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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 |
### Framework versions
- Transformers 5.8.0
- Pytorch 2.11.0+cu130
- Datasets 4.8.5
- Tokenizers 0.22.2
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