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- I have searched the Roboflow Notebooks issues and found no similar bug report.
Notebook name
Bug
Thank you for your excellent work. While attempting to fine-tune for a downstream detection task based on PaliGemma2, I noticed that the final training results produced many redundant bounding boxes. Based on the predictions, the model is capable of detecting the targets, but it continuously outputs additional bounding boxes until it reaches the set max-new-tokens limit. Could you provide any insights or suggestions on this issue?
<loc0400><loc0516><loc0652><loc0712> 7 of clubs ; <loc0292><loc0300><loc0584><loc0512> 8 of clubs ; <loc0406><loc0724><loc0708><loc1007> 5 of clubs ; <loc0216><loc0084><loc0528><loc0316> 6 of clubs ; <loc0400><loc0516><loc0648><loc0708> 6 of clubs ; <loc0292><loc0295><loc0580><loc0512> 8 of clubs ; <loc0412><loc0732><loc0701><loc1007> 4 of clubs ; <loc0208><loc0080><loc0528><loc0316> 5 of clubs ; <loc0756><loc0136><loc1023><loc0316> 10 of clubs ; <loc0000><loc0000><loc1023><loc1016> 9 of clubs ; <loc0000><loc0000><loc0580><loc0540> 10 of clubs ; <loc0416><loc0540><loc0644><loc0708> 8 of clubs ; <loc0756><loc0144><loc0880><loc0292> 5 of clubs ; <loc0756><loc0144><loc1023><loc0322> 2 of clubs ; <loc0756><loc0144><loc1023><loc0316> 9 of clubs ; <loc0756><loc0144><loc1023><loc0316> 5 of clubs ; <loc0756><loc0144><loc1023><loc0305> 5 of clubs ; <loc0756><loc0144><loc1023><loc0305> 5 of clubs ; <loc0756><loc0144><loc1023><loc0295> 5 of clubs ; <loc0756><loc0232><loc1023><loc0322> 5 of clubs ; <loc0738><loc0000><loc1023><loc0136> 5 of clubs ; <loc0756><loc0000><loc1023><loc0136> 5 of clubs ; <loc0756><loc0000><loc1023><loc0136> 5 of clubs ; <loc0000><loc0000><loc0580><loc0372> 10 of clubs ; <loc0738><loc0000><loc1023><loc0136> 5 of clubs ; <loc0738><loc0000><loc1023>Environment
- Local
- OS: Ubuntu 20.04
- Python: 3.10.6
- Transformers: 4.47.0
Minimal Reproducible Example
No response
Additional
Additionally, here is the terminal log output:
ubuntu@ubuntu:/ssd2/workspace/mllm/fine-tune-paligemma/Google-PaliGemma2-Finetune$ CUDA_VISIBLE_DEVICES=2,3 python train.py --lora --epochs 8
hyperparameters: remove_unused_columns=False, gradient_accumulation_steps=16, warmup_steps=2, weight_decay=1e-06, adam_beta2=0.999, logging_steps=50, optim=adamw_hf, save_strategy=steps, save_steps=200, save_total_limit=1, bf16=True, report_to=['tensorboard'], dataloader_pin_memory=False
Loading checkpoint shards: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:05<00:00, 3.00s/it]
trainable params: 11,876,352 || all params: 3,045,003,504 || trainable%: 0.3900
freezing vision model layers
freezing multi-modal projector
Detected kernel version 5.4.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.
{'loss': 2.6005, 'grad_norm': 23.956716537475586, 'learning_rate': 1.7587939698492464e-05, 'epoch': 0.99}
{'loss': 1.9132, 'grad_norm': 23.055450439453125, 'learning_rate': 1.5075376884422112e-05, 'epoch': 1.97}
{'loss': 1.6768, 'grad_norm': 33.97260284423828, 'learning_rate': 1.256281407035176e-05, 'epoch': 2.95}
{'loss': 1.5855, 'grad_norm': 26.143875122070312, 'learning_rate': 1.0050251256281408e-05, 'epoch': 3.93}
{'loss': 1.5406, 'grad_norm': 24.072601318359375, 'learning_rate': 7.537688442211056e-06, 'epoch': 4.91}
{'loss': 1.515, 'grad_norm': 34.959720611572266, 'learning_rate': 5.025125628140704e-06, 'epoch': 5.89}
{'loss': 1.5009, 'grad_norm': 29.38210105895996, 'learning_rate': 2.512562814070352e-06, 'epoch': 6.87}
{'loss': 1.4799, 'grad_norm': 39.997161865234375, 'learning_rate': 0.0, 'epoch': 7.85}
{'train_runtime': 5094.8047, 'train_samples_per_second': 1.273, 'train_steps_per_second': 0.079, 'train_loss': 1.726562728881836, 'epoch': 7.85}
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 400/400 [1:24:54<00:00, 12.74s/it]
0%| | 0/44 [00:00<?, ?it/s]The 'batch_size' attribute of HybridCache is deprecated and will be removed in v4.49. Use the more precisely named 'self.max_batch_size' attribute instead.
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 44/44 [32:50<00:00, 44.77s/it]
map_result: MeanAveragePrecisionResult:
Metric target: MetricTarget.BOXES
Class agnostic: False
mAP @ 50:95: 0.4415
mAP @ 50: 0.4892
mAP @ 75: 0.4738
mAP scores: [0.48919323 0.48853667 0.48907614 0.47919466 0.47776663 0.47379989
0.47379989 0.4553695 0.38925978 0.19944912]
IoU thresh: [0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95]
AP per class:
0: [0.03280268 0.03280268 0.03280268 0.03280268 0.03280268 0.03280268
0.03280268 0.03280268 0.03280268 0.02328249]
1: [0.07497781 0.07497781 0.07497781 0.07497781 0.07497781 0.07497781
0.07497781 0.07497781 0.07497781 0.02398884]
2: [0.05993939 0.05993939 0.05993939 0.05993939 0.05993939 0.05993939
0.05993939 0.05993939 0.04830372 0.03577281]
3: [0.07545013 0.07545013 0.07545013 0.07545013 0.07545013 0.07545013
0.07545013 0.07545013 0.07545013 0.04084158]
4: [0.23377338 0.23377338 0.23377338 0.23377338 0.23377338 0.23377338
0.23377338 0.23377338 0.23377338 0.12376238]
5: [0.1980198 0.1980198 0.1980198 0.1980198 0.1980198 0.1980198 0.1980198
0.1980198 0.1980198 0.1980198]
6: [0.24752475 0.24752475 0.24752475 0.24752475 0.24752475 0.24752475
0.24752475 0.24752475 0.24752475 0.24752475]
7: [0.23883888 0.23883888 0.23883888 0.23883888 0.23883888 0.23883888
0.23883888 0.09207921 0.09207921 0. ]
8: [0.330033 0.330033 0.330033 0.330033 0.330033 0.330033
0.330033 0.330033 0.330033 0.08250825]
9: [0.330033 0.330033 0.330033 0.330033 0.330033 0.330033 0.330033 0.330033
0.330033 0.330033]
10: [0.4950495 0.4950495 0.4950495 0.4950495 0.4950495 0.4950495 0.4950495
0.4950495 0.4950495 0.4950495]
11: [0.32850071 0.32850071 0.32850071 0.32850071 0.32850071 0.32850071
0.32850071 0.32850071 0.12835926 0.04084158]
12: [0.7029703 0.7029703 0.7029703 0.7029703 0.7029703 0.7029703 0.7029703
0.7029703 0.7029703 0. ]
13: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
14: [0.4950495 0.4950495 0.4950495 0.4950495 0.4950495 0.4950495 0.4950495
0.4950495 0.4950495 0. ]
15: [0.32850071 0.32850071 0.32850071 0.32850071 0.32850071 0.32850071
0.32850071 0.32850071 0.20226308 0.0466761 ]
16: [0.46153408 0.42739274 0.34818482 0.34818482 0.27392739 0.27392739
0.27392739 0.27392739 0.27392739 0.27392739]
17: [0.34771334 0.34771334 0.34771334 0.34771334 0.34771334 0.34771334
0.34771334 0.34771334 0.34771334 0.16184311]
18: [0.34470678 0.34470678 0.34470678 0.34470678 0.34470678 0.34470678
0.34470678 0.34470678 0.19455264 0.04158416]
19: [0.11261932 0.11261932 0.11261932 0.11261932 0.11261932 0.11261932
0.11261932 0.07263803 0.0090009 0. ]
20: [0.76661952 0.76661952 0.76661952 0.76661952 0.76661952 0.76661952
0.76661952 0.76661952 0.47100424 0.04950495]
21: [0.62164074 0.62164074 0.62164074 0.62164074 0.62164074 0.62164074
0.62164074 0.62164074 0.62164074 0.04950495]
22: [0.83168317 0.83168317 0.83168317 0.83168317 0.83168317 0.83168317
0.83168317 0.83168317 0.61110325 0. ]
23: [0.71047105 0.71047105 0.71047105 0.47794779 0.47794779 0.47794779
0.47794779 0.47794779 0.08550855 0.04950495]
24: [0.51815182 0.51815182 0.51815182 0.51815182 0.51815182 0.51815182
0.51815182 0.51815182 0.51815182 0.43894389]
25: [0.71239981 0.71239981 0.71239981 0.71239981 0.71239981 0.71239981
0.71239981 0.71239981 0.71239981 0.5709571 ]
26: [0.6165732 0.6165732 0.6165732 0.6165732 0.6165732 0.6165732
0.6165732 0.6165732 0.52602183 0.20660066]
27: [0.72811567 0.72811567 0.72811567 0.44680182 0.44680182 0.44680182
0.44680182 0.15558699 0.07260726 0. ]
28: [0.74422442 0.74422442 0.74422442 0.74422442 0.74422442 0.5379538
0.5379538 0.5379538 0.5379538 0.34818482]
29: [0.77310231 0.77310231 0.77310231 0.77310231 0.77310231 0.77310231
0.77310231 0.77310231 0.77310231 0.14438944]
30: [0.74014555 0.74014555 0.74014555 0.74014555 0.74014555 0.74014555
0.74014555 0.74014555 0.74014555 0.17161716]
31: [0.55941981 0.55941981 0.55941981 0.55941981 0.55941981 0.55941981
0.55941981 0.55941981 0.55941981 0.01414427]
32: [0.47854785 0.47854785 0.47854785 0.47854785 0.47854785 0.47854785
0.47854785 0.47854785 0.47854785 0.22277228]
33: [0.42285479 0.42285479 0.42285479 0.42285479 0.42285479 0.42285479
0.42285479 0.42285479 0.24752475 0.25636492]
34: [0.2491377 0.2491377 0.2491377 0.2491377 0.2491377 0.2491377
0.2491377 0.10245912 0.10245912 0.04479019]
35: [0.08392268 0.08392268 0.08392268 0.08392268 0.08392268 0.08392268
0.08392268 0.08392268 0.08392268 0.01815182]
36: [0.44554455 0.44554455 0.44554455 0.44554455 0.44554455 0.44554455
0.44554455 0.44554455 0.44554455 0.0990099 ]
37: [0.4950495 0.4950495 0.4950495 0.4950495 0.4950495 0.4950495 0.4950495
0.4950495 0.4950495 1. ]
38: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
39: [0.44059406 0.44059406 0.44059406 0.44059406 0.44059406 0.44059406
0.44059406 0.44059406 0.4950495 0.08168317]
40: [1. 1. 1. 1. 1. 1.
1. 1. 1. 0.69059406]
41: [0.17030549 0.17030549 0.17030549 0.17030549 0.17030549 0.17030549
0.17030549 0.17030549 0.13645595 0.00634679]
42: [0.8019802 0.8019802 0.8019802 0.8019802 0.8019802 0.8019802 0.8019802
0.8019802 0.8019802 0.0990099]
43: [0.91584158 0.91584158 0.80693069 0.80693069 0.80693069 0.80693069
0.80693069 0.80693069 0.08168317 0. ]
44: [0.81848185 0.81848185 0.81848185 0.81848185 0.81848185 0.81848185
0.81848185 0.81848185 0.81848185 0.1320132 ]
45: [0.25990099 0.25990099 0.47607261 0.47607261 0.47607261 0.47607261
0.47607261 0.14232673 0.14232673 0.14232673]
46: [0.82791136 0.82791136 0.82791136 0.82791136 0.82791136 0.82791136
0.82791136 0.82791136 0.82791136 0.42479962]
47: [0.7019802 0.7019802 0.7019802 0.7019802 0.7019802 0.7019802
0.7019802 0.7019802 0.06534653 0. ]
48: [0.09806695 0.09806695 0.09806695 0.09806695 0.09806695 0.09806695
0.09806695 0.09806695 0.09806695 0.00884017]
49: [0.62871287 0.62871287 0.62871287 0.62871287 0.62871287 0.62871287
0.62871287 0.62871287 0.62871287 0.62871287]
50: [0.71287129 0.71287129 0.71287129 0.71287129 0.71287129 0.71287129
0.71287129 0.71287129 0.42574257 0.30693069]
51: [0.12575994 0.12575994 0.12575994 0.12575994 0.12575994 0.12575994
0.12575994 0.12575994 0.12575994 0. ]
Small objects:
MeanAveragePrecisionResult:
Metric target: MetricTarget.BOXES
Class agnostic: False
mAP @ 50:95: 0.0000
mAP @ 50: 0.0000
mAP @ 75: 0.0000
mAP scores: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
IoU thresh: [0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95]
AP per class:
No results
Medium objects:
MeanAveragePrecisionResult:
Metric target: MetricTarget.BOXES
Class agnostic: False
mAP @ 50:95: 0.0000
mAP @ 50: 0.0000
mAP @ 75: 0.0000
mAP scores: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
IoU thresh: [0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95]
AP per class:
No results
Large objects:
MeanAveragePrecisionResult:
Metric target: MetricTarget.BOXES
Class agnostic: False
mAP @ 50:95: 0.4800
mAP @ 50: 0.5283
mAP @ 75: 0.5193
mAP scores: [0.52829363 0.52765895 0.53252578 0.52467807 0.52340872 0.51931504
0.51931504 0.49847883 0.42658422 0.20021746]
IoU thresh: [0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95]
AP per class:
0: [0.03619003 0.03619003 0.03619003 0.03619003 0.03619003 0.03619003
0.03619003 0.03619003 0.03619003 0.02657742]
1: [0.05798595 0.05798595 0.05798595 0.05798595 0.05798595 0.05798595
0.05798595 0.05798595 0.05798595 0.0256342 ]
2: [0.04018435 0.04018435 0.04018435 0.04018435 0.04018435 0.04018435
0.04018435 0.04018435 0.0289021 0.01678454]
3: [0.11543189 0.11543189 0.11543189 0.11543189 0.11543189 0.11543189
0.11543189 0.11543189 0.11543189 0.08168317]
4: [0.26520509 0.26520509 0.26520509 0.26520509 0.26520509 0.26520509
0.26520509 0.26520509 0.26520509 0.12376238]
5: [0.24752475 0.24752475 0.24752475 0.24752475 0.24752475 0.24752475
0.24752475 0.24752475 0.24752475 0.24752475]
6: [0.330033 0.330033 0.330033 0.330033 0.330033 0.330033 0.330033 0.330033
0.330033 0.330033]
7: [0.56831683 0.56831683 0.56831683 0.56831683 0.56831683 0.56831683
0.56831683 0.4019802 0.4019802 0. ]
8: [0.330033 0.330033 0.330033 0.330033 0.330033 0.330033
0.330033 0.330033 0.330033 0.08250825]
9: [0.330033 0.330033 0.330033 0.330033 0.330033 0.330033 0.330033 0.330033
0.330033 0.330033]
10: [0.4950495 0.4950495 0.4950495 0.4950495 0.4950495 0.4950495 0.4950495
0.4950495 0.4950495 0.4950495]
11: [0.62623762 0.62623762 0.62623762 0.62623762 0.62623762 0.62623762
0.62623762 0.62623762 0.41831683 0.33663366]
12: [0.75247525 0.75247525 0.75247525 0.75247525 0.75247525 0.75247525
0.75247525 0.75247525 0.75247525 0. ]
13: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
14: [0.4950495 0.4950495 0.4950495 0.4950495 0.4950495 0.4950495 0.4950495
0.4950495 0.4950495 0. ]
15: [0.57953795 0.57953795 0.57953795 0.57953795 0.57953795 0.57953795
0.57953795 0.57953795 0.1379538 0.03630363]
16: [0.44494449 0.41194119 0.33993399 0.33993399 0.27392739 0.27392739
0.27392739 0.27392739 0.27392739 0.27392739]
17: [0.37600189 0.37600189 0.37600189 0.37600189 0.37600189 0.37600189
0.37600189 0.37600189 0.37600189 0.16184311]
18: [0.33200051 0.33200051 0.33200051 0.33200051 0.33200051 0.33200051
0.33200051 0.33200051 0.18223141 0.03272827]
19: [0.34899919 0.34899919 0.34899919 0.34899919 0.34899919 0.34899919
0.34899919 0.19309074 0.0330033 0. ]
20: [0.81188119 0.81188119 0.81188119 0.81188119 0.81188119 0.81188119
0.81188119 0.81188119 0.5049505 0.04950495]
21: [0.48797737 0.48797737 0.48797737 0.48797737 0.48797737 0.48797737
0.48797737 0.48797737 0.48797737 0.04950495]
22: [0.74257426 0.74257426 0.74257426 0.74257426 0.74257426 0.74257426
0.74257426 0.74257426 0.5675389 0. ]
23: [0.60003143 0.60003143 0.60003143 0.39336791 0.39336791 0.39336791
0.39336791 0.39336791 0.07150715 0.04950495]
24: [0.57001414 0.57001414 0.57001414 0.57001414 0.57001414 0.57001414
0.57001414 0.57001414 0.57001414 0.49080622]
25: [0.71239981 0.71239981 0.71239981 0.71239981 0.71239981 0.71239981
0.71239981 0.71239981 0.71239981 0.5709571 ]
26: [0.60591059 0.60591059 0.60591059 0.60591059 0.60591059 0.60591059
0.60591059 0.60591059 0.51749175 0.20660066]
27: [0.59619491 0.59619491 0.59619491 0.39477771 0.39477771 0.39477771
0.39477771 0.11745292 0.08958039 0. ]
28: [0.77062706 0.77062706 0.77062706 0.77062706 0.77062706 0.55775578
0.55775578 0.55775578 0.55775578 0.36138614]
29: [0.77310231 0.77310231 0.77310231 0.77310231 0.77310231 0.77310231
0.77310231 0.77310231 0.77310231 0.14438944]
30: [0.73443344 0.73443344 0.73443344 0.73443344 0.73443344 0.73443344
0.73443344 0.73443344 0.73443344 0.16984006]
31: [0.6039604 0.6039604 0.6039604 0.6039604 0.6039604 0.6039604 0.6039604
0.6039604 0.6039604 0.0330033]
32: [0.51980198 0.51980198 0.51980198 0.51980198 0.51980198 0.51980198
0.51980198 0.51980198 0.51980198 0.24339934]
33: [0.41136256 0.41136256 0.41136256 0.41136256 0.41136256 0.41136256
0.41136256 0.41136256 0.25636492 0.24752475]
34: [0.26440296 0.26440296 0.26440296 0.26440296 0.26440296 0.26440296
0.26440296 0.11423612 0.11423612 0.04696624]
35: [0.15470297 0.15470297 0.15470297 0.15470297 0.15470297 0.15470297
0.15470297 0.15470297 0.15470297 0.04084158]
36: [0.41254125 0.41254125 0.41254125 0.41254125 0.41254125 0.41254125
0.41254125 0.41254125 0.41254125 0.12376238]
37: [1. 1. 1. 1. 1. 1. 1.
1. 1. 0.4950495]
38: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
39: [0.7019802 0.7019802 0.7019802 0.7019802 0.7019802 0.7019802
0.7019802 0.7019802 0.75643564 0.06534653]
40: [1. 1. 1. 1. 1. 1.
1. 1. 1. 0.69059406]
41: [0.17030549 0.17030549 0.17030549 0.17030549 0.17030549 0.17030549
0.17030549 0.17030549 0.13645595 0.00634679]
42: [0.75247525 0.75247525 0.75247525 0.75247525 0.75247525 0.75247525
0.75247525 0.75247525 0.75247525 0.0990099 ]
43: [0.80693069 0.80693069 0.91584158 0.91584158 0.91584158 0.91584158
0.91584158 0.91584158 0.08168317 0. ]
44: [0.81848185 0.81848185 0.81848185 0.81848185 0.81848185 0.81848185
0.81848185 0.81848185 0.81848185 0.1320132 ]
45: [0.25990099 0.25990099 0.47607261 0.47607261 0.47607261 0.47607261
0.47607261 0.14232673 0.14232673 0.14232673]
46: [0.87741631 0.87741631 0.87741631 0.87741631 0.87741631 0.87741631
0.87741631 0.87741631 0.87741631 0.39179632]
47: [0.75643564 0.75643564 0.75643564 0.75643564 0.75643564 0.75643564
0.75643564 0.75643564 0.06534653 0. ]
48: [0.09806695 0.09806695 0.09806695 0.09806695 0.09806695 0.09806695
0.09806695 0.09806695 0.09806695 0.00884017]
49: [0.61103253 0.61103253 0.61103253 0.61103253 0.61103253 0.61103253
0.61103253 0.61103253 0.61103253 0.61103253]
50: [0.64686469 0.64686469 0.64686469 0.64686469 0.64686469 0.64686469
0.64686469 0.64686469 0.45874587 0.33993399]
51: [0.42822549 0.42822549 0.42822549 0.42822549 0.42822549 0.42822549
0.42822549 0.42822549 0.42822549 0. ]
Are you willing to submit a PR?
- Yes I'd like to help by submitting a PR!