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VehicleNet-RFDn

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  2. checkpoint_9.ckpt +3 -0
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  4. checkpoint_best_regular.pth +3 -0
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  21. wandb/run-20260404_165325-pld9ikbe/files/config.yaml +90 -0
  22. wandb/run-20260404_165325-pld9ikbe/files/output.log +566 -0
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  25. wandb/run-20260404_165325-pld9ikbe/files/wandb-summary.json +1 -0
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  28. wandb/run-20260404_165325-pld9ikbe/logs/debug.log +21 -0
  29. wandb/run-20260404_165325-pld9ikbe/run-pld9ikbe.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
+ [2026-04-04 16:50:52] [INFO] rf-detr - Building Roboflow train dataset with square resize at resolution 384
3
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+ [2026-04-04 16:50:52] [INFO] rf-detr - Built 1 Albumentations transforms from config
6
+ loading annotations into memory...
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+ Done (t=1.04s)
8
+ creating index...
9
+ index created!
10
+ [2026-04-04 16:50:54] [INFO] rf-detr - Building Roboflow val dataset with square resize at resolution 384
11
+ [2026-04-04 16:50:54] [INFO] rf-detr - Using multi-scale training with square resize and scales: [544]
12
+ [2026-04-04 16:50:54] [INFO] rf-detr - Built 1 Albumentations transforms from config
13
+ loading annotations into memory...
14
+ Done (t=0.27s)
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, 0.64it/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
+ [2026-04-04 16:51:00] [INFO] rf-detr - Best EMA mAP improved to 0.0752 (epoch 0)
81
+ Epoch 0: 0%| | 2/2331 [00:01<26:15, 1.48it/s, v_num=gl50, train/lr=0.0001, tr
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
+ Traceback (most recent call last):
85
+ File "/kaggle/working/train.py", line 8, in <module>
86
+ model.train(
87
+ File "/usr/local/lib/python3.12/dist-packages/rfdetr/detr.py", line 505, in train
88
+ trainer.fit(module, datamodule, ckpt_path=config.resume or None)
89
+ File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/trainer.py", line 584, in fit
90
+ call._call_and_handle_interrupt(
91
+ File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/call.py", line 48, in _call_and_handle_interrupt
92
+ return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs)
93
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
94
+ File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/strategies/launchers/subprocess_script.py", line 105, in launch
95
+ return function(*args, **kwargs)
96
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
97
+ File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/trainer.py", line 630, in _fit_impl
98
+ self._run(model, ckpt_path=ckpt_path, weights_only=weights_only)
99
+ File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/trainer.py", line 1079, in _run
100
+ results = self._run_stage()
101
+ ^^^^^^^^^^^^^^^^^
102
+ File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/trainer.py", line 1123, in _run_stage
103
+ self.fit_loop.run()
104
+ File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/loops/fit_loop.py", line 217, in run
105
+ self.advance()
106
+ File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/loops/fit_loop.py", line 465, in advance
107
+ self.epoch_loop.run(self._data_fetcher)
108
+ File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/loops/training_epoch_loop.py", line 153, in run
109
+ self.advance(data_fetcher)
110
+ File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/loops/training_epoch_loop.py", line 352, in advance
111
+ batch_output = self.automatic_optimization.run(trainer.optimizers[0], batch_idx, kwargs)
112
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
113
+ File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/loops/optimization/automatic.py", line 185, in run
114
+ closure()
115
+ File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/loops/optimization/automatic.py", line 146, in __call__
116
+ self._result = self.closure(*args, **kwargs)
117
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
118
+ File "/usr/local/lib/python3.12/dist-packages/torch/utils/_contextlib.py", line 124, in decorate_context
119
+ return func(*args, **kwargs)
120
+ ^^^^^^^^^^^^^^^^^^^^^
121
+ File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/loops/optimization/automatic.py", line 131, in closure
122
+ step_output = self._step_fn()
123
+ ^^^^^^^^^^^^^^^
124
+ File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/loops/optimization/automatic.py", line 319, in _training_step
125
+ training_step_output = call._call_strategy_hook(trainer, "training_step", *kwargs.values())
126
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
127
+ File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/call.py", line 329, in _call_strategy_hook
128
+ output = fn(*args, **kwargs)
129
+ ^^^^^^^^^^^^^^^^^^^
130
+ File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/strategies/strategy.py", line 390, in training_step
131
+ return self._forward_redirection(self.model, self.lightning_module, "training_step", *args, **kwargs)
132
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
133
+ File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/strategies/strategy.py", line 641, in __call__
134
+ wrapper_output = wrapper_module(*args, **kwargs)
135
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
136
+ File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 1776, in _wrapped_call_impl
137
+ return self._call_impl(*args, **kwargs)
138
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
139
+ File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 1787, in _call_impl
140
+ return forward_call(*args, **kwargs)
141
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
142
+ File "/usr/local/lib/python3.12/dist-packages/torch/nn/parallel/distributed.py", line 1662, in forward
143
+ inputs, kwargs = self._pre_forward(*inputs, **kwargs)
144
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
145
+ File "/usr/local/lib/python3.12/dist-packages/torch/nn/parallel/distributed.py", line 1551, in _pre_forward
146
+ if torch.is_grad_enabled() and self.reducer._rebuild_buckets():
147
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
148
+ RuntimeError: It looks like your LightningModule has parameters that were not used in producing the loss returned by training_step. If this is intentional, you must enable the detection of unused parameters in DDP, either by setting the string value `strategy='ddp_find_unused_parameters_true'` or by setting the flag in the strategy with `strategy=DDPStrategy(find_unused_parameters=True)`.
149
+ [rank0]: Traceback (most recent call last):
150
+ [rank0]: File "/kaggle/working/train.py", line 8, in <module>
151
+ [rank0]: model.train(
152
+ [rank0]: File "/usr/local/lib/python3.12/dist-packages/rfdetr/detr.py", line 505, in train
153
+ [rank0]: trainer.fit(module, datamodule, ckpt_path=config.resume or None)
154
+ [rank0]: File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/trainer.py", line 584, in fit
155
+ [rank0]: call._call_and_handle_interrupt(
156
+ [rank0]: File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/call.py", line 48, in _call_and_handle_interrupt
157
+ [rank0]: return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs)
158
+ [rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
159
+ [rank0]: File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/strategies/launchers/subprocess_script.py", line 105, in launch
160
+ [rank0]: return function(*args, **kwargs)
161
+ [rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^
162
+ [rank0]: File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/trainer.py", line 630, in _fit_impl
163
+ [rank0]: self._run(model, ckpt_path=ckpt_path, weights_only=weights_only)
164
+ [rank0]: File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/trainer.py", line 1079, in _run
165
+ [rank0]: results = self._run_stage()
166
+ [rank0]: ^^^^^^^^^^^^^^^^^
167
+ [rank0]: File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/trainer.py", line 1123, in _run_stage
168
+ [rank0]: self.fit_loop.run()
169
+ [rank0]: File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/loops/fit_loop.py", line 217, in run
170
+ [rank0]: self.advance()
171
+ [rank0]: File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/loops/fit_loop.py", line 465, in advance
172
+ [rank0]: self.epoch_loop.run(self._data_fetcher)
173
+ [rank0]: File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/loops/training_epoch_loop.py", line 153, in run
174
+ [rank0]: self.advance(data_fetcher)
175
+ [rank0]: File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/loops/training_epoch_loop.py", line 352, in advance
176
+ [rank0]: batch_output = self.automatic_optimization.run(trainer.optimizers[0], batch_idx, kwargs)
177
+ [rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
178
+ [rank0]: File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/loops/optimization/automatic.py", line 185, in run
179
+ [rank0]: closure()
180
+ [rank0]: File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/loops/optimization/automatic.py", line 146, in __call__
181
+ [rank0]: self._result = self.closure(*args, **kwargs)
182
+ [rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
183
+ [rank0]: File "/usr/local/lib/python3.12/dist-packages/torch/utils/_contextlib.py", line 124, in decorate_context
184
+ [rank0]: return func(*args, **kwargs)
185
+ [rank0]: ^^^^^^^^^^^^^^^^^^^^^
186
+ [rank0]: File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/loops/optimization/automatic.py", line 131, in closure
187
+ [rank0]: step_output = self._step_fn()
188
+ [rank0]: ^^^^^^^^^^^^^^^
189
+ [rank0]: File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/loops/optimization/automatic.py", line 319, in _training_step
190
+ [rank0]: training_step_output = call._call_strategy_hook(trainer, "training_step", *kwargs.values())
191
+ [rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
192
+ [rank0]: File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/trainer/call.py", line 329, in _call_strategy_hook
193
+ [rank0]: output = fn(*args, **kwargs)
194
+ [rank0]: ^^^^^^^^^^^^^^^^^^^
195
+ [rank0]: File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/strategies/strategy.py", line 390, in training_step
196
+ [rank0]: return self._forward_redirection(self.model, self.lightning_module, "training_step", *args, **kwargs)
197
+ [rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
198
+ [rank0]: File "/usr/local/lib/python3.12/dist-packages/pytorch_lightning/strategies/strategy.py", line 641, in __call__
199
+ [rank0]: wrapper_output = wrapper_module(*args, **kwargs)
200
+ [rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
201
+ [rank0]: File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 1776, in _wrapped_call_impl
202
+ [rank0]: return self._call_impl(*args, **kwargs)
203
+ [rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
204
+ [rank0]: File "/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py", line 1787, in _call_impl
205
+ [rank0]: return forward_call(*args, **kwargs)
206
+ [rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
207
+ [rank0]: File "/usr/local/lib/python3.12/dist-packages/torch/nn/parallel/distributed.py", line 1662, in forward
208
+ [rank0]: inputs, kwargs = self._pre_forward(*inputs, **kwargs)
209
+ [rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
210
+ [rank0]: File "/usr/local/lib/python3.12/dist-packages/torch/nn/parallel/distributed.py", line 1551, in _pre_forward
211
+ [rank0]: if torch.is_grad_enabled() and self.reducer._rebuild_buckets():
212
+ [rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
213
+ [rank0]: RuntimeError: It looks like your LightningModule has parameters that were not used in producing the loss returned by training_step. If this is intentional, you must enable the detection of unused parameters in DDP, either by setting the string value `strategy='ddp_find_unused_parameters_true'` or by setting the flag in the strategy with `strategy=DDPStrategy(find_unused_parameters=True)`.
wandb/run-20260404_165034-jlvlgl50/files/requirements.txt ADDED
@@ -0,0 +1,959 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ setuptools==75.2.0
2
+ types-setuptools==80.10.0.20260124
3
+ requirements-parser==0.9.0
4
+ pi_heif==1.3.0
5
+ transformers==5.5.0
6
+ idna==3.7
7
+ faster-coco-eval==1.7.2
8
+ pillow-avif-plugin==1.5.5
9
+ opencv-python-headless==4.10.0.84
10
+ hf-xet==1.4.3
11
+ pip==26.0.1
12
+ fsspec==2025.3.0
13
+ supervision==0.27.0.post2
14
+ rfdetr==1.6.3
15
+ huggingface_hub==1.9.0
16
+ pyDeprecate==0.5.0
17
+ google-cloud-bigquery-storage==2.37.0
18
+ roboflow==1.3.1
19
+ pytools==2025.2.5
20
+ pycuda==2026.1
21
+ siphash24==1.8
22
+ protobuf==5.29.5
23
+ torchtune==0.6.1
24
+ learntools==0.3.5
25
+ rouge_score==0.1.2
26
+ pyclipper==1.4.0
27
+ urwid_readline==0.15.1
28
+ h2o==3.46.0.10
29
+ rfc3161-client==1.0.5
30
+ blake3==1.0.8
31
+ mpld3==0.5.12
32
+ qgrid==1.3.1
33
+ ConfigSpace==1.2.2
34
+ woodwork==0.31.0
35
+ ujson==5.12.0
36
+ y-py==0.6.2
37
+ ipywidgets==8.1.5
38
+ scikit-multilearn==0.2.0
39
+ lightning-utilities==0.15.3
40
+ pytesseract==0.3.13
41
+ Cartopy==0.25.0
42
+ odfpy==1.4.1
43
+ Boruta==0.4.3
44
+ docstring-to-markdown==0.17
45
+ torchinfo==1.8.0
46
+ clint==0.5.1
47
+ comm==0.2.3
48
+ Deprecated==1.3.1
49
+ pymongo==4.16.0
50
+ tensorflow-io-gcs-filesystem==0.37.1
51
+ jmespath==1.1.0
52
+ pygltflib==1.16.5
53
+ keras-core==0.1.7
54
+ pandas==2.3.3
55
+ securesystemslib==1.3.1
56
+ ghapi==1.0.11
57
+ qtconsole==5.7.1
58
+ pyemd==2.0.0
59
+ pandas-profiling==3.6.6
60
+ nilearn==0.13.1
61
+ in-toto-attestation==0.9.3
62
+ a2a-sdk==0.3.25
63
+ keras-tuner==1.4.8
64
+ fastuuid==0.14.0
65
+ scikit-surprise==1.1.4
66
+ vtk==9.3.1
67
+ jupyter-ydoc==0.2.5
68
+ aiofiles==22.1.0
69
+ pytokens==0.4.1
70
+ featuretools==1.31.0
71
+ plotly-express==0.4.1
72
+ marshmallow==3.26.2
73
+ easyocr==1.7.2
74
+ ppft==1.7.8
75
+ openslide-bin==4.0.0.13
76
+ fuzzywuzzy==0.18.0
77
+ id==1.6.1
78
+ openslide-python==1.4.3
79
+ kaggle-environments==1.27.3
80
+ pyarrow==23.0.1
81
+ pandasql==0.7.3
82
+ update-checker==0.18.0
83
+ pathos==0.3.2
84
+ jupyter_server_fileid==0.9.3
85
+ fasttext==0.9.3
86
+ coverage==7.13.5
87
+ s3fs==2026.2.0
88
+ stopit==1.1.2
89
+ haversine==2.9.0
90
+ jupyter_server==2.12.5
91
+ geojson==3.2.0
92
+ botocore==1.42.70
93
+ fury==0.12.0
94
+ ipympl==0.10.0
95
+ ipython_pygments_lexers==1.1.1
96
+ olefile==0.47
97
+ jupyter_server_proxy==4.4.0
98
+ datasets==4.8.3
99
+ pytorch-ignite==0.5.3
100
+ xvfbwrapper==0.2.22
101
+ daal==2025.11.0
102
+ open_spiel==1.6.12
103
+ jupyter-lsp==1.5.1
104
+ trx-python==0.4.0
105
+ gpxpy==1.6.2
106
+ papermill==2.7.0
107
+ simpervisor==1.0.0
108
+ kagglehub==1.0.0
109
+ mlcrate==0.2.0
110
+ kaggle==2.0.0
111
+ dask-jobqueue==0.9.0
112
+ model-signing==1.1.1
113
+ jupyterlab==3.6.8
114
+ args==0.1.0
115
+ ImageHash==4.3.2
116
+ typing-inspect==0.9.0
117
+ PyUpSet==0.1.1.post7
118
+ dacite==1.9.2
119
+ pycryptodome==3.23.0
120
+ google-cloud-videointelligence==2.18.0
121
+ visions==0.8.1
122
+ deap==1.4.3
123
+ lml==0.2.0
124
+ jiter==0.10.0
125
+ ypy-websocket==0.8.4
126
+ cytoolz==1.1.0
127
+ path.py==12.5.0
128
+ tensorflow-io==0.37.1
129
+ wavio==0.0.9
130
+ pdf2image==1.17.0
131
+ line_profiler==5.0.2
132
+ aiobotocore==3.3.0
133
+ optuna==4.8.0
134
+ fastgit==0.0.4
135
+ litellm==1.82.4
136
+ pyLDAvis==3.4.1
137
+ Janome==0.5.0
138
+ langid==1.1.6
139
+ sigstore-models==0.0.6
140
+ pokerkit==0.6.3
141
+ pyaml==26.2.1
142
+ scikit-plot==0.3.7
143
+ nbdev==3.0.12
144
+ simpleitk==2.5.3
145
+ ml_collections==1.1.0
146
+ filetype==1.2.0
147
+ Wand==0.7.0
148
+ jupyter_server_ydoc==0.8.0
149
+ pyjson5==2.0.0
150
+ email-validator==2.3.0
151
+ execnb==0.1.18
152
+ colorama==0.4.6
153
+ ruamel.yaml==0.19.1
154
+ python-lsp-server==1.14.0
155
+ black==26.3.1
156
+ PyArabic==0.6.15
157
+ gymnasium==1.2.0
158
+ path==17.1.1
159
+ gensim==4.4.0
160
+ pypdf==6.9.1
161
+ TPOT==1.1.0
162
+ Pympler==1.1
163
+ bayesian-optimization==3.2.1
164
+ nbconvert==6.4.5
165
+ kornia==0.8.2
166
+ pathspec==1.0.4
167
+ pybind11==3.0.2
168
+ sigstore==4.2.0
169
+ funcy==2.0
170
+ func_timeout==4.3.5
171
+ testpath==0.6.0
172
+ aioitertools==0.13.0
173
+ google-cloud-vision==3.12.1
174
+ ray==2.54.0
175
+ kornia_rs==0.1.10
176
+ traitlets==5.14.3
177
+ gymnax==0.0.8
178
+ dnspython==2.8.0
179
+ chex==0.1.90
180
+ gym==0.26.2
181
+ nbclient==0.5.13
182
+ ydata-profiling==4.18.1
183
+ POT==0.9.6.post1
184
+ deepdiff==8.6.2
185
+ squarify==0.4.4
186
+ dataclasses-json==0.6.7
187
+ pettingzoo==1.24.0
188
+ pytorch-lightning==2.6.1
189
+ segment_anything==1.0
190
+ emoji==2.15.0
191
+ python-bidi==0.6.7
192
+ rgf-python==3.12.0
193
+ ninja==1.13.0
194
+ widgetsnbextension==4.0.15
195
+ minify_html==0.18.1
196
+ urwid==3.0.5
197
+ jedi==0.19.2
198
+ jupyterlab-lsp==3.10.2
199
+ python-lsp-jsonrpc==1.1.2
200
+ QtPy==2.4.3
201
+ pydicom==3.0.1
202
+ multimethod==1.12
203
+ torchmetrics==1.9.0
204
+ asttokens==3.0.1
205
+ docker==7.1.0
206
+ dask-expr==2.0.0
207
+ s3transfer==0.16.0
208
+ build==1.4.0
209
+ Shimmy==2.0.0
210
+ igraph==1.0.0
211
+ puremagic==2.1.0
212
+ jupyterlab_server==2.28.0
213
+ isoweek==1.3.3
214
+ texttable==1.7.0
215
+ kt-legacy==1.0.5
216
+ orderly-set==5.5.0
217
+ pyexcel-io==0.6.7
218
+ catboost==1.2.10
219
+ kagglesdk==0.1.16
220
+ mamba==0.11.3
221
+ dipy==1.12.0
222
+ colorlog==6.10.1
223
+ asn1crypto==1.5.1
224
+ pyexcel-ods==0.6.0
225
+ lime==0.2.0.1
226
+ pox==0.3.7
227
+ rfc8785==0.1.4
228
+ sigstore-rekor-types==0.0.18
229
+ cesium==0.12.4
230
+ boto3==1.42.70
231
+ tuf==6.0.0
232
+ hep_ml==0.8.0
233
+ pyproject_hooks==1.2.0
234
+ phik==0.12.5
235
+ pudb==2025.1.5
236
+ mne==1.11.0
237
+ keras-cv==0.9.0
238
+ dill==0.4.1
239
+ gatspy==0.3
240
+ scikit-learn-intelex==2025.11.0
241
+ onnx==1.20.1
242
+ scikit-optimize==0.10.2
243
+ category_encoders==2.9.0
244
+ mypy_extensions==1.1.0
245
+ mistune==0.8.4
246
+ json5==0.13.0
247
+ google-colab==1.0.0
248
+ psutil==5.9.5
249
+ jsonschema==4.26.0
250
+ astunparse==1.6.3
251
+ pycocotools==2.0.11
252
+ lxml==6.0.2
253
+ ipython==7.34.0
254
+ oauthlib==3.3.1
255
+ grpc-google-iam-v1==0.14.3
256
+ array_record==0.8.3
257
+ PuLP==3.3.0
258
+ nvidia-cuda-runtime-cu12==12.8.90
259
+ dask-cuda==26.2.0
260
+ immutabledict==4.3.1
261
+ peewee==4.0.0
262
+ fiona==1.10.1
263
+ aiosignal==1.4.0
264
+ libclang==18.1.1
265
+ annotated-types==0.7.0
266
+ spreg==1.8.5
267
+ grain==0.2.15
268
+ geemap==0.35.3
269
+ patsy==1.0.2
270
+ imagesize==1.4.1
271
+ py-cpuinfo==9.0.0
272
+ pyzmq==26.2.1
273
+ nvidia-cufile-cu12==1.13.1.3
274
+ multidict==6.7.1
275
+ srsly==2.5.2
276
+ intel-openmp==2025.3.2
277
+ uuid_utils==0.14.1
278
+ google-cloud-language==2.19.0
279
+ soxr==1.0.0
280
+ jupyterlab_pygments==0.3.0
281
+ backcall==0.2.0
282
+ tensorflow-hub==0.16.1
283
+ google==3.0.0
284
+ requests-oauthlib==2.0.0
285
+ dopamine_rl==4.1.2
286
+ overrides==7.7.0
287
+ db-dtypes==1.5.0
288
+ jeepney==0.9.0
289
+ langgraph-sdk==0.3.9
290
+ ipython-genutils==0.2.0
291
+ nvidia-cuda-cupti-cu12==12.8.90
292
+ libcugraph-cu12==26.2.0
293
+ catalogue==2.0.10
294
+ beautifulsoup4==4.13.5
295
+ nvidia-ml-py==13.590.48
296
+ sphinxcontrib-devhelp==2.0.0
297
+ partd==1.4.2
298
+ sklearn-pandas==2.2.0
299
+ sphinxcontrib-qthelp==2.0.0
300
+ google-cloud-spanner==3.63.0
301
+ h5py==3.15.1
302
+ python-box==7.4.1
303
+ distributed-ucxx-cu12==0.48.0
304
+ xlrd==2.0.2
305
+ branca==0.8.2
306
+ chardet==5.2.0
307
+ pycairo==1.29.0
308
+ Authlib==1.6.8
309
+ cuda-core==0.3.2
310
+ sentencepiece==0.2.1
311
+ nvidia-cusparselt-cu12==0.7.1
312
+ matplotlib-venn==1.1.2
313
+ scooby==0.11.0
314
+ fqdn==1.5.1
315
+ gin-config==0.5.0
316
+ ipython-sql==0.5.0
317
+ toml==0.10.2
318
+ PyOpenGL==3.1.10
319
+ weasel==0.4.3
320
+ jsonpointer==3.0.0
321
+ google-auth-httplib2==0.3.0
322
+ spint==1.0.7
323
+ nvtx==0.2.14
324
+ websocket-client==1.9.0
325
+ torchao==0.10.0
326
+ splot==1.1.7
327
+ langgraph-checkpoint==4.0.0
328
+ alabaster==1.0.0
329
+ jaxlib==0.7.2
330
+ google-resumable-media==2.8.0
331
+ namex==0.1.0
332
+ quantecon==0.11.0
333
+ nvidia-cuda-cccl-cu12==12.9.27
334
+ google-cloud-aiplatform==1.138.0
335
+ treelite==4.6.1
336
+ google-cloud-resource-manager==1.16.0
337
+ jupyter_core==5.9.1
338
+ spacy-legacy==3.0.12
339
+ librosa==0.11.0
340
+ ibis-framework==9.5.0
341
+ requests-toolbelt==1.0.0
342
+ smart_open==7.5.1
343
+ tensorflow-metadata==1.17.3
344
+ pysal==25.7
345
+ highspy==1.13.1
346
+ click==8.3.1
347
+ markdown-it-py==4.0.0
348
+ nvidia-cusolver-cu12==11.7.3.90
349
+ cupy-cuda12x==14.0.1
350
+ imutils==0.5.4
351
+ grpclib==0.4.9
352
+ opt_einsum==3.4.0
353
+ folium==0.20.0
354
+ moviepy==1.0.3
355
+ opencv-python==4.13.0.92
356
+ en_core_web_sm==3.8.0
357
+ tensorflow-text==2.19.0
358
+ langchain-core==1.2.15
359
+ yarl==1.22.0
360
+ spacy==3.8.11
361
+ importlib_resources==6.5.2
362
+ peft==0.18.1
363
+ lazy_loader==0.4
364
+ polars-runtime-32==1.35.2
365
+ pylibcudf-cu12==26.2.1
366
+ bigquery-magics==0.10.3
367
+ spanner-graph-notebook==1.1.8
368
+ sqlglot==25.20.2
369
+ linkify-it-py==2.0.3
370
+ types-pytz==2025.2.0.20251108
371
+ tifffile==2026.2.20
372
+ tsfresh==0.21.1
373
+ nbclassic==1.3.3
374
+ scikit-image==0.25.2
375
+ tensorflow_decision_forests==1.12.0
376
+ simsimd==6.5.13
377
+ isoduration==20.11.0
378
+ momepy==0.11.0
379
+ pytest==8.4.2
380
+ nvidia-cuda-nvcc-cu12==12.5.82
381
+ cuda-bindings==12.9.4
382
+ torchsummary==1.5.1
383
+ earthengine-api==1.5.24
384
+ webencodings==0.5.1
385
+ optree==0.19.0
386
+ jax-cuda12-pjrt==0.7.2
387
+ langchain==1.2.10
388
+ safehttpx==0.1.7
389
+ holidays==0.91
390
+ google-cloud-firestore==2.23.0
391
+ fastjsonschema==2.21.2
392
+ pymc==5.28.0
393
+ pydantic==2.12.3
394
+ jaraco.context==6.1.0
395
+ pyogrio==0.12.1
396
+ numba-cuda==0.22.2
397
+ fonttools==4.61.1
398
+ httpimport==1.4.1
399
+ rsa==4.9.1
400
+ tomlkit==0.13.3
401
+ entrypoints==0.4
402
+ anyio==4.12.1
403
+ charset-normalizer==3.4.4
404
+ pooch==1.9.0
405
+ libcuml-cu12==26.2.0
406
+ astropy-iers-data==0.2026.2.23.0.48.33
407
+ ipyleaflet==0.20.0
408
+ cryptography==43.0.3
409
+ missingno==0.5.2
410
+ langgraph==1.0.9
411
+ pandas-datareader==0.10.0
412
+ pyviz_comms==3.0.6
413
+ cycler==0.12.1
414
+ tensorboard==2.19.0
415
+ gast==0.7.0
416
+ jax-cuda12-plugin==0.7.2
417
+ platformdirs==4.9.2
418
+ google-genai==1.64.0
419
+ inflect==7.5.0
420
+ httplib2==0.31.2
421
+ h11==0.16.0
422
+ alembic==1.18.4
423
+ multitasking==0.0.12
424
+ rmm-cu12==26.2.0
425
+ cvxpy==1.6.7
426
+ affine==2.4.0
427
+ cuml-cu12==26.2.0
428
+ pyparsing==3.3.2
429
+ cffi==2.0.0
430
+ h5netcdf==1.8.1
431
+ Markdown==3.10.2
432
+ google-cloud-translate==3.24.0
433
+ rpy2==3.5.17
434
+ regex==2025.11.3
435
+ tf_keras==2.19.0
436
+ google-auth==2.47.0
437
+ nvidia-libnvcomp-cu12==5.1.0.21
438
+ Send2Trash==2.1.0
439
+ cymem==2.0.13
440
+ pylibraft-cu12==26.2.0
441
+ shap==0.50.0
442
+ shapely==2.1.2
443
+ psygnal==0.15.1
444
+ uri-template==1.3.0
445
+ parso==0.8.6
446
+ webcolors==25.10.0
447
+ nltk==3.9.1
448
+ atpublic==5.1
449
+ ImageIO==2.37.2
450
+ sphinxcontrib-applehelp==2.0.0
451
+ bigframes==2.35.0
452
+ pydot==4.0.1
453
+ onemkl-license==2025.3.1
454
+ treescope==0.1.10
455
+ tcmlib==1.4.1
456
+ opentelemetry-sdk==1.38.0
457
+ tiktoken==0.12.0
458
+ nibabel==5.3.3
459
+ multiprocess==0.70.16
460
+ typing_extensions==4.15.0
461
+ PyYAML==6.0.3
462
+ defusedxml==0.7.1
463
+ sphinxcontrib-serializinghtml==2.0.0
464
+ bleach==6.3.0
465
+ tenacity==9.1.4
466
+ python-utils==3.9.1
467
+ google-cloud-bigquery==3.40.1
468
+ google-cloud-bigquery-connection==1.20.0
469
+ opentelemetry-resourcedetector-gcp==1.11.0a0
470
+ ormsgpack==1.12.2
471
+ pydotplus==2.0.2
472
+ pycryptodomex==3.23.0
473
+ openai==2.23.0
474
+ matplotlib==3.10.0
475
+ ml_dtypes==0.5.4
476
+ uvloop==0.22.1
477
+ google-pasta==0.2.0
478
+ giddy==2.3.8
479
+ ipyparallel==8.8.0
480
+ keras==3.10.0
481
+ cuvs-cu12==26.2.0
482
+ mcp==1.26.0
483
+ spacy-loggers==1.0.5
484
+ google-cloud-logging==3.13.0
485
+ rfc3987-syntax==1.1.0
486
+ google-ai-generativelanguage==0.6.15
487
+ keras-hub==0.21.1
488
+ pydata-google-auth==1.9.1
489
+ absl-py==1.4.0
490
+ ydf==0.15.0
491
+ narwhals==2.17.0
492
+ nvidia-cusparse-cu12==12.5.8.93
493
+ openpyxl==3.1.5
494
+ nvidia-cublas-cu12==12.8.4.1
495
+ roman-numerals==4.1.0
496
+ vega-datasets==0.9.0
497
+ mpmath==1.3.0
498
+ etils==1.13.0
499
+ sentence-transformers==5.2.3
500
+ osqp==1.1.1
501
+ traittypes==0.2.3
502
+ opentelemetry-exporter-gcp-monitoring==1.11.0a0
503
+ graphviz==0.21
504
+ google-cloud-trace==1.18.0
505
+ einops==0.8.2
506
+ torchdata==0.11.0
507
+ jax==0.7.2
508
+ cachetools==6.2.6
509
+ aiohappyeyeballs==2.6.1
510
+ annotated-doc==0.0.4
511
+ starlette==0.52.1
512
+ fastapi==0.133.0
513
+ typer==0.24.1
514
+ duckdb==1.3.2
515
+ blinker==1.9.0
516
+ referencing==0.37.0
517
+ googledrivedownloader==1.1.0
518
+ GDAL==3.8.4
519
+ cuda-python==12.9.4
520
+ pycparser==3.0
521
+ et_xmlfile==2.0.0
522
+ jieba==0.42.1
523
+ zict==3.0.0
524
+ hyperopt==0.2.7
525
+ python-louvain==0.16
526
+ SQLAlchemy==2.0.47
527
+ cuda-toolkit==12.8.1
528
+ PyDrive2==1.21.3
529
+ roman-numerals-py==4.1.0
530
+ urllib3==2.5.0
531
+ jaraco.functools==4.4.0
532
+ optax==0.2.7
533
+ pyOpenSSL==24.2.1
534
+ jupyter-console==6.6.3
535
+ libkvikio-cu12==26.2.0
536
+ gspread==6.2.1
537
+ docstring_parser==0.17.0
538
+ albumentations==2.0.8
539
+ jupytext==1.19.1
540
+ seaborn==0.13.2
541
+ librmm-cu12==26.2.0
542
+ cons==0.4.7
543
+ scipy==1.16.3
544
+ matplotlib-inline==0.2.1
545
+ pynndescent==0.6.0
546
+ stringzilla==4.6.0
547
+ flatbuffers==25.12.19
548
+ omegaconf==2.3.0
549
+ umap-learn==0.5.11
550
+ progressbar2==4.5.0
551
+ pexpect==4.9.0
552
+ torchcodec==0.10.0+cu128
553
+ ptyprocess==0.7.0
554
+ pygame==2.6.1
555
+ kiwisolver==1.4.9
556
+ Cython==3.0.12
557
+ shellingham==1.5.4
558
+ soupsieve==2.8.3
559
+ snowballstemmer==3.0.1
560
+ propcache==0.4.1
561
+ ucxx-cu12==0.48.0
562
+ nbformat==5.10.4
563
+ python-snappy==0.7.3
564
+ rasterstats==0.20.0
565
+ bqplot==0.12.45
566
+ nest-asyncio==1.6.0
567
+ notebook==6.5.7
568
+ flax==0.11.2
569
+ google-cloud-functions==1.22.0
570
+ multipledispatch==1.0.0
571
+ googleapis-common-protos==1.72.0
572
+ xgboost==3.2.0
573
+ eerepr==0.1.2
574
+ torchaudio==2.10.0+cu128
575
+ locket==1.0.0
576
+ prettytable==3.17.0
577
+ pygit2==1.19.1
578
+ plotly==5.24.1
579
+ fastai==2.8.7
580
+ msgpack==1.1.2
581
+ clarabel==0.11.1
582
+ cligj==0.7.2
583
+ google-cloud-secret-manager==2.26.0
584
+ spglm==1.1.0
585
+ ipytree==0.2.2
586
+ termcolor==3.3.0
587
+ tweepy==4.16.0
588
+ google-cloud-core==2.5.0
589
+ dataproc-spark-connect==1.0.2
590
+ mkl==2025.3.1
591
+ umf==1.0.3
592
+ textblob==0.19.0
593
+ firebase-admin==6.9.0
594
+ simple-parsing==0.1.8
595
+ debugpy==1.8.15
596
+ google-cloud-discoveryengine==0.13.12
597
+ fastcore==1.12.16
598
+ decorator==4.4.2
599
+ pickleshare==0.7.5
600
+ rasterio==1.5.0
601
+ networkx==3.6.1
602
+ typer-slim==0.24.0
603
+ wasabi==1.1.3
604
+ mgwr==2.2.1
605
+ hdbscan==0.8.41
606
+ pydub==0.25.1
607
+ tobler==0.13.0
608
+ more-itertools==10.8.0
609
+ keyrings.google-artifactregistry-auth==1.1.2
610
+ cloudpickle==3.1.2
611
+ nvidia-nvtx-cu12==12.8.90
612
+ fastlite==0.2.4
613
+ colorcet==3.1.0
614
+ lark==1.3.1
615
+ antlr4-python3-runtime==4.9.3
616
+ keras-nlp==0.21.1
617
+ music21==9.9.1
618
+ Pygments==2.19.2
619
+ triton==3.6.0
620
+ toolz==0.12.1
621
+ python-slugify==8.0.4
622
+ sqlparse==0.5.5
623
+ jupyter-leaflet==0.20.0
624
+ gym-notices==0.1.0
625
+ torchvision==0.25.0+cu128
626
+ prophet==1.3.0
627
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628
+ semantic-version==2.10.0
629
+ fastprogress==1.1.5
630
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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
+ [2026-04-04 16:53:31] [INFO] rf-detr - Building Roboflow train dataset with square resize at resolution 384
3
+ [2026-04-04 16:53:31] [INFO] rf-detr - Using multi-scale training with square resize and scales: [544]
4
+ [2026-04-04 16:53:31] [INFO] rf-detr - Built 1 Albumentations transforms from config
5
+ [2026-04-04 16:53:31] [INFO] rf-detr - Built 1 Albumentations transforms from config
6
+ loading annotations into memory...
7
+ Done (t=1.00s)
8
+ creating index...
9
+ index created!
10
+ [2026-04-04 16:53:33] [INFO] rf-detr - Building Roboflow val dataset with square resize at resolution 384
11
+ [2026-04-04 16:53:33] [INFO] rf-detr - Using multi-scale training with square resize and scales: [544]
12
+ [2026-04-04 16:53:33] [INFO] rf-detr - Built 1 Albumentations transforms from config
13
+ loading annotations into memory...
14
+ Done (t=0.27s)
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 [00:01<00:00, 1.54it/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
+ [2026-04-04 16:53:37] [INFO] rf-detr - Best EMA mAP improved to 0.0752 (epoch 0)
81
+ Epoch 0: 100%|█| 2331/2331 [25:15<00:00, 1.54it/s, v_num=ikbe, 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, 2.87it/s]
85
+ ┃ mAP ┃ mAR ┃ F1 sweep ┃
86
+ ┡━━━━━━━━┯━━━━━━━━┯━━━━━━━━╇━━━━━━━━╇━━━━━━━━┯━━━━━━━━┯━━━━━━━━┩
87
+ │ 50:95 │ 50 │ 75 │ @500 │ F1 │ Prec │ Recall │
88
+ ├────────┼────────┼────────┼────────┼────────┼────────┼────────┤
89
+ │ 0.4548 │ 0.5855 │ 0.5061 │ 0.7752 │ 0.5477 │ 0.5473 │ 0.5829 │
90
+ └────────┴────────┴────────┴────────┴────────┴────────┴────────┘
91
+  Val — Per-class Metrics 
92
+ ┏━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━┓
93
+ ┃ Class  ┃ AP 50:95 ┃  AR ┃  F1 ┃ Precision ┃ Recall ┃
94
+ ┡━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━┩
95
+ │ Hatchback  │ 0.4531 │ 0.7926 │ 0.5580 │ 0.4224 │ 0.8220 │
96
+ │ Sedan  │ 0.4841 │ 0.8181 │ 0.5439 │ 0.5480 │ 0.5400 │
97
+ │ SUV  │ 0.4132 │ 0.8158 │ 0.4968 │ 0.4736 │ 0.5224 │
98
+ │ MUV  │ 0.3523 │ 0.8333 │ 0.4059 │ 0.4511 │ 0.3689 │
99
+ │ Bus  │ 0.6101 │ 0.7661 │ 0.7314 │ 0.7078 │ 0.7566 │
100
+ │ Truck  │ 0.5194 │ 0.7862 │ 0.5976 │ 0.5196 │ 0.7032 │
101
+ │ Three-wheeler  │ 0.6285 │ 0.7169 │ 0.8122 │ 0.9230 │ 0.7251 │
102
+ │ Two-wheeler  │ 0.5422 │ 0.6519 │ 0.7715 │ 0.7456 │ 0.7993 │
103
+ │ LCV  │ 0.5225 │ 0.7683 │ 0.6384 │ 0.5784 │ 0.7124 │
104
+ │ Mini-bus  │ 0.1676 │ 0.7565 │ 0.1353 │ 0.3784 │ 0.0824 │
105
+ │ Tempo-traveller │ 0.5864 │ 0.8469 │ 0.5891 │ 0.5116 │ 0.6943 │
106
+ │ Bicycle  │ 0.3985 │ 0.7034 │ 0.5135 │ 0.4667 │ 0.5708 │
107
+ │ Van  │ 0.2340 │ 0.8211 │ 0.3264 │ 0.3894 │ 0.2809 │
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%|█| 2331/2331 [33:43<00:00, 1.15it/s, v_num=ikbe, train/lr=0.0001,[2026-04-04 17:27:21] [INFO] rf-detr - Best regular mAP saved to /kaggle/working/output/checkpoint_best_regular.pth (epoch 0)
113
+ [2026-04-04 17:27:21] [INFO] rf-detr - Best EMA mAP improved to 0.4750 (epoch 0)
114
+ [rank: 0] Metric __rfdetr_effective_map__ improved. New best score: 0.475
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%|█| 2331/2331 [24:57<00:00, 1.56it/s, v_num=ikbe, train/lr=1e-5, t Val — Overall Metrics
130
+ ┏━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━┓0:00, 2.93it/s]
131
+ ┃ mAP ┃ mAR ┃ F1 sweep ┃
132
+ ┡━━━━━━━━┯━━━━━━━━┯━━━━━━━━╇━━━━━━━━╇━━━━━━━━┯━━━━━━━━┯━━━━━━━━┩
133
+ │ 50:95 │ 50 │ 75 │ @500 │ F1 │ Prec │ Recall │
134
+ ├────────┼────────┼────────┼────────┼────────┼────────┼────────┤
135
+ │ 0.4906 │ 0.6132 │ 0.5418 │ 0.7912 │ 0.5887 │ 0.5871 │ 0.6037 │
136
+ └────────┴────────┴────────┴────────┴────────┴────────┴────────┘
137
+  Val — Per-class Metrics 
138
+ ┏━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━┓
139
+ ┃ Class  ┃ AP 50:95 ┃  AR ┃  F1 ┃ Precision ┃ Recall ┃
140
+ ┡━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━┩
141
+ │ Hatchback  │ 0.4932 │ 0.8084 │ 0.5872 │ 0.4917 │ 0.7288 │
142
+ │ Sedan  │ 0.5264 │ 0.8389 │ 0.5676 │ 0.5287 │ 0.6127 │
143
+ │ SUV  │ 0.4438 │ 0.8302 │ 0.5157 │ 0.4870 │ 0.5480 │
144
+ │ MUV  │ 0.4009 │ 0.8497 │ 0.4519 │ 0.4056 │ 0.5101 │
145
+ │ Bus  │ 0.6428 │ 0.7759 │ 0.7503 │ 0.7415 │ 0.7593 │
146
+ │ Truck  │ 0.5424 │ 0.7937 │ 0.6339 │ 0.6059 │ 0.6646 │
147
+ │ Three-wheeler  │ 0.6672 │ 0.7503 │ 0.8237 │ 0.8326 │ 0.8150 │
148
+ │ Two-wheeler  │ 0.5783 │ 0.6819 │ 0.7938 │ 0.8031 │ 0.7847 │
149
+ │ LCV  │ 0.5551 │ 0.7810 │ 0.6719 │ 0.6445 │ 0.7017 │
150
+ │ Mini-bus  │ 0.1920 │ 0.7782 │ 0.2786 │ 0.3545 │ 0.2294 │
151
+ │ Tempo-traveller │ 0.6226 │ 0.8506 │ 0.6515 │ 0.6115 │ 0.6971 │
152
+ │ Bicycle  │ 0.4359 │ 0.7047 │ 0.5859 │ 0.6560 │ 0.5293 │
153
+ │ Van  │ 0.2770 │ 0.8420 │ 0.3410 │ 0.4704 │ 0.2674 │
154
+ └─────────────────┴──────────┴────────┴────────┴───────────┴────────┘
155
+ Epoch 1: 100%|█| 2331/2331 [33:17<00:00, 1.17it/s, v_num=ikbe, train/lr=1e-5, t[2026-04-04 18:00:41] [INFO] rf-detr - Best regular mAP saved to /kaggle/working/output/checkpoint_best_regular.pth (epoch 1)
156
+ [2026-04-04 18:00:42] [INFO] rf-detr - Best EMA mAP improved to 0.4892 (epoch 1)
157
+ [rank: 0] Metric __rfdetr_effective_map__ improved by 0.016 >= min_delta = 0.001. New best score: 0.491
158
+ Epoch 2: 100%|█| 2331/2331 [25:23<00:00, 1.53it/s, v_num=ikbe, train/lr=1e-5, t Val — Overall Metrics
159
+ ┏━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━┓0:00, 2.84it/s]
160
+ ┃ mAP ┃ mAR ┃ F1 sweep ┃
161
+ ┡━━━━━━━━┯━━━━━━━━┯━━━━━━━━╇━━━━━━━━╇━━━━━━━━┯━━━━━━━━┯━━━━━━━━┩
162
+ │ 50:95 │ 50 │ 75 │ @500 │ F1 │ Prec │ Recall │
163
+ ├────────┼────────┼────────┼────────┼────────┼────────┼────────┤
164
+ │ 0.4977 │ 0.6209 │ 0.5491 │ 0.7956 │ 0.5939 │ 0.5896 │ 0.6111 │
165
+ └────────┴────────┴────────┴────────┴────────┴────────┴────────┘
166
+  Val — Per-class Metrics 
167
+ ┏━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━┓
168
+ ┃ Class  ┃ AP 50:95 ┃  AR ┃  F1 ┃ Precision ┃ Recall ┃
169
+ ┡━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━┩
170
+ │ Hatchback  │ 0.5009 │ 0.8116 │ 0.5948 │ 0.5047 │ 0.7239 │
171
+ │ Sedan  │ 0.5349 │ 0.8410 │ 0.5681 │ 0.4932 │ 0.6698 │
172
+ │ SUV  │ 0.4506 │ 0.8298 │ 0.5213 │ 0.4836 │ 0.5653 │
173
+ │ MUV  │ 0.4063 │ 0.8516 │ 0.4589 │ 0.4249 │ 0.4989 │
174
+ │ Bus  │ 0.6500 │ 0.7812 │ 0.7568 │ 0.7621 │ 0.7516 │
175
+ │ Truck  │ 0.5491 │ 0.7957 │ 0.6474 │ 0.6185 │ 0.6791 │
176
+ │ Three-wheeler  │ 0.6707 │ 0.7513 │ 0.8319 │ 0.8718 │ 0.7954 │
177
+ │ Two-wheeler  │ 0.5842 │ 0.6870 │ 0.7981 │ 0.8101 │ 0.7864 │
178
+ │ LCV  │ 0.5601 │ 0.7829 │ 0.6727 │ 0.6320 │ 0.7190 │
179
+ │ Mini-bus  │ 0.1943 │ 0.7771 │ 0.2676 │ 0.3333 │ 0.2235 │
180
+ │ Tempo-traveller │ 0.6274 │ 0.8577 │ 0.6585 │ 0.6263 │ 0.6943 │
181
+ │ Bicycle  │ 0.4403 │ 0.7252 │ 0.5786 │ 0.6203 │ 0.5422 │
182
+ │ Van  │ 0.3017 │ 0.8501 │ 0.3659 │ 0.4834 │ 0.2944 │
183
+ └─────────────────┴──────────┴────────┴────────┴───────────┴────────┘
184
+ Epoch 2: 100%|█| 2331/2331 [33:57<00:00, 1.14it/s, v_num=ikbe, train/lr=1e-5, t[2026-04-04 18:34:42] [INFO] rf-detr - Best regular mAP saved to /kaggle/working/output/checkpoint_best_regular.pth (epoch 2)
185
+ [2026-04-04 18:34:42] [INFO] rf-detr - Best EMA mAP improved to 0.4982 (epoch 2)
186
+ [rank: 0] Metric __rfdetr_effective_map__ improved by 0.008 >= min_delta = 0.001. New best score: 0.498
187
+ Epoch 3: 100%|█| 2331/2331 [25:14<00:00, 1.54it/s, v_num=ikbe, train/lr=1e-5, t Val — Overall Metrics
188
+ ┏━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━┓0:00, 2.89it/s]
189
+ ┃ mAP ┃ mAR ┃ F1 sweep ┃
190
+ ┡━━━━━━━━┯━━━━━━━━┯━━━━━━━━╇━━━��━━━━╇━━━━━━━━┯━━━━━━━━┯━━━━━━━━┩
191
+ │ 50:95 │ 50 │ 75 │ @500 │ F1 │ Prec │ Recall │
192
+ ├────────┼────────┼────────┼────────┼────────┼────────┼────────┤
193
+ │ 0.5040 │ 0.6272 │ 0.5567 │ 0.7974 │ 0.6008 │ 0.6124 │ 0.5995 │
194
+ └────────┴────────┴────────┴────────┴────────┴────────┴────────┘
195
+  Val — Per-class Metrics 
196
+ ┏━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━┓
197
+ ┃ Class  ┃ AP 50:95 ┃  AR ┃  F1 ┃ Precision ┃ Recall ┃
198
+ ┡━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━┩
199
+ │ Hatchback  │ 0.5091 │ 0.8132 │ 0.6026 │ 0.5210 │ 0.7144 │
200
+ │ Sedan  │ 0.5425 │ 0.8425 │ 0.5831 │ 0.5785 │ 0.5877 │
201
+ │ SUV  │ 0.4550 │ 0.8310 │ 0.5203 │ 0.4676 │ 0.5864 │
202
+ │ MUV  │ 0.4168 │ 0.8531 │ 0.4597 │ 0.4797 │ 0.4414 │
203
+ │ Bus  │ 0.6509 │ 0.7800 │ 0.7574 │ 0.7761 │ 0.7396 │
204
+ │ Truck  │ 0.5581 │ 0.8003 │ 0.6472 │ 0.6104 │ 0.6887 │
205
+ │ Three-wheeler  │ 0.6758 │ 0.7569 │ 0.8340 │ 0.8599 │ 0.8097 │
206
+ │ Two-wheeler  │ 0.5880 │ 0.6895 │ 0.7994 │ 0.8105 │ 0.7886 │
207
+ │ LCV  │ 0.5672 │ 0.7849 │ 0.6857 │ 0.6902 │ 0.6812 │
208
+ │ Mini-bus  │ 0.2025 │ 0.7835 │ 0.2724 │ 0.3486 │ 0.2235 │
209
+ │ Tempo-traveller │ 0.6322 │ 0.8577 │ 0.6648 │ 0.6452 │ 0.6857 │
210
+ │ Bicycle  │ 0.4498 │ 0.7206 │ 0.5952 │ 0.7023 │ 0.5165 │
211
+ │ Van  │ 0.3045 │ 0.8526 │ 0.3884 │ 0.4712 │ 0.3303 │
212
+ └─────────────────┴──────────┴────────┴────────┴───────────┴────────┘
213
+ Epoch 3: 100%|█| 2331/2331 [33:42<00:00, 1.15it/s, v_num=ikbe, train/lr=1e-5, t[2026-04-04 19:08:27] [INFO] rf-detr - Best regular mAP saved to /kaggle/working/output/checkpoint_best_regular.pth (epoch 3)
214
+ [2026-04-04 19:08:27] [INFO] rf-detr - Best EMA mAP improved to 0.5046 (epoch 3)
215
+ [rank: 0] Metric __rfdetr_effective_map__ improved by 0.006 >= min_delta = 0.001. New best score: 0.505
216
+ Epoch 4: 100%|█| 2331/2331 [25:05<00:00, 1.55it/s, v_num=ikbe, train/lr=1e-5, t Val — Overall Metrics
217
+ ┏━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━┓0:00, 2.95it/s]
218
+ ┃ mAP ┃ mAR ┃ F1 sweep ┃
219
+ ┡━━━━━━━━┯━━━━━━━━┯━━━━━━━━╇━━━━━━━━╇━━━━━━━━┯━━━━━━━━┯━━━━━━━━┩
220
+ │ 50:95 │ 50 │ 75 │ @500 │ F1 │ Prec │ Recall │
221
+ ├────────┼────────┼────────┼────────┼────────┼────────┼────────┤
222
+ │ 0.5093 │ 0.6333 │ 0.5629 │ 0.7988 │ 0.6069 │ 0.6216 │ 0.5994 │
223
+ └────────┴────────┴────────┴────────┴────────┴────────┴────────┘
224
+  Val — Per-class Metrics 
225
+ ┏━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━���━━━━━━━━┓
226
+ ┃ Class  ┃ AP 50:95 ┃  AR ┃  F1 ┃ Precision ┃ Recall ┃
227
+ ┡━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━┩
228
+ │ Hatchback  │ 0.5136 │ 0.8160 │ 0.6029 │ 0.5193 │ 0.7187 │
229
+ │ Sedan  │ 0.5418 │ 0.8450 │ 0.5789 │ 0.5566 │ 0.6030 │
230
+ │ SUV  │ 0.4620 │ 0.8335 │ 0.5307 │ 0.5273 │ 0.5341 │
231
+ │ MUV  │ 0.4287 │ 0.8557 │ 0.4710 │ 0.4884 │ 0.4548 │
232
+ │ Bus  │ 0.6552 │ 0.7836 │ 0.7594 │ 0.7725 │ 0.7467 │
233
+ │ Truck  │ 0.5610 │ 0.8000 │ 0.6601 │ 0.6549 │ 0.6654 │
234
+ │ Three-wheeler  │ 0.6775 │ 0.7578 │ 0.8364 │ 0.8611 │ 0.8130 │
235
+ │ Two-wheeler  │ 0.5904 │ 0.6923 │ 0.8043 │ 0.8318 │ 0.7785 │
236
+ │ LCV  │ 0.5717 │ 0.7888 │ 0.6864 │ 0.6798 │ 0.6931 │
237
+ │ Mini-bus  │ 0.2126 │ 0.7818 │ 0.2857 │ 0.3282 │ 0.2529 │
238
+ │ Tempo-traveller │ 0.6381 │ 0.8509 │ 0.6804 │ 0.6988 │ 0.6629 │
239
+ │ Bicycle  │ 0.4526 │ 0.7262 │ 0.5918 │ 0.6734 │ 0.5279 │
240
+ │ Van  │ 0.3151 │ 0.8524 │ 0.4021 │ 0.4887 │ 0.3416 │
241
+ └─────────────────┴──────────┴────────┴────────┴───────────┴────────┘
242
+ Epoch 4: 100%|█| 2331/2331 [33:26<00:00, 1.16it/s, v_num=ikbe, train/lr=1e-5, t[2026-04-04 19:41:56] [INFO] rf-detr - Best regular mAP saved to /kaggle/working/output/checkpoint_best_regular.pth (epoch 4)
243
+ [2026-04-04 19:41:56] [INFO] rf-detr - Best EMA mAP improved to 0.5096 (epoch 4)
244
+ [rank: 0] Metric __rfdetr_effective_map__ improved by 0.005 >= min_delta = 0.001. New best score: 0.510
245
+ Epoch 5: 100%|█| 2331/2331 [25:09<00:00, 1.54it/s, v_num=ikbe, train/lr=1e-5, t Val — Overall Metrics
246
+ ┏━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━┓0:00, 2.94it/s]
247
+ ┃ mAP ┃ mAR ┃ F1 sweep ┃
248
+ ┡━━━━━━━━┯━━━━━━━━┯━━━━━━━━╇━━━━━━━━╇━━━━━━━━┯━━━━━━━━┯━━━━━━━━┩
249
+ │ 50:95 │ 50 │ 75 │ @500 │ F1 │ Prec │ Recall │
250
+ ├────────┼────────┼────────┼────────┼────────┼────────┼────────┤
251
+ │ 0.5130 │ 0.6371 │ 0.5663 │ 0.7991 │ 0.6094 │ 0.6245 │ 0.6028 │
252
+ └────────┴────────┴────────┴────────┴────────┴────────┴────────┘
253
+  Val — Per-class Metrics 
254
+ ┏━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━┓
255
+ ┃ Class  ┃ AP 50:95 ┃  AR ┃  F1 ┃ Precision ┃ Recall ┃
256
+ ┡━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━┩
257
+ │ Hatchback  │ 0.5182 │ 0.8155 │ 0.6059 │ 0.5703 │ 0.6462 │
258
+ │ Sedan  │ 0.5503 │ 0.8445 │ 0.5860 │ 0.5666 │ 0.6067 │
259
+ │ SUV  │ 0.4655 │ 0.8337 │ 0.5307 │ 0.4799 │ 0.5935 │
260
+ │ MUV  │ 0.4363 │ 0.8571 │ 0.4826 │ 0.4567 │ 0.5116 │
261
+ │ Bus  │ 0.6568 │ 0.7853 │ 0.7564 │ 0.7503 │ 0.7626 │
262
+ │ Truck  │ 0.5620 │ 0.8008 │ 0.6693 │ 0.6940 │ 0.6463 │
263
+ │ Three-wheeler  │ 0.6803 │ 0.7594 │ 0.8375 │ 0.8652 │ 0.8115 │
264
+ │ Two-wheeler  │ 0.5914 │ 0.6927 │ 0.8041 │ 0.8181 │ 0.7904 │
265
+ │ LCV  │ 0.5730 │ 0.7889 │ 0.6828 │ 0.6591 │ 0.7083 │
266
+ │ Mini-bus  │ 0.2115 │ 0.7818 │ 0.2848 │ 0.3258 │ 0.2529 │
267
+ │ Tempo-traveller │ 0.6379 │ 0.8529 │ 0.6844 │ 0.7073 │ 0.6629 │
268
+ │ Bicycle  │ 0.4570 │ 0.7205 │ 0.6083 │ 0.7202 │ 0.5265 │
269
+ │ Van  │ 0.3293 │ 0.8551 │ 0.3895 │ 0.5054 │ 0.3169 │
270
+ └─────────────────┴──────────┴────────┴────────┴───────────┴────────┘
271
+ Epoch 5: 100%|█| 2331/2331 [33:29<00:00, 1.16it/s, v_num=ikbe, train/lr=1e-5, t[2026-04-04 20:15:28] [INFO] rf-detr - Best regular mAP saved to /kaggle/working/output/checkpoint_best_regular.pth (epoch 5)
272
+ [2026-04-04 20:15:28] [INFO] rf-detr - Best EMA mAP improved to 0.5139 (epoch 5)
273
+ [rank: 0] Metric __rfdetr_effective_map__ improved by 0.004 >= min_delta = 0.001. New best score: 0.514
274
+ Epoch 6: 100%|█| 2331/2331 [24:59<00:00, 1.55it/s, v_num=ikbe, train/lr=1e-5, t Val — Overall Metrics
275
+ ┏━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━┓0:00, 2.92it/s]
276
+ ┃ mAP ┃ mAR ┃ F1 sweep ┃
277
+ ┡━━━━━━━━┯━━━━━━━━┯━━━━━━━━╇━━━━━━━━╇━━━━━━━━┯━━━━━━━━┯━━━━━━━━┩
278
+ │ 50:95 │ 50 │ 75 │ @500 │ F1 │ Prec │ Recall │
279
+ ├────────┼────────┼────────┼────────┼────────┼────────┼────────┤
280
+ │ 0.5173 │ 0.6408 │ 0.5720 │ 0.8019 │ 0.6107 │ 0.6264 │ 0.6049 │
281
+ └────────┴────────┴────────┴────────┴────────┴────────┴────────┘
282
+  Val — Per-class Metrics 
283
+ ┏━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━┓
284
+ ┃ Class  ┃ AP 50:95 ┃  AR ┃  F1 ┃ Precision ┃ Recall ┃
285
+ ┡━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━┩
286
+ │ Hatchback  │ 0.5214 │ 0.8176 │ 0.6100 │ 0.5578 │ 0.6731 │
287
+ │ Sedan  │ 0.5530 │ 0.8458 │ 0.5904 │ 0.5683 │ 0.6142 │
288
+ │ SUV  │ 0.4665 │ 0.8352 │ 0.5326 │ 0.4990 │ 0.5709 │
289
+ │ MUV  │ 0.4411 │ 0.8562 │ 0.4841 │ 0.4632 │ 0.5071 │
290
+ │ Bus  │ 0.6597 │ 0.7864 │ 0.7689 │ 0.7981 │ 0.7418 │
291
+ │ Truck  │ 0.5691 │ 0.8047 │ 0.6692 │ 0.6731 │ 0.6654 │
292
+ │ Three-wheeler  │ 0.6832 │ 0.7617 │ 0.8382 │ 0.8598 │ 0.8177 │
293
+ │ Two-wheeler  │ 0.5939 │ 0.6936 │ 0.8077 │ 0.8318 │ 0.7848 │
294
+ │ LCV  │ 0.5797 │ 0.7939 │ 0.6886 │ 0.6677 │ 0.7109 │
295
+ │ Mini-bus  │ 0.2102 │ 0.7906 │ 0.2692 │ 0.3889 │ 0.2059 │
296
+ │ Tempo-traveller │ 0.6469 │ 0.8631 │ 0.6751 │ 0.6676 │ 0.6829 │
297
+ │ Bicycle  │ 0.4578 │ 0.7185 │ 0.5957 │ 0.6466 │ 0.5522 │
298
+ │ Van  │ 0.3419 │ 0.8566 │ 0.4093 │ 0.5208 │ 0.3371 │
299
+ └─────────────────┴──────────┴────────┴───��────┴───────────┴────────┘
300
+ Epoch 6: 100%|█| 2331/2331 [33:23<00:00, 1.16it/s, v_num=ikbe, train/lr=1e-5, t[2026-04-04 20:48:54] [INFO] rf-detr - Best regular mAP saved to /kaggle/working/output/checkpoint_best_regular.pth (epoch 6)
301
+ [2026-04-04 20:48:54] [INFO] rf-detr - Best EMA mAP improved to 0.5184 (epoch 6)
302
+ [rank: 0] Metric __rfdetr_effective_map__ improved by 0.005 >= min_delta = 0.001. New best score: 0.518
303
+ Epoch 7: 100%|█| 2331/2331 [24:56<00:00, 1.56it/s, v_num=ikbe, train/lr=1e-5, t Val — Overall Metrics
304
+ ┏━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━┓0:00, 2.94it/s]
305
+ ┃ mAP ┃ mAR ┃ F1 sweep ┃
306
+ ┡━━━━━━━━┯━━━━━━━━┯━━━━━━━━╇━━━━━━━━╇━━━━━━━━┯━━━━━━━━┯━━━━━━━━┩
307
+ │ 50:95 │ 50 │ 75 │ @500 │ F1 │ Prec │ Recall │
308
+ ├────────┼────────┼────────┼────────┼────────┼────────┼────────┤
309
+ │ 0.5197 │ 0.6426 │ 0.5738 │ 0.8019 │ 0.6159 │ 0.6210 │ 0.6170 │
310
+ └────────┴────────┴────────┴────────┴────────┴────────┴────────┘
311
+  Val — Per-class Metrics 
312
+ ┏━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━┓
313
+ ┃ Class  ┃ AP 50:95 ┃  AR ┃  F1 ┃ Precision ┃ Recall ┃
314
+ ┡━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━┩
315
+ │ Hatchback  │ 0.5266 │ 0.8173 │ 0.6119 │ 0.5335 │ 0.7174 │
316
+ │ Sedan  │ 0.5570 │ 0.8474 │ 0.5912 │ 0.5672 │ 0.6174 │
317
+ │ SUV  │ 0.4728 │ 0.8346 │ 0.5404 │ 0.5363 │ 0.5446 │
318
+ │ MUV  │ 0.4444 │ 0.8544 │ 0.4867 │ 0.4887 │ 0.4847 │
319
+ │ Bus  │ 0.6612 │ 0.7906 │ 0.7693 │ 0.7982 │ 0.7423 │
320
+ │ Truck  │ 0.5718 │ 0.8064 │ 0.6652 │ 0.6436 │ 0.6883 │
321
+ │ Three-wheeler  │ 0.6857 │ 0.7628 │ 0.8364 │ 0.8490 │ 0.8242 │
322
+ │ Two-wheeler  │ 0.5959 │ 0.6958 │ 0.8033 │ 0.8009 │ 0.8056 │
323
+ │ LCV  │ 0.5823 │ 0.7929 │ 0.6859 │ 0.6501 │ 0.7258 │
324
+ │ Mini-bus  │ 0.2034 │ 0.7841 │ 0.2911 │ 0.3151 │ 0.2706 │
325
+ │ Tempo-traveller │ 0.6483 │ 0.8609 │ 0.6802 │ 0.6891 │ 0.6714 │
326
+ │ Bicycle  │ 0.4584 │ 0.7199 │ 0.6118 │ 0.7302 │ 0.5265 │
327
+ │ Van  │ 0.3480 │ 0.8573 │ 0.4339 │ 0.4711 │ 0.4022 │
328
+ └─────────────────┴──────────┴────────┴────────┴───────────┴────────┘
329
+ Epoch 7: 100%|█| 2331/2331 [33:16<00:00, 1.17it/s, v_num=ikbe, train/lr=1e-5, t[2026-04-04 21:22:13] [INFO] rf-detr - Best regular mAP saved to /kaggle/working/output/checkpoint_best_regular.pth (epoch 7)
330
+ [2026-04-04 21:22:13] [INFO] rf-detr - Best EMA mAP improved to 0.5211 (epoch 7)
331
+ [rank: 0] Metric __rfdetr_effective_map__ improved by 0.003 >= min_delta = 0.001. New best score: 0.521
332
+ Epoch 8: 100%|█| 2331/2331 [25:13<00:00, 1.54it/s, v_num=ikbe, train/lr=1e-5, t Val — Overall Metrics
333
+ ┏━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━┓0:00, 2.91it/s]
334
+ ┃ mAP ┃ mAR ┃ F1 sweep ┃
335
+ ┡━━━━━━━━┯━━━━━━━━┯━━━━━━━━╇━━━━━━━━╇━━━━━━━━┯━━���━━━━━┯━━━━━━━━┩
336
+ │ 50:95 │ 50 │ 75 │ @500 │ F1 │ Prec │ Recall │
337
+ ├────────┼────────┼────────┼────────┼────────┼────────┼────────┤
338
+ │ 0.5227 │ 0.6462 │ 0.5782 │ 0.8044 │ 0.6172 │ 0.6202 │ 0.6201 │
339
+ └────────┴────────┴────────┴────────┴────────┴────────┴────────┘
340
+  Val — Per-class Metrics 
341
+ ┏━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━┓
342
+ ┃ Class  ┃ AP 50:95 ┃  AR ┃  F1 ┃ Precision ┃ Recall ┃
343
+ ┡━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━┩
344
+ │ Hatchback  │ 0.5295 │ 0.8193 │ 0.6166 │ 0.5414 │ 0.7160 │
345
+ │ Sedan  │ 0.5590 │ 0.8464 │ 0.5904 │ 0.5568 │ 0.6283 │
346
+ │ SUV  │ 0.4748 │ 0.8385 │ 0.5399 │ 0.4926 │ 0.5973 │
347
+ │ MUV  │ 0.4508 │ 0.8579 │ 0.4926 │ 0.5117 │ 0.4750 │
348
+ │ Bus  │ 0.6655 │ 0.7908 │ 0.7667 │ 0.7731 │ 0.7604 │
349
+ │ Truck  │ 0.5691 │ 0.8062 │ 0.6682 │ 0.6714 │ 0.6650 │
350
+ │ Three-wheeler  │ 0.6866 │ 0.7647 │ 0.8386 │ 0.8538 │ 0.8239 │
351
+ │ Two-wheeler  │ 0.5979 │ 0.6994 │ 0.8059 │ 0.8181 │ 0.7940 │
352
+ │ LCV  │ 0.5837 │ 0.7941 │ 0.6871 │ 0.6513 │ 0.7270 │
353
+ │ Mini-bus  │ 0.2118 │ 0.7876 │ 0.2866 │ 0.3212 │ 0.2588 │
354
+ │ Tempo-traveller │ 0.6507 │ 0.8634 │ 0.6860 │ 0.6982 │ 0.6743 │
355
+ │ Bicycle  │ 0.4633 │ 0.7283 │ 0.6049 │ 0.6773 │ 0.5465 │
356
+ │ Van  │ 0.3519 │ 0.8602 │ 0.4400 │ 0.4958 │ 0.3955 │
357
+ └─────────────────┴──────────┴────────┴────────┴───────────┴────────┘
358
+ Epoch 8: 100%|█| 2331/2331 [33:36<00:00, 1.16it/s, v_num=ikbe, train/lr=1e-5, t[2026-04-04 21:55:52] [INFO] rf-detr - Best regular mAP saved to /kaggle/working/output/checkpoint_best_regular.pth (epoch 8)
359
+ [2026-04-04 21:55:53] [INFO] rf-detr - Best EMA mAP improved to 0.5240 (epoch 8)
360
+ [rank: 0] Metric __rfdetr_effective_map__ improved by 0.003 >= min_delta = 0.001. New best score: 0.524
361
+ Epoch 9: 100%|█| 2331/2331 [25:19<00:00, 1.53it/s, v_num=ikbe, train/lr=1e-5, t Val — Overall Metrics
362
+ ┏━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━┓0:00, 2.93it/s]
363
+ ┃ mAP ┃ mAR ┃ F1 sweep ┃
364
+ ┡━━━━━━━━┯━━━━━━━━┯━━━━━━━━╇━━━━━━━━╇━━━━━━━━┯━━━━━━━━┯━━━━━━━━┩
365
+ │ 50:95 │ 50 │ 75 │ @500 │ F1 │ Prec │ Recall │
366
+ ├────────┼────────┼────────┼────────┼────────┼────────┼────────┤
367
+ │ 0.5257 │ 0.6496 │ 0.5806 │ 0.8040 │ 0.6194 │ 0.6429 │ 0.6064 │
368
+ └────────┴────────┴────────┴────────┴────────┴────────┴────────┘
369
+  Val — Per-class Metrics 
370
+ ┏━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━┓
371
+ ┃ Class  ┃ AP 50:95 ┃  AR ┃  F1 ┃ Precision ┃ Recall ┃
372
+ ┡━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━┩
373
+ │ Hatchback  │ 0.5329 │ 0.8198 │ 0.6162 │ 0.5752 │ 0.6636 │
374
+ │ Sedan  │ 0.5629 │ 0.8483 │ 0.5974 │ 0.5866 │ 0.6086 │
375
+ │ SUV  │ 0.4781 │ 0.8356 │ 0.5403 │ 0.5193 │ 0.5630 │
376
+ │ MUV  │ 0.4517 │ 0.8586 │ 0.4877 │ 0.4456 │ 0.5385 │
377
+ │ Bus  │ 0.6673 │ 0.7940 │ 0.7745 │ 0.8121 │ 0.7402 │
378
+ │ Truck  │ 0.5753 │ 0.8041 │ 0.6748 │ 0.6870 │ 0.6631 │
379
+ │ Three-wheeler  │ 0.6869 │ 0.7647 │ 0.8412 │ 0.8654 │ 0.8183 │
380
+ │ Two-wheeler  │ 0.5991 │ 0.6989 │ 0.8104 │ 0.8401 │ 0.7826 │
381
+ │ LCV  │ 0.5833 │ 0.7925 │ 0.6932 │ 0.6936 │ 0.6928 │
382
+ │ Mini-bus  │ 0.2156 │ 0.7871 │ 0.2794 │ 0.3725 │ 0.2235 │
383
+ │ Tempo-traveller │ 0.6540 │ 0.8660 │ 0.6863 │ 0.6731 │ 0.7000 │
384
+ │ Bicycle  │ 0.4624 │ 0.7232 │ 0.6076 │ 0.7624 │ 0.5050 │
385
+ │ Van  │ 0.3644 │ 0.8591 │ 0.4436 │ 0.5245 │ 0.3843 │
386
+ └─────────────────┴──────────┴────────┴────────┴───────────┴────────┘
387
+ Epoch 9: 100%|█| 2331/2331 [33:43<00:00, 1.15it/s, v_num=ikbe, train/lr=1e-5, t[2026-04-04 22:29:39] [INFO] rf-detr - Best regular mAP saved to /kaggle/working/output/checkpoint_best_regular.pth (epoch 9)
388
+ [2026-04-04 22:29:39] [INFO] rf-detr - Best EMA mAP improved to 0.5271 (epoch 9)
389
+ [rank: 0] Metric __rfdetr_effective_map__ improved by 0.003 >= min_delta = 0.001. New best score: 0.527
390
+ Epoch 10: 100%|█| 2331/2331 [25:20<00:00, 1.53it/s, v_num=ikbe, train/lr=1e-5, Val — Overall Metrics
391
+ ┏━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━┓0:00, 2.90it/s]
392
+ ┃ mAP ┃ mAR ┃ F1 sweep ┃
393
+ ┡━━━━━━━━┯━━━━━━━━┯━━━━━━━━╇━━━━━━━━╇━━━━━━━━┯━━━━━━━━┯━━━━━━━━┩
394
+ │ 50:95 │ 50 │ 75 │ @500 │ F1 │ Prec │ Recall │
395
+ ├────────┼────────┼────────┼────────┼────────┼────────┼────────┤
396
+ │ 0.5270 │ 0.6509 │ 0.5824 │ 0.8047 │ 0.6230 │ 0.6270 │ 0.6250 │
397
+ └────────┴────────┴────────┴────────┴────────┴────────┴────────┘
398
+  Val — Per-class Metrics 
399
+ ┏━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━┓
400
+ ┃ Class  ┃ AP 50:95 ┃  AR ┃  F1 ┃ Precision ┃ Recall ┃
401
+ ┡━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━┩
402
+ │ Hatchback  │ 0.5358 │ 0.8192 │ 0.6170 │ 0.5446 │ 0.7117 │
403
+ │ Sedan  │ 0.5634 │ 0.8468 │ 0.5997 │ 0.5700 │ 0.6326 │
404
+ │ SUV  │ 0.4800 │ 0.8370 │ 0.5458 │ 0.5140 │ 0.5819 │
405
+ │ MUV  │ 0.4556 │ 0.8584 │ 0.4939 │ 0.5051 │ 0.4832 │
406
+ │ Bus  │ 0.6699 │ 0.7948 │ 0.7721 │ 0.8000 │ 0.7462 │
407
+ │ Truck  │ 0.5776 │ 0.8066 │ 0.6684 │ 0.6436 │ 0.6952 │
408
+ │ Three-wheeler  │ 0.6897 │ 0.7667 │ 0.8401 │ 0.8549 │ 0.8257 │
409
+ │ Two-wheeler  │ 0.6013 │ 0.7008 │ 0.8078 │ 0.8116 │ 0.8040 │
410
+ │ LCV  │ 0.5871 │ 0.7960 │ 0.6941 │ 0.6701 │ 0.7198 │
411
+ │ Mini-bus  │ 0.2081 │ 0.7859 │ 0.2894 │ 0.3191 │ 0.2647 │
412
+ │ Tempo-traveller │ 0.6537 │ 0.8663 │ 0.6941 │ 0.6882 │ 0.7000 │
413
+ │ Bicycle  │ 0.4654 │ 0.7229 │ 0.6107 │ 0.7190 │ 0.5308 │
414
+ │ Van  │ 0.3629 │ 0.8596 │ 0.4664 │ 0.5107 │ 0.4292 │
415
+ └─────────────────┴──────────┴────────┴────────┴───────────┴────────┘
416
+ Epoch 10: 100%|█| 2331/2331 [33:44<00:00, 1.15it/s, v_num=ikbe, train/lr=1e-5, [2026-04-04 23:03:28] [INFO] rf-detr - Best regular mAP saved to /kaggle/working/output/checkpoint_best_regular.pth (epoch 10)
417
+ [2026-04-04 23:03:28] [INFO] rf-detr - Best EMA mAP improved to 0.5286 (epoch 10)
418
+ [rank: 0] Metric __rfdetr_effective_map__ improved by 0.002 >= min_delta = 0.001. New best score: 0.529
419
+ Epoch 11: 100%|█| 2331/2331 [25:02<00:00, 1.55it/s, v_num=ikbe, train/lr=1e-5, Val — Overall Metrics
420
+ ┏━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━┓0:00, 2.89it/s]
421
+ ┃ mAP ┃ mAR ┃ F1 sweep ┃
422
+ ┡━━━━━━━━┯━━━━━━━━┯━━━━━━━━╇━━━━━━━━╇━━━━━━━━┯━━━━━━━━┯━━━━━━━━┩
423
+ │ 50:95 │ 50 │ 75 │ @500 │ F1 │ Prec │ Recall │
424
+ ├────────┼────────┼────────┼────────┼────────┼────────┼────────┤
425
+ │ 0.5283 │ 0.6519 │ 0.5840 │ 0.8054 │ 0.6243 │ 0.6428 │ 0.6127 │
426
+ └────────┴────────┴────────┴────────┴────────┴────────┴────────┘
427
+  Val — Per-class Metrics 
428
+ ┏━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━┓
429
+ ┃ Class  ┃ AP 50:95 ┃  AR ┃  F1 ┃ Precision ┃ Recall ┃
430
+ ┡━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━┩
431
+ │ Hatchback  │ 0.5385 │ 0.8204 │ 0.6229 │ 0.5696 │ 0.6873 │
432
+ │ Sedan  │ 0.5652 │ 0.8477 │ 0.5993 │ 0.6443 │ 0.5602 │
433
+ │ SUV  │ 0.4803 │ 0.8367 │ 0.5476 │ 0.5201 │ 0.5781 │
434
+ │ MUV  │ 0.4596 │ 0.8590 │ 0.4965 │ 0.5157 │ 0.4787 │
435
+ │ Bus  │ 0.6718 │ 0.7982 │ 0.7744 │ 0.7919 │ 0.7577 │
436
+ │ Truck  │ 0.5808 │ 0.8080 │ 0.6754 │ 0.6544 │ 0.6978 │
437
+ │ Three-wheeler  │ 0.6907 │ 0.7668 │ 0.8393 │ 0.8523 │ 0.8266 │
438
+ │ Two-wheeler  │ 0.6019 │ 0.6997 │ 0.8111 │ 0.8281 │ 0.7949 │
439
+ │ LCV  │ 0.5858 │ 0.7936 │ 0.6931 │ 0.7025 │ 0.6839 │
440
+ │ Mini-bus  │ 0.2091 │ 0.7900 │ 0.2761 │ 0.3228 │ 0.2412 │
441
+ │ Tempo-traveller │ 0.6561 │ 0.8663 │ 0.6977 │ 0.6899 │ 0.7057 │
442
+ │ Bicycle  │ 0.4631 │ 0.7219 │ 0.6108 │ 0.7596 │ 0.5107 │
443
+ │ Van  │ 0.3646 │ 0.8625 │ 0.4719 │ 0.5051 │ 0.4427 │
444
+ └─────────────────┴──────────┴────────┴────────┴──────────��┴────────┘
445
+ Epoch 11: 100%|█| 2331/2331 [33:27<00:00, 1.16it/s, v_num=ikbe, train/lr=1e-5, [2026-04-04 23:36:58] [INFO] rf-detr - Best regular mAP saved to /kaggle/working/output/checkpoint_best_regular.pth (epoch 11)
446
+ [2026-04-04 23:36:58] [INFO] rf-detr - Best EMA mAP improved to 0.5305 (epoch 11)
447
+ [rank: 0] Metric __rfdetr_effective_map__ improved by 0.002 >= min_delta = 0.001. New best score: 0.531
448
+ Epoch 12: 100%|█| 2331/2331 [25:01<00:00, 1.55it/s, v_num=ikbe, train/lr=1e-5, Val — Overall Metrics
449
+ ┏━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━┓0:00, 2.89it/s]
450
+ ┃ mAP ┃ mAR ┃ F1 sweep ┃
451
+ ┡━━━━━━━━┯━━━━━━━━┯━━━━━━━━╇━━━━━━━━╇━━━━━━━━┯━━━━━━━━┯━━━━━━━━┩
452
+ │ 50:95 │ 50 │ 75 │ @500 │ F1 │ Prec │ Recall │
453
+ ├────────┼────────┼────────┼────────┼────────┼────────┼────────┤
454
+ │ 0.5331 │ 0.6566 │ 0.5884 │ 0.8067 │ 0.6278 │ 0.6395 │ 0.6224 │
455
+ └────────┴────────┴────────┴────────┴────────┴────────┴────────┘
456
+  Val — Per-class Metrics 
457
+ ┏━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━┓
458
+ ┃ Class  ┃ AP 50:95 ┃  AR ┃  F1 ┃ Precision ┃ Recall ┃
459
+ ┡━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━┩
460
+ │ Hatchback  │ 0.5424 │ 0.8224 │ 0.6210 │ 0.5351 │ 0.7399 │
461
+ │ Sedan  │ 0.5698 │ 0.8514 │ 0.6023 │ 0.5882 │ 0.6170 │
462
+ │ SUV  │ 0.4838 │ 0.8373 │ 0.5463 │ 0.5667 │ 0.5273 │
463
+ │ MUV  │ 0.4668 │ 0.8618 │ 0.5062 │ 0.5280 │ 0.4862 │
464
+ │ Bus  │ 0.6707 │ 0.7954 │ 0.7779 │ 0.8191 │ 0.7407 │
465
+ │ Truck  │ 0.5781 │ 0.8106 │ 0.6750 │ 0.6785 │ 0.6715 │
466
+ │ Three-wheeler  │ 0.6930 │ 0.7685 │ 0.8432 │ 0.8687 │ 0.8191 │
467
+ │ Two-wheeler  │ 0.6041 │ 0.7046 │ 0.8115 │ 0.8304 │ 0.7934 │
468
+ │ LCV  │ 0.5899 │ 0.7970 │ 0.6897 │ 0.6560 │ 0.7270 │
469
+ │ Mini-bus  │ 0.2231 │ 0.7794 │ 0.2953 │ 0.3438 │ 0.2588 │
470
+ │ Tempo-traveller │ 0.6653 │ 0.8651 │ 0.7042 │ 0.7114 │ 0.6971 │
471
+ │ Bicycle  │ 0.4665 │ 0.7295 │ 0.6064 │ 0.7045 │ 0.5322 │
472
+ │ Van  │ 0.3765 │ 0.8647 │ 0.4820 │ 0.4831 │ 0.4809 │
473
+ └─────────────────┴──────────┴────────┴────────┴───────────┴────────┘
474
+ Epoch 12: 100%|█| 2331/2331 [33:26<00:00, 1.16it/s, v_num=ikbe, train/lr=1e-5, [2026-04-05 00:10:28] [INFO] rf-detr - Best regular mAP saved to /kaggle/working/output/checkpoint_best_regular.pth (epoch 12)
475
+ [2026-04-05 00:10:28] [INFO] rf-detr - Best EMA mAP improved to 0.5335 (epoch 12)
476
+ [rank: 0] Metric __rfdetr_effective_map__ improved by 0.003 >= min_delta = 0.001. New best score: 0.534
477
+ Epoch 13: 100%|█| 2331/2331 [25:41<00:00, 1.51it/s, v_num=ikbe, train/lr=1e-5, Val — Overall Metrics
478
+ ┏━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━┓0:00, 2.67it/s]
479
+ ┃ mAP ┃ mAR ┃ F1 sweep ┃
480
+ ┡━━━━━━━━┯━━━━━━━━┯━━━━━━━━╇━━━━━━━━╇━━━━━━━━┯━━━━━━━━┯━━━━━━━━┩
481
+ │ 50:95 │ 50 │ 75 │ @500 │ F1 │ Prec │ Recall │
482
+ ├────────┼────────┼────────┼────────┼────────┼────────┼────────┤
483
+ │ 0.5338 │ 0.6575 │ 0.5897 │ 0.8055 │ 0.6272 │ 0.6420 │ 0.6202 │
484
+ └────────┴────────┴────────┴────────┴────────┴────────┴────────┘
485
+  Val — Per-class Metrics 
486
+ ┏━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━┓
487
+ ┃ Class  ┃ AP 50:95 ┃  AR ┃  F1 ┃ Precision ┃ Recall ┃
488
+ ┡━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━┩
489
+ │ Hatchback  │ 0.5417 │ 0.8223 │ 0.6209 │ 0.5987 │ 0.6447 │
490
+ │ Sedan  │ 0.5705 │ 0.8505 │ 0.6039 │ 0.5720 │ 0.6395 │
491
+ │ SUV  │ 0.4850 │ 0.8394 │ 0.5432 │ 0.4788 │ 0.6278 │
492
+ │ MUV  │ 0.4610 │ 0.8608 │ 0.5007 │ 0.4974 │ 0.5041 │
493
+ │ Bus  │ 0.6745 │ 0.7987 │ 0.7740 │ 0.7829 │ 0.7653 │
494
+ │ Truck  │ 0.5813 │ 0.8065 │ 0.6793 │ 0.6834 │ 0.6753 │
495
+ │ Three-wheeler  │ 0.6950 │ 0.7702 │ 0.8439 │ 0.8677 │ 0.8214 │
496
+ │ Two-wheeler  │ 0.6060 │ 0.7060 │ 0.8128 │ 0.8332 │ 0.7933 │
497
+ │ LCV  │ 0.5901 │ 0.7947 │ 0.6978 │ 0.6835 │ 0.7127 │
498
+ │ Mini-bus  │ 0.2194 │ 0.7759 │ 0.2885 │ 0.3259 │ 0.2588 │
499
+ │ Tempo-traveller │ 0.6603 │ 0.8566 │ 0.7095 │ 0.7254 │ 0.6943 │
500
+ │ Bicycle  │ 0.4714 │ 0.7278 │ 0.6211 │ 0.7430 │ 0.5336 │
501
+ │ Van  │ 0.3832 │ 0.8620 │ 0.4585 │ 0.5541 │ 0.3910 │
502
+ └─────────────────┴──────────┴────────┴────────┴───────────┴────────┘
503
+ Epoch 13: 100%|█| 2331/2331 [34:52<00:00, 1.11it/s, v_num=ikbe, train/lr=1e-5, [2026-04-05 00:45:22] [INFO] rf-detr - Best regular mAP saved to /kaggle/working/output/checkpoint_best_regular.pth (epoch 13)
504
+ [2026-04-05 00:45:23] [INFO] rf-detr - Best EMA mAP improved to 0.5341 (epoch 13)
505
+ Epoch 14: 100%|█| 2331/2331 [25:56<00:00, 1.50it/s, v_num=ikbe, train/lr=1e-5, Val — Overall Metrics
506
+ ┏━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━┓0:00, 2.89it/s]
507
+ ┃ mAP ┃ mAR ┃ F1 sweep ┃
508
+ ┡━━━━━━━━┯━━━━━━━━┯━━━━━━━━╇━━━━━━━━╇━━━━━━━━┯━━━━━━━━┯━━━━━━━━┩
509
+ │ 50:95 │ 50 │ 75 │ @500 │ F1 │ Prec │ Recall │
510
+ ├────────┼────────┼────────┼────────┼────────┼────────┼────────┤
511
+ │ 0.5348 │ 0.6589 │ 0.5905 │ 0.8053 │ 0.6282 │ 0.6240 │ 0.6381 │
512
+ └────────┴────────┴────────┴────────┴────────┴────────┴────────┘
513
+  Val — Per-class Metrics 
514
+ ┏━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━┓
515
+ ┃ Class  ┃ AP 50:95 ┃  AR ┃  F1 ┃ Precision ┃ Recall ┃
516
+ ┡━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━┩
517
+ │ Hatchback  │ 0.5461 │ 0.8219 │ 0.6285 │ 0.5532 │ 0.7274 │
518
+ │ Sedan  │ 0.5737 │ 0.8502 │ 0.6089 │ 0.5840 │ 0.6361 │
519
+ │ SUV  │ 0.4871 │ 0.8378 │ 0.5479 │ 0.5080 │ 0.5947 │
520
+ │ MUV  │ 0.4687 │ 0.8584 │ 0.5087 │ 0.4669 │ 0.5586 │
521
+ │ Bus  │ 0.6715 │ 0.7951 │ 0.7670 │ 0.7633 │ 0.7708 │
522
+ │ Truck  │ 0.5793 │ 0.8053 │ 0.6764 │ 0.6718 │ 0.6810 │
523
+ │ Three-wheeler  │ 0.6943 │ 0.7697 │ 0.8402 │ 0.8494 │ 0.8312 │
524
+ │ Two-wheeler  │ 0.6036 │ 0.7018 │ 0.8095 │ 0.8137 │ 0.8054 │
525
+ │ LCV  │ 0.5919 │ 0.7959 │ 0.6912 │ 0.6468 │ 0.7421 │
526
+ │ Mini-bus  │ 0.2155 │ 0.7753 │ 0.2838 │ 0.3333 │ 0.2471 │
527
+ │ Tempo-traveller │ 0.6651 │ 0.8611 │ 0.7068 │ 0.7460 │ 0.6714 │
528
+ │ Bicycle  │ 0.4720 │ 0.7322 │ 0.6095 │ 0.6557 │ 0.5694 │
529
+ │ Van  │ 0.3830 │ 0.8636 │ 0.4887 │ 0.5203 │ 0.4607 │
530
+ └─────────────────┴──────────┴────────┴────────┴───────────┴────────┘
531
+ Epoch 14: 100%|█| 2331/2331 [34:21<00:00, 1.13it/s, v_num=ikbe, train/lr=1e-5, [2026-04-05 01:19:48] [INFO] rf-detr - Best regular mAP saved to /kaggle/working/output/checkpoint_best_regular.pth (epoch 14)
532
+ [2026-04-05 01:19:48] [INFO] rf-detr - Best EMA mAP improved to 0.5362 (epoch 14)
533
+ [rank: 0] Metric __rfdetr_effective_map__ improved by 0.003 >= min_delta = 0.001. New best score: 0.536
534
+ Epoch 15: 100%|█| 2331/2331 [25:09<00:00, 1.54it/s, v_num=ikbe, train/lr=1e-5, Val — Overall Metrics
535
+ ┏━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━┓0:00, 2.91it/s]
536
+ ┃ mAP ┃ mAR ┃ F1 sweep ┃
537
+ ┡━━━━━━━━┯━━━━━━━━┯━━━━━━━━╇━━━━━━━━╇━━━━━━━━┯━━━━━━━━┯━━━━━━━━┩
538
+ │ 50:95 │ 50 │ 75 │ @500 │ F1 │ Prec │ Recall │
539
+ ├────────┼────────┼────────┼────────┼────────┼────────┼────────┤
540
+ │ 0.5362 │ 0.6605 │ 0.5916 │ 0.8070 │ 0.6307 │ 0.6549 │ 0.6151 │
541
+ └────────┴────────┴────────┴────────┴────────┴────────┴────────┘
542
+  Val — Per-class Metrics 
543
+ ┏━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━┓
544
+ ┃ Class  ┃ AP 50:95 ┃  AR ┃  F1 ┃ Precision ┃ Recall ┃
545
+ ┡━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━┩
546
+ │ Hatchback  │ 0.5459 │ 0.8241 │ 0.6267 │ 0.6022 │ 0.6532 │
547
+ │ Sedan  │ 0.5746 │ 0.8512 │ 0.6078 │ 0.6411 │ 0.5777 │
548
+ │ SUV  │ 0.4868 │ 0.8391 │ 0.5502 │ 0.5336 │ 0.5679 │
549
+ │ MUV  │ 0.4689 │ 0.8630 │ 0.5079 │ 0.4721 │ 0.5497 │
550
+ │ Bus  │ 0.6745 │ 0.7981 │ 0.7765 │ 0.7915 │ 0.7620 │
551
+ │ Truck  │ 0.5787 │ 0.8079 │ 0.6734 │ 0.7337 │ 0.6223 │
552
+ │ Three-wheeler  │ 0.6953 │ 0.7716 │ 0.8441 │ 0.8665 │ 0.8229 │
553
+ │ Two-wheeler  │ 0.6047 │ 0.7028 │ 0.8131 │ 0.8361 │ 0.7914 │
554
+ │ LCV  │ 0.5944 │ 0.7995 │ 0.6976 │ 0.6802 │ 0.7160 │
555
+ │ Mini-bus  │ 0.2153 │ 0.7765 │ 0.2887 │ 0.3471 │ 0.2471 │
556
+ │ Tempo-traveller │ 0.6681 │ 0.8623 │ 0.7046 │ 0.6846 │ 0.7257 │
557
+ │ Bicycle  │ 0.4726 │ 0.7298 │ 0.6183 │ 0.7937 │ 0.5064 │
558
+ │ Van  │ 0.3908 │ 0.8647 │ 0.4897 │ 0.5316 │ 0.4539 │
559
+ └─────────────────┴──────────┴────────┴────────┴───────────┴────────┘
560
+ Epoch 15: 100%|█| 2331/2331 [33:34<00:00, 1.16it/s, v_num=ikbe, train/lr=1e-5, [2026-04-05 01:53:25] [INFO] rf-detr - Best regular mAP saved to /kaggle/working/output/checkpoint_best_regular.pth (epoch 15)
561
+ [2026-04-05 01:53:25] [INFO] rf-detr - Best EMA mAP improved to 0.5385 (epoch 15)
562
+ [rank: 0] Metric __rfdetr_effective_map__ improved by 0.002 >= min_delta = 0.001. New best score: 0.539
563
+ Epoch 16: 77%|▊| 1787/2331 [19:21<05:53, 1.54it/s, v_num=ikbe, train/lr=1e-5,
564
+
565
+ Detected KeyboardInterrupt, attempting graceful shutdown ...
566
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wandb/run-20260404_165325-pld9ikbe/files/requirements.txt ADDED
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459
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461
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462
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463
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464
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465
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466
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467
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468
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469
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470
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471
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472
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473
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474
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475
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476
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477
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478
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479
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480
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481
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482
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483
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484
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485
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486
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487
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488
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489
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490
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491
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492
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493
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494
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495
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496
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497
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498
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500
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501
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502
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503
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505
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506
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507
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508
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509
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510
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514
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515
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516
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517
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518
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519
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520
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521
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522
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523
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524
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525
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526
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527
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528
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529
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530
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531
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532
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533
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534
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535
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536
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537
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538
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539
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540
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541
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542
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543
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544
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545
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546
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547
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548
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549
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550
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551
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552
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553
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554
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555
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556
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557
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558
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559
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560
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561
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562
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563
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564
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565
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566
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567
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568
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569
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570
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571
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572
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573
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574
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575
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576
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577
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578
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579
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580
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581
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582
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583
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584
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586
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587
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588
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589
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590
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591
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592
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593
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594
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595
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596
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597
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598
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599
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600
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602
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603
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604
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605
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606
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607
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608
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609
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610
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611
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612
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613
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614
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615
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616
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617
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618
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619
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620
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622
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624
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625
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626
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627
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628
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629
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630
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631
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634
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635
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637
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638
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639
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641
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643
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648
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649
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650
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659
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671
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672
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673
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674
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678
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689
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694
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695
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696
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697
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698
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699
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702
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703
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705
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706
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707
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708
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709
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710
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711
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712
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713
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714
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715
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716
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717
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718
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719
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720
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721
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722
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723
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724
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726
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727
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728
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730
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731
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732
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737
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739
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740
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741
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742
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743
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744
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745
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746
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747
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748
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749
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750
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751
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752
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755
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759
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760
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761
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762
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763
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764
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765
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766
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767
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768
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769
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770
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771
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772
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773
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774
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775
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776
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777
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778
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779
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780
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781
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782
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783
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784
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785
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786
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787
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788
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790
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791
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792
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793
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794
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795
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796
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797
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798
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799
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800
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801
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804
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806
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807
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808
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809
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810
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811
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812
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813
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814
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815
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816
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817
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818
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823
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825
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827
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830
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831
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832
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833
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836
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838
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839
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840
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841
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842
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843
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844
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848
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849
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851
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853
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859
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860
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861
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862
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863
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864
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866
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871
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876
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881
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885
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886
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891
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893
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896
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898
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959
+ MarkupSafe==2.0.1
wandb/run-20260404_165325-pld9ikbe/files/wandb-metadata.json ADDED
@@ -0,0 +1,43 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
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+ "python": "CPython 3.12.12",
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