| --- |
| library_name: transformers |
| license: other |
| base_model: nvidia/mit-b0 |
| tags: |
| - image-segmentation |
| - vision |
| - generated_from_trainer |
| datasets: |
| - generator |
| model-index: |
| - name: autocrop-tekst |
| 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-tekst |
|
|
| This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the /mnt/disk1/autocrop-data/datasets/tekst dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.0197 |
| - Mean Iou: 0.4970 |
| - Mean Accuracy: 0.9939 |
| - Overall Accuracy: 0.9939 |
| - Accuracy Background: nan |
| - Accuracy Crop: 0.9939 |
| - Iou Background: 0.0 |
| - Iou Crop: 0.9939 |
|
|
| ## 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 | |
| |:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-------------:|:--------------:|:--------:| |
| | 0.1117 | 1.0 | 624 | 0.0847 | 0.4905 | 0.9810 | 0.9810 | nan | 0.9810 | 0.0 | 0.9810 | |
| | 0.0532 | 2.0 | 1248 | 0.0444 | 0.4940 | 0.9880 | 0.9880 | nan | 0.9880 | 0.0 | 0.9880 | |
| | 0.0382 | 3.0 | 1872 | 0.0330 | 0.4962 | 0.9924 | 0.9924 | nan | 0.9924 | 0.0 | 0.9924 | |
| | 0.0302 | 4.0 | 2496 | 0.0292 | 0.4954 | 0.9908 | 0.9908 | nan | 0.9908 | 0.0 | 0.9908 | |
| | 0.0294 | 5.0 | 3120 | 0.0279 | 0.4946 | 0.9892 | 0.9892 | nan | 0.9892 | 0.0 | 0.9892 | |
| | 0.0284 | 6.0 | 3744 | 0.0255 | 0.4962 | 0.9925 | 0.9925 | nan | 0.9925 | 0.0 | 0.9925 | |
| | 0.0256 | 7.0 | 4368 | 0.0249 | 0.4953 | 0.9906 | 0.9906 | nan | 0.9906 | 0.0 | 0.9906 | |
| | 0.0294 | 8.0 | 4992 | 0.0242 | 0.4949 | 0.9898 | 0.9898 | nan | 0.9898 | 0.0 | 0.9898 | |
| | 0.0262 | 9.0 | 5616 | 0.0238 | 0.4963 | 0.9927 | 0.9927 | nan | 0.9927 | 0.0 | 0.9927 | |
| | 0.0262 | 10.0 | 6240 | 0.0230 | 0.4959 | 0.9918 | 0.9918 | nan | 0.9918 | 0.0 | 0.9918 | |
| | 0.0306 | 11.0 | 6864 | 0.0225 | 0.4965 | 0.9930 | 0.9930 | nan | 0.9930 | 0.0 | 0.9930 | |
| | 0.0223 | 12.0 | 7488 | 0.0221 | 0.4961 | 0.9921 | 0.9921 | nan | 0.9921 | 0.0 | 0.9921 | |
| | 0.0231 | 13.0 | 8112 | 0.0215 | 0.4963 | 0.9926 | 0.9926 | nan | 0.9926 | 0.0 | 0.9926 | |
| | 0.0208 | 14.0 | 8736 | 0.0215 | 0.4965 | 0.9931 | 0.9931 | nan | 0.9931 | 0.0 | 0.9931 | |
| | 0.0203 | 15.0 | 9360 | 0.0215 | 0.4966 | 0.9933 | 0.9933 | nan | 0.9933 | 0.0 | 0.9933 | |
| | 0.0222 | 16.0 | 9984 | 0.0211 | 0.4970 | 0.9940 | 0.9940 | nan | 0.9940 | 0.0 | 0.9940 | |
| | 0.0230 | 17.0 | 10608 | 0.0211 | 0.4967 | 0.9935 | 0.9935 | nan | 0.9935 | 0.0 | 0.9935 | |
| | 0.0228 | 18.0 | 11232 | 0.0211 | 0.4975 | 0.9950 | 0.9950 | nan | 0.9950 | 0.0 | 0.9950 | |
| | 0.0211 | 19.0 | 11856 | 0.0216 | 0.4968 | 0.9936 | 0.9936 | nan | 0.9936 | 0.0 | 0.9936 | |
| | 0.0201 | 20.0 | 12480 | 0.0211 | 0.4973 | 0.9945 | 0.9945 | nan | 0.9945 | 0.0 | 0.9945 | |
| | 0.0198 | 21.0 | 13104 | 0.0204 | 0.4969 | 0.9938 | 0.9938 | nan | 0.9938 | 0.0 | 0.9938 | |
| | 0.0204 | 22.0 | 13728 | 0.0206 | 0.4967 | 0.9934 | 0.9934 | nan | 0.9934 | 0.0 | 0.9934 | |
| | 0.0192 | 23.0 | 14352 | 0.0201 | 0.4965 | 0.9931 | 0.9931 | nan | 0.9931 | 0.0 | 0.9931 | |
| | 0.0201 | 24.0 | 14976 | 0.0202 | 0.4973 | 0.9946 | 0.9946 | nan | 0.9946 | 0.0 | 0.9946 | |
| | 0.0192 | 25.0 | 15600 | 0.0202 | 0.4971 | 0.9943 | 0.9943 | nan | 0.9943 | 0.0 | 0.9943 | |
| | 0.0189 | 26.0 | 16224 | 0.0197 | 0.4970 | 0.9939 | 0.9939 | nan | 0.9939 | 0.0 | 0.9939 | |
| | 0.0184 | 27.0 | 16848 | 0.0200 | 0.4970 | 0.9940 | 0.9940 | nan | 0.9940 | 0.0 | 0.9940 | |
| | 0.0179 | 28.0 | 17472 | 0.0201 | 0.4974 | 0.9948 | 0.9948 | nan | 0.9948 | 0.0 | 0.9948 | |
| | 0.0192 | 29.0 | 18096 | 0.0198 | 0.4971 | 0.9941 | 0.9941 | nan | 0.9941 | 0.0 | 0.9941 | |
| | 0.0176 | 30.0 | 18720 | 0.0199 | 0.4969 | 0.9938 | 0.9938 | nan | 0.9938 | 0.0 | 0.9938 | |
| | 0.0175 | 31.0 | 19344 | 0.0200 | 0.4972 | 0.9945 | 0.9945 | nan | 0.9945 | 0.0 | 0.9945 | |
| | 0.0158 | 32.0 | 19968 | 0.0200 | 0.4972 | 0.9944 | 0.9944 | nan | 0.9944 | 0.0 | 0.9944 | |
| | 0.0173 | 33.0 | 20592 | 0.0204 | 0.4971 | 0.9943 | 0.9943 | nan | 0.9943 | 0.0 | 0.9943 | |
| | 0.0172 | 34.0 | 21216 | 0.0201 | 0.4972 | 0.9943 | 0.9943 | nan | 0.9943 | 0.0 | 0.9943 | |
| | 0.0164 | 35.0 | 21840 | 0.0201 | 0.4972 | 0.9945 | 0.9945 | nan | 0.9945 | 0.0 | 0.9945 | |
| | 0.0156 | 36.0 | 22464 | 0.0199 | 0.4972 | 0.9944 | 0.9944 | nan | 0.9944 | 0.0 | 0.9944 | |
| | 0.0164 | 37.0 | 23088 | 0.0202 | 0.4973 | 0.9947 | 0.9947 | nan | 0.9947 | 0.0 | 0.9947 | |
| | 0.0159 | 38.0 | 23712 | 0.0201 | 0.4974 | 0.9947 | 0.9947 | nan | 0.9947 | 0.0 | 0.9947 | |
| | 0.0160 | 39.0 | 24336 | 0.0200 | 0.4970 | 0.9940 | 0.9940 | nan | 0.9940 | 0.0 | 0.9940 | |
| | 0.0169 | 40.0 | 24960 | 0.0201 | 0.4971 | 0.9942 | 0.9942 | nan | 0.9942 | 0.0 | 0.9942 | |
| | 0.0152 | 41.0 | 25584 | 0.0201 | 0.4970 | 0.9941 | 0.9941 | nan | 0.9941 | 0.0 | 0.9941 | |
| | 0.0159 | 42.0 | 26208 | 0.0201 | 0.4972 | 0.9944 | 0.9944 | nan | 0.9944 | 0.0 | 0.9944 | |
| | 0.0177 | 43.0 | 26832 | 0.0200 | 0.4970 | 0.9941 | 0.9941 | nan | 0.9941 | 0.0 | 0.9941 | |
| | 0.0142 | 44.0 | 27456 | 0.0201 | 0.4971 | 0.9942 | 0.9942 | nan | 0.9942 | 0.0 | 0.9942 | |
| | 0.0176 | 45.0 | 28080 | 0.0201 | 0.4972 | 0.9944 | 0.9944 | nan | 0.9944 | 0.0 | 0.9944 | |
| | 0.0159 | 46.0 | 28704 | 0.0203 | 0.4973 | 0.9946 | 0.9946 | nan | 0.9946 | 0.0 | 0.9946 | |
| | 0.0149 | 47.0 | 29328 | 0.0202 | 0.4972 | 0.9944 | 0.9944 | nan | 0.9944 | 0.0 | 0.9944 | |
| | 0.0159 | 48.0 | 29952 | 0.0201 | 0.4973 | 0.9946 | 0.9946 | nan | 0.9946 | 0.0 | 0.9946 | |
| | 0.0142 | 49.0 | 30576 | 0.0202 | 0.4972 | 0.9944 | 0.9944 | nan | 0.9944 | 0.0 | 0.9944 | |
| | 0.0139 | 50.0 | 31200 | 0.0201 | 0.4972 | 0.9944 | 0.9944 | nan | 0.9944 | 0.0 | 0.9944 | |
|
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|
|
| ### Framework versions |
|
|
| - Transformers 5.8.0 |
| - Pytorch 2.11.0+cu130 |
| - Datasets 4.8.5 |
| - Tokenizers 0.22.2 |
|
|
