| --- |
| library_name: transformers |
| license: other |
| base_model: nvidia/mit-b0 |
| tags: |
| - image-segmentation |
| - vision |
| - generated_from_trainer |
| datasets: |
| - generator |
| model-index: |
| - name: autocrop-bilder |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # autocrop-bilder |
|
|
| This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the /mnt/disk1/autocrop-data/datasets/bilder dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.0434 |
| - Mean Iou: 0.4950 |
| - Mean Accuracy: 0.9899 |
| - Overall Accuracy: 0.9899 |
| - Accuracy Background: nan |
| - Accuracy Crop: 0.9899 |
| - Iou Background: 0.0 |
| - Iou Crop: 0.9899 |
|
|
| ## Model description |
|
|
| More information needed |
|
|
| ## Intended uses & limitations |
|
|
| More information needed |
|
|
| ## Training and evaluation data |
|
|
| More information needed |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 6e-05 |
| - train_batch_size: 8 |
| - eval_batch_size: 8 |
| - seed: 42 |
| - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| - lr_scheduler_type: cosine |
| - lr_scheduler_warmup_steps: 0.1 |
| - num_epochs: 50.0 |
| - mixed_precision_training: Native AMP |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Crop | Iou Background | Iou Crop | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-------------:|:--------------:|:--------:| |
| | 0.3277 | 1.0 | 112 | 0.3304 | 0.4474 | 0.8948 | 0.8948 | nan | 0.8948 | 0.0 | 0.8948 | |
| | 0.1834 | 2.0 | 224 | 0.1733 | 0.4725 | 0.9450 | 0.9450 | nan | 0.9450 | 0.0 | 0.9450 | |
| | 0.1279 | 3.0 | 336 | 0.1177 | 0.4907 | 0.9813 | 0.9813 | nan | 0.9813 | 0.0 | 0.9813 | |
| | 0.0879 | 4.0 | 448 | 0.0841 | 0.4929 | 0.9858 | 0.9858 | nan | 0.9858 | 0.0 | 0.9858 | |
| | 0.0796 | 5.0 | 560 | 0.0840 | 0.4871 | 0.9742 | 0.9742 | nan | 0.9742 | 0.0 | 0.9742 | |
| | 0.0641 | 6.0 | 672 | 0.0709 | 0.4930 | 0.9860 | 0.9860 | nan | 0.9860 | 0.0 | 0.9860 | |
| | 0.0523 | 7.0 | 784 | 0.0633 | 0.4947 | 0.9894 | 0.9894 | nan | 0.9894 | 0.0 | 0.9894 | |
| | 0.0577 | 8.0 | 896 | 0.0606 | 0.4904 | 0.9807 | 0.9807 | nan | 0.9807 | 0.0 | 0.9807 | |
| | 0.0528 | 9.0 | 1008 | 0.0596 | 0.4952 | 0.9904 | 0.9904 | nan | 0.9904 | 0.0 | 0.9904 | |
| | 0.0449 | 10.0 | 1120 | 0.0565 | 0.4925 | 0.9850 | 0.9850 | nan | 0.9850 | 0.0 | 0.9850 | |
| | 0.0466 | 11.0 | 1232 | 0.0533 | 0.4926 | 0.9853 | 0.9853 | nan | 0.9853 | 0.0 | 0.9853 | |
| | 0.0464 | 12.0 | 1344 | 0.0500 | 0.4937 | 0.9874 | 0.9874 | nan | 0.9874 | 0.0 | 0.9874 | |
| | 0.0456 | 13.0 | 1456 | 0.0503 | 0.4957 | 0.9914 | 0.9914 | nan | 0.9914 | 0.0 | 0.9914 | |
| | 0.0394 | 14.0 | 1568 | 0.0491 | 0.4938 | 0.9876 | 0.9876 | nan | 0.9876 | 0.0 | 0.9876 | |
| | 0.0402 | 15.0 | 1680 | 0.0514 | 0.4960 | 0.9921 | 0.9921 | nan | 0.9921 | 0.0 | 0.9921 | |
| | 0.0421 | 16.0 | 1792 | 0.0489 | 0.4955 | 0.9910 | 0.9910 | nan | 0.9910 | 0.0 | 0.9910 | |
| | 0.0453 | 17.0 | 1904 | 0.0461 | 0.4947 | 0.9894 | 0.9894 | nan | 0.9894 | 0.0 | 0.9894 | |
| | 0.0449 | 18.0 | 2016 | 0.0485 | 0.4929 | 0.9858 | 0.9858 | nan | 0.9858 | 0.0 | 0.9858 | |
| | 0.0349 | 19.0 | 2128 | 0.0468 | 0.4962 | 0.9925 | 0.9925 | nan | 0.9925 | 0.0 | 0.9925 | |
| | 0.0351 | 20.0 | 2240 | 0.0470 | 0.4962 | 0.9924 | 0.9924 | nan | 0.9924 | 0.0 | 0.9924 | |
| | 0.0324 | 21.0 | 2352 | 0.0452 | 0.4949 | 0.9897 | 0.9897 | nan | 0.9897 | 0.0 | 0.9897 | |
| | 0.0367 | 22.0 | 2464 | 0.0461 | 0.4949 | 0.9897 | 0.9897 | nan | 0.9897 | 0.0 | 0.9897 | |
| | 0.0350 | 23.0 | 2576 | 0.0451 | 0.4952 | 0.9903 | 0.9903 | nan | 0.9903 | 0.0 | 0.9903 | |
| | 0.0354 | 24.0 | 2688 | 0.0469 | 0.4957 | 0.9914 | 0.9914 | nan | 0.9914 | 0.0 | 0.9914 | |
| | 0.0353 | 25.0 | 2800 | 0.0452 | 0.4945 | 0.9890 | 0.9890 | nan | 0.9890 | 0.0 | 0.9890 | |
| | 0.0334 | 26.0 | 2912 | 0.0448 | 0.4962 | 0.9924 | 0.9924 | nan | 0.9924 | 0.0 | 0.9924 | |
| | 0.0269 | 27.0 | 3024 | 0.0448 | 0.4958 | 0.9915 | 0.9915 | nan | 0.9915 | 0.0 | 0.9915 | |
| | 0.0319 | 28.0 | 3136 | 0.0443 | 0.4949 | 0.9898 | 0.9898 | nan | 0.9898 | 0.0 | 0.9898 | |
| | 0.0293 | 29.0 | 3248 | 0.0450 | 0.4962 | 0.9924 | 0.9924 | nan | 0.9924 | 0.0 | 0.9924 | |
| | 0.0306 | 30.0 | 3360 | 0.0438 | 0.4962 | 0.9923 | 0.9923 | nan | 0.9923 | 0.0 | 0.9923 | |
| | 0.0278 | 31.0 | 3472 | 0.0447 | 0.4960 | 0.9920 | 0.9920 | nan | 0.9920 | 0.0 | 0.9920 | |
| | 0.0268 | 32.0 | 3584 | 0.0459 | 0.4962 | 0.9924 | 0.9924 | nan | 0.9924 | 0.0 | 0.9924 | |
| | 0.0269 | 33.0 | 3696 | 0.0434 | 0.4950 | 0.9899 | 0.9899 | nan | 0.9899 | 0.0 | 0.9899 | |
| | 0.0268 | 34.0 | 3808 | 0.0445 | 0.4953 | 0.9906 | 0.9906 | nan | 0.9906 | 0.0 | 0.9906 | |
| | 0.0302 | 35.0 | 3920 | 0.0443 | 0.4946 | 0.9891 | 0.9891 | nan | 0.9891 | 0.0 | 0.9891 | |
| | 0.0239 | 36.0 | 4032 | 0.0439 | 0.4959 | 0.9919 | 0.9919 | nan | 0.9919 | 0.0 | 0.9919 | |
| | 0.0268 | 37.0 | 4144 | 0.0442 | 0.4958 | 0.9915 | 0.9915 | nan | 0.9915 | 0.0 | 0.9915 | |
| | 0.0318 | 38.0 | 4256 | 0.0451 | 0.4958 | 0.9916 | 0.9916 | nan | 0.9916 | 0.0 | 0.9916 | |
| | 0.0276 | 39.0 | 4368 | 0.0444 | 0.4956 | 0.9912 | 0.9912 | nan | 0.9912 | 0.0 | 0.9912 | |
| | 0.0248 | 40.0 | 4480 | 0.0456 | 0.4960 | 0.9921 | 0.9921 | nan | 0.9921 | 0.0 | 0.9921 | |
| | 0.0244 | 41.0 | 4592 | 0.0449 | 0.4952 | 0.9905 | 0.9905 | nan | 0.9905 | 0.0 | 0.9905 | |
| | 0.0235 | 42.0 | 4704 | 0.0445 | 0.4961 | 0.9922 | 0.9922 | nan | 0.9922 | 0.0 | 0.9922 | |
| | 0.0241 | 43.0 | 4816 | 0.0445 | 0.4960 | 0.9920 | 0.9920 | nan | 0.9920 | 0.0 | 0.9920 | |
| | 0.0295 | 44.0 | 4928 | 0.0445 | 0.4959 | 0.9919 | 0.9919 | nan | 0.9919 | 0.0 | 0.9919 | |
| | 0.0252 | 45.0 | 5040 | 0.0443 | 0.4960 | 0.9919 | 0.9919 | nan | 0.9919 | 0.0 | 0.9919 | |
| | 0.0213 | 46.0 | 5152 | 0.0443 | 0.4961 | 0.9922 | 0.9922 | nan | 0.9922 | 0.0 | 0.9922 | |
| | 0.0238 | 47.0 | 5264 | 0.0446 | 0.4958 | 0.9917 | 0.9917 | nan | 0.9917 | 0.0 | 0.9917 | |
| | 0.0234 | 48.0 | 5376 | 0.0445 | 0.4959 | 0.9918 | 0.9918 | nan | 0.9918 | 0.0 | 0.9918 | |
| | 0.0223 | 49.0 | 5488 | 0.0445 | 0.4957 | 0.9914 | 0.9914 | nan | 0.9914 | 0.0 | 0.9914 | |
| | 0.0245 | 50.0 | 5600 | 0.0447 | 0.4959 | 0.9917 | 0.9917 | nan | 0.9917 | 0.0 | 0.9917 | |
|
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|
|
| ### Framework versions |
|
|
| - Transformers 5.8.0 |
| - Pytorch 2.11.0+cu130 |
| - Datasets 4.8.5 |
| - Tokenizers 0.22.2 |
|
|
