │ 0.5283 │ 0.8099 │ 0.6179 │ 0.5810 │ 0.6598 │
200
+ │ Sedan  │ 0.5563 │ 0.8390 │ 0.5929 │ 0.5426 │ 0.6536 │
201
+ │ SUV  │ 0.4559 │ 0.8290 │ 0.5290 │ 0.4809 │ 0.5879 │
202
+ │ MUV  │ 0.4417 │ 0.8530 │ 0.4901 │ 0.4528 │ 0.5340 │
203
+ │ Bus  │ 0.6618 │ 0.7963 │ 0.7660 │ 0.7683 │ 0.7637 │
204
+ │ Truck  │ 0.5735 │ 0.7978 │ 0.6753 │ 0.7318 │ 0.6269 │
205
+ │ Three-wheeler  │ 0.6765 │ 0.7544 │ 0.8400 │ 0.8827 │ 0.8013 │
206
+ │ Two-wheeler  │ 0.5817 │ 0.6835 │ 0.8036 │ 0.8395 │ 0.7707 │
207
+ │ LCV  │ 0.5791 │ 0.7863 │ 0.6971 │ 0.6894 │ 0.7050 │
208
+ │ Mini-bus  │ 0.2202 │ 0.7735 │ 0.2746 │ 0.3421 │ 0.2294 │
209
+ │ Tempo-traveller │ 0.6582 │ 0.8569 │ 0.7189 │ 0.7774 │ 0.6686 │
210
+ │ Bicycle  │ 0.4383 │ 0.7122 │ 0.5721 │ 0.5917 │ 0.5536 │
211
+ │ Van  │ 0.3719 │ 0.8449 │ 0.4810 │ 0.5297 │ 0.4404 │
212
+ └─────────────────┴──────────┴────────┴────────┴───────────┴────────┘
213
+ Epoch 3: 100%|█| 2332/2332 [35:59<00:00, 1.08it/s, v_num=cwrn, train/lr=0.0001,[2026-04-16 11:07:29] [INFO] rf-detr - Best regular mAP saved to /kaggle/working/output/checkpoint_best_regular.pth (epoch 3)
214
+ [2026-04-16 11:07:29] [INFO] rf-detr - Best EMA mAP improved to 0.5333 (epoch 3)
215
+ [rank: 0] Metric __rfdetr_effective_map__ improved by 0.007 >= min_delta = 0.001. New best score: 0.533
216
+ Epoch 4: 100%|█| 2332/2332 [26:34<00:00, 1.46it/s, v_num=cwrn, train/lr=0.0001, Val — Overall Metrics
217
+ ┏━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━┓0:00, 2.58it/s]
218
+ ┃ mAP ┃ mAR ┃ F1 sweep ┃
219
+ ┡━━━━━━━━┯━━━━━━━━┯━━━━━━━━╇━━━━━━━━╇━━━━━━━━┯━━━━━━━━┯━━━━━━━━┩
220
+ │ 50:95 │ 50 │ 75 │ @500 │ F1 │ Prec │ Recall │
221
+ ├────────┼────────┼────────┼────────┼────────┼────────┼────────┤
222
+ │ 0.5239 │ 0.6520 │ 0.5805 │ 0.7960 │ 0.6193 │ 0.6178 │ 0.6362 │
223
+ └────────┴────────┴────────┴────────┴────────┴────────┴────────┘
224
+  Val — Per-class Metrics 
225
+ ┏━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━���━━━━━━━━┓
226
+ ┃ Class  ┃ AP 50:95 ┃  AR ┃  F1 ┃ Precision ┃ Recall ┃
227
+ ┡━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━┩
228
+ │ Hatchback  │ 0.5354 │ 0.8121 │ 0.6205 │ 0.5359 │ 0.7370 │
229
+ │ Sedan  │ 0.5645 │ 0.8414 │ 0.5758 │ 0.4703 │ 0.7425 │
230
+ │ SUV  │ 0.4675 │ 0.8322 │ 0.5361 │ 0.5557 │ 0.5179 │
231
+ │ MUV  │ 0.4286 │ 0.8556 │ 0.4862 │ 0.4382 │ 0.5459 │
232
+ │ Bus  │ 0.6611 │ 0.7872 │ 0.7666 │ 0.7770 │ 0.7566 │
233
+ │ Truck  │ 0.5720 │ 0.7968 │ 0.6793 │ 0.6868 │ 0.6719 │
234
+ │ Three-wheeler  │ 0.6857 │ 0.7597 │ 0.8425 │ 0.8571 │ 0.8284 │
235
+ │ Two-wheeler  │ 0.5890 │ 0.6905 │ 0.8016 │ 0.7994 │ 0.8039 │
236
+ │ LCV  │ 0.5831 │ 0.7907 │ 0.6643 │ 0.5819 │ 0.7739 │
237
+ │ Mini-bus  │ 0.2415 │ 0.7671 │ 0.3303 │ 0.3374 │ 0.3235 │
238
+ │ Tempo-traveller │ 0.6527 │ 0.8491 │ 0.7122 │ 0.8264 │ 0.6257 │
239
+ │ Bicycle  │ 0.4402 │ 0.7246 │ 0.5758 │ 0.5933 │ 0.5594 │
240
+ │ Van  │ 0.3890 │ 0.8409 │ 0.4597 │ 0.5719 │ 0.3843 │
241
+ └─────────────────┴──────────┴────────┴────────┴───────────┴────────┘
242
+ Epoch 4: 100%|█| 2332/2332 [35:51<00:00, 1.08it/s, v_num=cwrn, train/lr=0.0001,[2026-04-16 11:43:24] [INFO] rf-detr - Best regular mAP saved to /kaggle/working/output/checkpoint_best_regular.pth (epoch 4)
243
+ [2026-04-16 11:43:24] [INFO] rf-detr - Best EMA mAP improved to 0.5407 (epoch 4)
244
+ [rank: 0] Metric __rfdetr_effective_map__ improved by 0.007 >= min_delta = 0.001. New best score: 0.541
245
+ Epoch 5: 100%|█| 2332/2332 [26:37<00:00, 1.46it/s, v_num=cwrn, train/lr=0.0001, Val — Overall Metrics
246
+ ┏━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━┓0:00, 2.54it/s]
247
+ ┃ mAP ┃ mAR ┃ F1 sweep ┃
248
+ ┡━━━━━━━━┯━━━━━━━━┯━━━━━━━━╇━━━━━━━━╇━━━━━━━━┯━━━━━━━━┯━━━━━━━━┩
249
+ │ 50:95 │ 50 │ 75 │ @500 │ F1 │ Prec │ Recall │
250
+ ├────────┼────────┼────────┼────────┼────────┼────────┼────────┤
251
+ │ 0.5320 │ 0.6610 │ 0.5895 │ 0.7967 │ 0.6287 │ 0.6259 │ 0.6400 │
252
+ └────────┴────────┴────────┴────────┴────────┴────────┴────────┘
253
+  Val — Per-class Metrics 
254
+ ┏━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━┓
255
+ ┃ Class  ┃ AP 50:95 ┃  AR ┃  F1 ┃ Precision ┃ Recall ┃
256
+ ┡━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━┩
257
+ │ Hatchback  │ 0.5478 │ 0.8177 │ 0.6247 │ 0.5349 │ 0.7509 │
258
+ │ Sedan  │ 0.5679 │ 0.8401 │ 0.6012 │ 0.6300 │ 0.5749 │
259
+ │ SUV  │ 0.4788 │ 0.8328 │ 0.5430 │ 0.5560 │ 0.5307 │
260
+ │ MUV  │ 0.4638 │ 0.8583 │ 0.5076 │ 0.5037 │ 0.5116 │
261
+ │ Bus  │ 0.6558 │ 0.7869 │ 0.7673 │ 0.8338 │ 0.7106 │
262
+ │ Truck  │ 0.5732 │ 0.8001 │ 0.6641 │ 0.6199 │ 0.7150 │
263
+ │ Three-wheeler  │ 0.6879 │ 0.7626 │ 0.8399 │ 0.8553 │ 0.8251 │
264
+ │ Two-wheeler  │ 0.5923 │ 0.6882 │ 0.8021 │ 0.7863 │ 0.8185 │
265
+ │ LCV  │ 0.5827 │ 0.7898 │ 0.6904 │ 0.6602 │ 0.7234 │
266
+ │ Mini-bus  │ 0.2400 │ 0.7441 │ 0.3099 │ 0.3860 │ 0.2588 │
267
+ │ Tempo-traveller │ 0.6755 │ 0.8531 │ 0.7075 │ 0.6397 │ 0.7914 │
268
+ │ Bicycle  │ 0.4599 │ 0.7242 │ 0.5908 │ 0.6334 │ 0.5536 │
269
+ │ Van  │ 0.3899 │ 0.8587 │ 0.5244 │ 0.4970 │ 0.5551 │
270
+ └─────────────────┴──────────┴────────┴────────┴───────────┴────────┘
271
+ Epoch 5: 100%|█| 2332/2332 [36:02<00:00, 1.08it/s, v_num=cwrn, train/lr=0.0001,[2026-04-16 12:19:29] [INFO] rf-detr - Best regular mAP saved to /kaggle/working/output/checkpoint_best_regular.pth (epoch 5)
272
+ [2026-04-16 12:19:30] [INFO] rf-detr - Best EMA mAP improved to 0.5448 (epoch 5)
273
+ [rank: 0] Metric __rfdetr_effective_map__ improved by 0.004 >= min_delta = 0.001. New best score: 0.545
274
+ Epoch 6: 100%|█| 2332/2332 [26:28<00:00, 1.47it/s, v_num=cwrn, train/lr=0.0001, Val — Overall Metrics
275
+ ┏━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━┓0:00, 2.64it/s]
276
+ ┃ mAP ┃ mAR ┃ F1 sweep ┃
277
+ ┡━━━━━━━━┯━━━━━━━━┯━━━━━━━━╇━━━━━━━━╇━━━━━━━━┯━━━━━━━━┯━━━━━━━━┩
278
+ │ 50:95 │ 50 │ 75 │ @500 │ F1 │ Prec │ Recall │
279
+ ├────────┼────────┼────────┼────────┼────────┼────────┼────────┤
280
+ │ 0.5330 │ 0.6637 │ 0.5924 │ 0.7959 │ 0.6333 │ 0.6361 │ 0.6370 │
281
+ └────────┴────────┴────────┴────────┴────────┴────────┴────────┘
282
+  Val — Per-class Metrics 
283
+ ┏━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━┓
284
+ ┃ Class  ┃ AP 50:95 ┃  AR ┃  F1 ┃ Precision ┃ Recall ┃
285
+ ┡━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━┩
286
+ │ Hatchback  │ 0.5429 │ 0.8099 │ 0.6274 │ 0.5641 │ 0.7067 │
287
+ │ Sedan  │ 0.5675 │ 0.8404 │ 0.6082 │ 0.6191 │ 0.5977 │
288
+ │ SUV  │ 0.4823 │ 0.8287 │ 0.5397 │ 0.6046 │ 0.4874 │
289
+ │ MUV  │ 0.4610 │ 0.8488 │ 0.5116 │ 0.4667 │ 0.5661 │
290
+ │ Bus  │ 0.6547 │ 0.7833 │ 0.7734 │ 0.8383 │ 0.7177 │
291
+ │ Truck  │ 0.5745 │ 0.7971 │ 0.6807 │ 0.7004 │ 0.6620 │
292
+ │ Three-wheeler  │ 0.6868 │ 0.7590 │ 0.8448 │ 0.8675 │ 0.8233 │
293
+ │ Two-wheeler  │ 0.5922 │ 0.6891 │ 0.8101 │ 0.8308 │ 0.7903 │
294
+ │ LCV  │ 0.5789 │ 0.7900 │ 0.6829 │ 0.6294 │ 0.7463 │
295
+ │ Mini-bus  │ 0.2591 │ 0.7682 │ 0.3208 │ 0.3821 │ 0.2765 │
296
+ │ Tempo-traveller │ 0.6725 │ 0.8491 │ 0.7308 │ 0.7037 │ 0.7600 │
297
+ │ Bicycle  │ 0.4526 │ 0.7250 │ 0.5844 │ 0.5795 │ 0.5894 │
298
+ │ Van  │ 0.4045 │ 0.8575 │ 0.5177 │ 0.4834 │ 0.5573 │
299
+ └─────────────────┴──────────┴────────┴───��────┴───────────┴────────┘
300
+ Epoch 6: 100%|█| 2332/2332 [35:32<00:00, 1.09it/s, v_num=cwrn, train/lr=0.0001,[2026-04-16 12:55:05] [INFO] rf-detr - Best regular mAP saved to /kaggle/working/output/checkpoint_best_regular.pth (epoch 6)
301
+ [2026-04-16 12:55:06] [INFO] rf-detr - Best EMA mAP improved to 0.5517 (epoch 6)
302
+ [rank: 0] Metric __rfdetr_effective_map__ improved by 0.007 >= min_delta = 0.001. New best score: 0.552
303
+ Epoch 7: 100%|█| 2332/2332 [26:15<00:00, 1.48it/s, v_num=cwrn, train/lr=0.0001, Val — Overall Metrics
304
+ ┏━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━┓0:00, 2.58it/s]
305
+ ┃ mAP ┃ mAR ┃ F1 sweep ┃
306
+ ┡━━━━━━━━┯━━━━━━━━┯━━━━━━━━╇━━━━━━━━╇━━━━━━━━┯━━━━━━━━┯━━━━━━━━┩
307
+ │ 50:95 │ 50 │ 75 │ @500 │ F1 │ Prec │ Recall │
308
+ ├────────┼────────┼────────┼────────┼────────┼────────┼────────┤
309
+ │ 0.5388 │ 0.6677 │ 0.5978 │ 0.8013 │ 0.6389 │ 0.6641 │ 0.6263 │
310
+ └────────┴────────┴────────┴────────┴────────┴────────┴────────┘
311
+  Val — Per-class Metrics 
312
+ ┏━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━┓
313
+ ┃ Class  ┃ AP 50:95 ┃  AR ┃  F1 ┃ Precision ┃ Recall ┃
314
+ ┡━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━┩
315
+ │ Hatchback  │ 0.5514 │ 0.8202 │ 0.6306 │ 0.5349 │ 0.7682 │
316
+ │ Sedan  │ 0.5756 │ 0.8476 │ 0.6101 │ 0.5705 │ 0.6557 │
317
+ │ SUV  │ 0.4919 │ 0.8328 │ 0.5548 │ 0.5567 │ 0.5529 │
318
+ │ MUV  │ 0.4623 │ 0.8586 │ 0.5027 │ 0.5677 │ 0.4511 │
319
+ │ Bus  │ 0.6696 │ 0.7862 │ 0.7788 │ 0.8030 │ 0.7560 │
320
+ │ Truck  │ 0.5790 │ 0.8005 │ 0.6783 │ 0.7179 │ 0.6429 │
321
+ │ Three-wheeler  │ 0.6929 │ 0.7686 │ 0.8497 │ 0.8841 │ 0.8179 │
322
+ │ Two-wheeler  │ 0.6002 │ 0.7001 │ 0.8167 │ 0.8411 │ 0.7936 │
323
+ │ LCV  │ 0.5891 │ 0.7891 │ 0.7034 │ 0.7176 │ 0.6898 │
324
+ │ Mini-bus  │ 0.2374 │ 0.7671 │ 0.3066 │ 0.4038 │ 0.2471 │
325
+ │ Tempo-traveller │ 0.6815 │ 0.8546 │ 0.7374 │ 0.7039 │ 0.7743 │
326
+ │ Bicycle  │ 0.4717 │ 0.7368 │ 0.6227 │ 0.7475 │ 0.5336 │
327
+ │ Van  │ 0.4023 │ 0.8551 │ 0.5139 │ 0.5845 │ 0.4584 │
328
+ └─────────────────┴──────────┴────────┴────────┴───────────┴────────┘
329
+ Epoch 7: 100%|█| 2332/2332 [35:32<00:00, 1.09it/s, v_num=cwrn, train/lr=0.0001,[2026-04-16 13:30:41] [INFO] rf-detr - Best regular mAP saved to /kaggle/working/output/checkpoint_best_regular.pth (epoch 7)
330
+ [2026-04-16 13:30:42] [INFO] rf-detr - Best EMA mAP improved to 0.5537 (epoch 7)
331
+ [rank: 0] Metric __rfdetr_effective_map__ improved by 0.002 >= min_delta = 0.001. New best score: 0.554
332
+ Epoch 7: 100%|█| 2332/2332 [35:36<00:00, 1.09it/s, v_num=cwrn, train/lr=0.0001,
333
+ `Trainer.fit` stopped: `max_epochs=8` reached.
334
+ [2026-04-16 13:30:45] [INFO] rf-detr - Best total checkpoint saved from EMA (regular=0.5388, ema=0.5537)
wandb/run-20260416_084308-rucpcwrn/files/requirements.txt ADDED
@@ -0,0 +1,957 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ setuptools==75.2.0
2
+ types-setuptools==80.10.0.20260124
3
+ requirements-parser==0.9.0
4
+ faster-coco-eval==1.7.2
5
+ hf-xet==1.4.3
6
+ pip==26.0.1
7
+ fsspec==2025.3.0
8
+ supervision==0.27.0.post2
9
+ pyDeprecate==0.5.0
10
+ transformers==5.5.4
11
+ rfdetr==1.6.4
12
+ google-cloud-bigquery-storage==2.37.0
13
+ huggingface_hub==1.10.2
14
+ lightning==2.6.1
15
+ pytools==2025.2.5
16
+ pycuda==2026.1
17
+ siphash24==1.8
18
+ protobuf==5.29.5
19
+ torchtune==0.6.1
20
+ learntools==0.3.5
21
+ rouge_score==0.1.2
22
+ pyclipper==1.4.0
23
+ urwid_readline==0.15.1
24
+ h2o==3.46.0.10
25
+ rfc3161-client==1.0.5
26
+ blake3==1.0.8
27
+ mpld3==0.5.12
28
+ qgrid==1.3.1
29
+ ConfigSpace==1.2.2
30
+ woodwork==0.31.0
31
+ ujson==5.12.0
32
+ y-py==0.6.2
33
+ ipywidgets==8.1.5
34
+ scikit-multilearn==0.2.0
35
+ lightning-utilities==0.15.3
36
+ pytesseract==0.3.13
37
+ Cartopy==0.25.0
38
+ odfpy==1.4.1
39
+ Boruta==0.4.3
40
+ docstring-to-markdown==0.17
41
+ torchinfo==1.8.0
42
+ clint==0.5.1
43
+ comm==0.2.3
44
+ Deprecated==1.3.1
45
+ pymongo==4.16.0
46
+ tensorflow-io-gcs-filesystem==0.37.1
47
+ jmespath==1.1.0
48
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49
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50
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51
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52
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53
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54
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55
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56
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57
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58
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61
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62
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63
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65
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66
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68
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69
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70
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71
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72
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73
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74
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75
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76
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77
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78
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79
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80
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82
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83
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84
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85
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86
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87
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88
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89
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90
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91
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92
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93
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94
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95
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96
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97
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98
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99
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100
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101
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102
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103
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104
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105
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106
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107
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108
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109
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110
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111
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112
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113
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114
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115
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116
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117
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118
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119
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120
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121
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122
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123
+ path.py==12.5.0
124
+ tensorflow-io==0.37.1
125
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126
+ pdf2image==1.17.0
127
+ line_profiler==5.0.2
128
+ aiobotocore==3.3.0
129
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130
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131
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132
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133
+ Janome==0.5.0
134
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135
+ sigstore-models==0.0.6
136
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137
+ pyaml==26.2.1
138
+ scikit-plot==0.3.7
139
+ nbdev==3.0.12
140
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141
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142
+ filetype==1.2.0
143
+ Wand==0.7.0
144
+ jupyter_server_ydoc==0.8.0
145
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146
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147
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148
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149
+ ruamel.yaml==0.19.1
150
+ python-lsp-server==1.14.0
151
+ black==26.3.1
152
+ PyArabic==0.6.15
153
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154
+ path==17.1.1
155
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156
+ pypdf==6.9.1
157
+ TPOT==1.1.0
158
+ Pympler==1.1
159
+ bayesian-optimization==3.2.1
160
+ nbconvert==6.4.5
161
+ kornia==0.8.2
162
+ pathspec==1.0.4
163
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164
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165
+ funcy==2.0
166
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167
+ testpath==0.6.0
168
+ aioitertools==0.13.0
169
+ google-cloud-vision==3.12.1
170
+ ray==2.54.0
171
+ kornia_rs==0.1.10
172
+ traitlets==5.14.3
173
+ gymnax==0.0.8
174
+ dnspython==2.8.0
175
+ chex==0.1.90
176
+ gym==0.26.2
177
+ nbclient==0.5.13
178
+ ydata-profiling==4.18.1
179
+ POT==0.9.6.post1
180
+ deepdiff==8.6.2
181
+ squarify==0.4.4
182
+ dataclasses-json==0.6.7
183
+ pettingzoo==1.24.0
184
+ pytorch-lightning==2.6.1
185
+ segment_anything==1.0
186
+ emoji==2.15.0
187
+ python-bidi==0.6.7
188
+ rgf-python==3.12.0
189
+ ninja==1.13.0
190
+ widgetsnbextension==4.0.15
191
+ minify_html==0.18.1
192
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193
+ jedi==0.19.2
194
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195
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196
+ QtPy==2.4.3
197
+ pydicom==3.0.1
198
+ multimethod==1.12
199
+ torchmetrics==1.9.0
200
+ asttokens==3.0.1
201
+ docker==7.1.0
202
+ dask-expr==2.0.0
203
+ s3transfer==0.16.0
204
+ build==1.4.0
205
+ Shimmy==2.0.0
206
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207
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208
+ jupyterlab_server==2.28.0
209
+ isoweek==1.3.3
210
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211
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212
+ orderly-set==5.5.0
213
+ pyexcel-io==0.6.7
214
+ catboost==1.2.10
215
+ kagglesdk==0.1.16
216
+ mamba==0.11.3
217
+ dipy==1.12.0
218
+ colorlog==6.10.1
219
+ asn1crypto==1.5.1
220
+ pyexcel-ods==0.6.0
221
+ lime==0.2.0.1
222
+ pox==0.3.7
223
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224
+ sigstore-rekor-types==0.0.18
225
+ cesium==0.12.4
226
+ boto3==1.42.70
227
+ tuf==6.0.0
228
+ hep_ml==0.8.0
229
+ pyproject_hooks==1.2.0
230
+ phik==0.12.5
231
+ pudb==2025.1.5
232
+ mne==1.11.0
233
+ keras-cv==0.9.0
234
+ dill==0.4.1
235
+ gatspy==0.3
236
+ scikit-learn-intelex==2025.11.0
237
+ onnx==1.20.1
238
+ scikit-optimize==0.10.2
239
+ category_encoders==2.9.0
240
+ mypy_extensions==1.1.0
241
+ mistune==0.8.4
242
+ json5==0.13.0
243
+ google-colab==1.0.0
244
+ psutil==5.9.5
245
+ jsonschema==4.26.0
246
+ astunparse==1.6.3
247
+ pycocotools==2.0.11
248
+ lxml==6.0.2
249
+ ipython==7.34.0
250
+ oauthlib==3.3.1
251
+ grpc-google-iam-v1==0.14.3
252
+ array_record==0.8.3
253
+ PuLP==3.3.0
254
+ nvidia-cuda-runtime-cu12==12.8.90
255
+ dask-cuda==26.2.0
256
+ immutabledict==4.3.1
257
+ peewee==4.0.0
258
+ fiona==1.10.1
259
+ aiosignal==1.4.0
260
+ libclang==18.1.1
261
+ annotated-types==0.7.0
262
+ spreg==1.8.5
263
+ grain==0.2.15
264
+ geemap==0.35.3
265
+ patsy==1.0.2
266
+ imagesize==1.4.1
267
+ py-cpuinfo==9.0.0
268
+ pyzmq==26.2.1
269
+ nvidia-cufile-cu12==1.13.1.3
270
+ multidict==6.7.1
271
+ srsly==2.5.2
272
+ intel-openmp==2025.3.2
273
+ uuid_utils==0.14.1
274
+ google-cloud-language==2.19.0
275
+ soxr==1.0.0
276
+ jupyterlab_pygments==0.3.0
277
+ backcall==0.2.0
278
+ tensorflow-hub==0.16.1
279
+ google==3.0.0
280
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281
+ dopamine_rl==4.1.2
282
+ overrides==7.7.0
283
+ db-dtypes==1.5.0
284
+ jeepney==0.9.0
285
+ langgraph-sdk==0.3.9
286
+ ipython-genutils==0.2.0
287
+ nvidia-cuda-cupti-cu12==12.8.90
288
+ libcugraph-cu12==26.2.0
289
+ catalogue==2.0.10
290
+ beautifulsoup4==4.13.5
291
+ nvidia-ml-py==13.590.48
292
+ sphinxcontrib-devhelp==2.0.0
293
+ partd==1.4.2
294
+ sklearn-pandas==2.2.0
295
+ sphinxcontrib-qthelp==2.0.0
296
+ google-cloud-spanner==3.63.0
297
+ h5py==3.15.1
298
+ python-box==7.4.1
299
+ distributed-ucxx-cu12==0.48.0
300
+ xlrd==2.0.2
301
+ branca==0.8.2
302
+ chardet==5.2.0
303
+ pycairo==1.29.0
304
+ Authlib==1.6.8
305
+ cuda-core==0.3.2
306
+ sentencepiece==0.2.1
307
+ nvidia-cusparselt-cu12==0.7.1
308
+ matplotlib-venn==1.1.2
309
+ scooby==0.11.0
310
+ fqdn==1.5.1
311
+ gin-config==0.5.0
312
+ ipython-sql==0.5.0
313
+ toml==0.10.2
314
+ PyOpenGL==3.1.10
315
+ weasel==0.4.3
316
+ jsonpointer==3.0.0
317
+ google-auth-httplib2==0.3.0
318
+ spint==1.0.7
319
+ nvtx==0.2.14
320
+ websocket-client==1.9.0
321
+ torchao==0.10.0
322
+ splot==1.1.7
323
+ langgraph-checkpoint==4.0.0
324
+ alabaster==1.0.0
325
+ jaxlib==0.7.2
326
+ google-resumable-media==2.8.0
327
+ namex==0.1.0
328
+ quantecon==0.11.0
329
+ nvidia-cuda-cccl-cu12==12.9.27
330
+ google-cloud-aiplatform==1.138.0
331
+ treelite==4.6.1
332
+ google-cloud-resource-manager==1.16.0
333
+ jupyter_core==5.9.1
334
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335
+ librosa==0.11.0
336
+ ibis-framework==9.5.0
337
+ requests-toolbelt==1.0.0
338
+ smart_open==7.5.1
339
+ tensorflow-metadata==1.17.3
340
+ pysal==25.7
341
+ highspy==1.13.1
342
+ click==8.3.1
343
+ markdown-it-py==4.0.0
344
+ nvidia-cusolver-cu12==11.7.3.90
345
+ cupy-cuda12x==14.0.1
346
+ imutils==0.5.4
347
+ grpclib==0.4.9
348
+ opt_einsum==3.4.0
349
+ folium==0.20.0
350
+ moviepy==1.0.3
351
+ opencv-python==4.13.0.92
352
+ en_core_web_sm==3.8.0
353
+ tensorflow-text==2.19.0
354
+ langchain-core==1.2.15
355
+ yarl==1.22.0
356
+ spacy==3.8.11
357
+ importlib_resources==6.5.2
358
+ peft==0.18.1
359
+ lazy_loader==0.4
360
+ polars-runtime-32==1.35.2
361
+ pylibcudf-cu12==26.2.1
362
+ bigquery-magics==0.10.3
363
+ spanner-graph-notebook==1.1.8
364
+ sqlglot==25.20.2
365
+ linkify-it-py==2.0.3
366
+ types-pytz==2025.2.0.20251108
367
+ tifffile==2026.2.20
368
+ tsfresh==0.21.1
369
+ nbclassic==1.3.3
370
+ scikit-image==0.25.2
371
+ tensorflow_decision_forests==1.12.0
372
+ simsimd==6.5.13
373
+ isoduration==20.11.0
374
+ momepy==0.11.0
375
+ pytest==8.4.2
376
+ nvidia-cuda-nvcc-cu12==12.5.82
377
+ cuda-bindings==12.9.4
378
+ torchsummary==1.5.1
379
+ earthengine-api==1.5.24
380
+ webencodings==0.5.1
381
+ optree==0.19.0
382
+ jax-cuda12-pjrt==0.7.2
383
+ langchain==1.2.10
384
+ safehttpx==0.1.7
385
+ holidays==0.91
386
+ google-cloud-firestore==2.23.0
387
+ fastjsonschema==2.21.2
388
+ pymc==5.28.0
389
+ pydantic==2.12.3
390
+ jaraco.context==6.1.0
391
+ pyogrio==0.12.1
392
+ numba-cuda==0.22.2
393
+ fonttools==4.61.1
394
+ httpimport==1.4.1
395
+ rsa==4.9.1
396
+ tomlkit==0.13.3
397
+ entrypoints==0.4
398
+ anyio==4.12.1
399
+ charset-normalizer==3.4.4
400
+ pooch==1.9.0
401
+ libcuml-cu12==26.2.0
402
+ astropy-iers-data==0.2026.2.23.0.48.33
403
+ ipyleaflet==0.20.0
404
+ cryptography==43.0.3
405
+ missingno==0.5.2
406
+ langgraph==1.0.9
407
+ pandas-datareader==0.10.0
408
+ pyviz_comms==3.0.6
409
+ cycler==0.12.1
410
+ tensorboard==2.19.0
411
+ gast==0.7.0
412
+ jax-cuda12-plugin==0.7.2
413
+ platformdirs==4.9.2
414
+ google-genai==1.64.0
415
+ inflect==7.5.0
416
+ httplib2==0.31.2
417
+ h11==0.16.0
418
+ alembic==1.18.4
419
+ multitasking==0.0.12
420
+ rmm-cu12==26.2.0
421
+ cvxpy==1.6.7
422
+ affine==2.4.0
423
+ cuml-cu12==26.2.0
424
+ pyparsing==3.3.2
425
+ cffi==2.0.0
426
+ h5netcdf==1.8.1
427
+ Markdown==3.10.2
428
+ google-cloud-translate==3.24.0
429
+ rpy2==3.5.17
430
+ regex==2025.11.3
431
+ tf_keras==2.19.0
432
+ google-auth==2.47.0
433
+ nvidia-libnvcomp-cu12==5.1.0.21
434
+ Send2Trash==2.1.0
435
+ cymem==2.0.13
436
+ pylibraft-cu12==26.2.0
437
+ shap==0.50.0
438
+ shapely==2.1.2
439
+ psygnal==0.15.1
440
+ uri-template==1.3.0
441
+ parso==0.8.6
442
+ webcolors==25.10.0
443
+ nltk==3.9.1
444
+ atpublic==5.1
445
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446
+ sphinxcontrib-applehelp==2.0.0
447
+ bigframes==2.35.0
448
+ pydot==4.0.1
449
+ onemkl-license==2025.3.1
450
+ treescope==0.1.10
451
+ tcmlib==1.4.1
452
+ opentelemetry-sdk==1.38.0
453
+ tiktoken==0.12.0
454
+ nibabel==5.3.3
455
+ multiprocess==0.70.16
456
+ typing_extensions==4.15.0
457
+ PyYAML==6.0.3
458
+ defusedxml==0.7.1
459
+ sphinxcontrib-serializinghtml==2.0.0
460
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461
+ tenacity==9.1.4
462
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463
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464
+ google-cloud-bigquery-connection==1.20.0
465
+ opentelemetry-resourcedetector-gcp==1.11.0a0
466
+ ormsgpack==1.12.2
467
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468
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469
+ openai==2.23.0
470
+ matplotlib==3.10.0
471
+ ml_dtypes==0.5.4
472
+ uvloop==0.22.1
473
+ google-pasta==0.2.0
474
+ giddy==2.3.8
475
+ ipyparallel==8.8.0
476
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477
+ cuvs-cu12==26.2.0
478
+ mcp==1.26.0
479
+ spacy-loggers==1.0.5
480
+ google-cloud-logging==3.13.0
481
+ rfc3987-syntax==1.1.0
482
+ google-ai-generativelanguage==0.6.15
483
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484
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485
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486
+ ydf==0.15.0
487
+ narwhals==2.17.0
488
+ nvidia-cusparse-cu12==12.5.8.93
489
+ openpyxl==3.1.5
490
+ nvidia-cublas-cu12==12.8.4.1
491
+ roman-numerals==4.1.0
492
+ vega-datasets==0.9.0
493
+ mpmath==1.3.0
494
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495
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496
+ osqp==1.1.1
497
+ traittypes==0.2.3
498
+ opentelemetry-exporter-gcp-monitoring==1.11.0a0
499
+ graphviz==0.21
500
+ google-cloud-trace==1.18.0
501
+ einops==0.8.2
502
+ torchdata==0.11.0
503
+ jax==0.7.2
504
+ cachetools==6.2.6
505
+ aiohappyeyeballs==2.6.1
506
+ annotated-doc==0.0.4
507
+ starlette==0.52.1
508
+ fastapi==0.133.0
509
+ typer==0.24.1
510
+ duckdb==1.3.2
511
+ blinker==1.9.0
512
+ referencing==0.37.0
513
+ googledrivedownloader==1.1.0
514
+ GDAL==3.8.4
515
+ cuda-python==12.9.4
516
+ pycparser==3.0
517
+ et_xmlfile==2.0.0
518
+ jieba==0.42.1
519
+ zict==3.0.0
520
+ hyperopt==0.2.7
521
+ python-louvain==0.16
522
+ SQLAlchemy==2.0.47
523
+ cuda-toolkit==12.8.1
524
+ PyDrive2==1.21.3
525
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526
+ urllib3==2.5.0
527
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528
+ optax==0.2.7
529
+ pyOpenSSL==24.2.1
530
+ jupyter-console==6.6.3
531
+ libkvikio-cu12==26.2.0
532
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533
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534
+ albumentations==2.0.8
535
+ jupytext==1.19.1
536
+ seaborn==0.13.2
537
+ librmm-cu12==26.2.0
538
+ cons==0.4.7
539
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540
+ matplotlib-inline==0.2.1
541
+ pynndescent==0.6.0
542
+ stringzilla==4.6.0
543
+ flatbuffers==25.12.19
544
+ omegaconf==2.3.0
545
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546
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547
+ pexpect==4.9.0
548
+ torchcodec==0.10.0+cu128
549
+ ptyprocess==0.7.0
550
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551
+ kiwisolver==1.4.9
552
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553
+ shellingham==1.5.4
554
+ soupsieve==2.8.3
555
+ snowballstemmer==3.0.1
556
+ propcache==0.4.1
557
+ ucxx-cu12==0.48.0
558
+ nbformat==5.10.4
559
+ python-snappy==0.7.3
560
+ rasterstats==0.20.0
561
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562
+ nest-asyncio==1.6.0
563
+ opencv-python-headless==4.13.0.92
564
+ notebook==6.5.7
565
+ flax==0.11.2
566
+ google-cloud-functions==1.22.0
567
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