|
--- |
|
library_name: transformers |
|
license: other |
|
base_model: nvidia/mit-b1 |
|
tags: |
|
- image-segmentation |
|
- vision |
|
- generated_from_trainer |
|
model-index: |
|
- name: segformer-finetuned-tt-2k-b1 |
|
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. --> |
|
|
|
# segformer-finetuned-tt-2k-b1 |
|
|
|
This model is a fine-tuned version of [nvidia/mit-b1](https://huggingface.co/nvidia/mit-b1) on the Saumya-Mundra/text255 dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.0929 |
|
- Mean Iou: 0.4897 |
|
- Mean Accuracy: 0.9793 |
|
- Overall Accuracy: 0.9793 |
|
- Accuracy Text: nan |
|
- Accuracy No Text: 0.9793 |
|
- Iou Text: 0.0 |
|
- Iou No Text: 0.9793 |
|
|
|
## 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-07 |
|
- train_batch_size: 8 |
|
- eval_batch_size: 8 |
|
- seed: 1337 |
|
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
|
- lr_scheduler_type: polynomial |
|
- training_steps: 2000 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Accuracy No Text | Accuracy Text | Iou No Text | Iou Text | Validation Loss | Mean Accuracy | Mean Iou | Overall Accuracy | |
|
|:-------------:|:-----:|:----:|:----------------:|:-------------:|:-----------:|:--------:|:---------------:|:-------------:|:--------:|:----------------:| |
|
| 0.3305 | 1.0 | 125 | 0.9586 | nan | 0.9586 | 0.0 | 0.1846 | 0.9586 | 0.4793 | 0.9586 | |
|
| 0.2037 | 2.0 | 250 | 0.9706 | nan | 0.9706 | 0.0 | 0.1322 | 0.9706 | 0.4853 | 0.9706 | |
|
| 0.1534 | 3.0 | 375 | 0.9784 | nan | 0.9784 | 0.0 | 0.1074 | 0.9784 | 0.4892 | 0.9784 | |
|
| 0.1313 | 4.0 | 500 | 0.9839 | nan | 0.9839 | 0.0 | 0.0976 | 0.9839 | 0.4920 | 0.9839 | |
|
| 0.1156 | 5.0 | 625 | 0.9799 | nan | 0.9799 | 0.0 | 0.1001 | 0.9799 | 0.4900 | 0.9799 | |
|
| 0.1123 | 6.0 | 750 | 0.9866 | nan | 0.9866 | 0.0 | 0.0920 | 0.9866 | 0.4933 | 0.9866 | |
|
| 0.108 | 7.0 | 875 | 0.9815 | nan | 0.9815 | 0.0 | 0.0946 | 0.9815 | 0.4908 | 0.9815 | |
|
| 0.1017 | 8.0 | 1000 | 0.9805 | nan | 0.9805 | 0.0 | 0.0943 | 0.9805 | 0.4903 | 0.9805 | |
|
| 0.0994 | 9.0 | 1125 | 0.9808 | nan | 0.9808 | 0.0 | 0.0927 | 0.9808 | 0.4904 | 0.9808 | |
|
| 0.0926 | 10.0 | 1250 | 0.9783 | nan | 0.9783 | 0.0 | 0.0957 | 0.9783 | 0.4891 | 0.9783 | |
|
| 0.0907 | 11.0 | 1375 | 0.9830 | nan | 0.9830 | 0.0 | 0.0913 | 0.9830 | 0.4915 | 0.9830 | |
|
| 0.0893 | 12.0 | 1500 | 0.9838 | nan | 0.9838 | 0.0 | 0.0893 | 0.9838 | 0.4919 | 0.9838 | |
|
| 0.0853 | 13.0 | 1625 | 0.9804 | nan | 0.9804 | 0.0 | 0.0913 | 0.9804 | 0.4902 | 0.9804 | |
|
| 0.0834 | 14.0 | 1750 | 0.9820 | nan | 0.9820 | 0.0 | 0.0899 | 0.9820 | 0.4910 | 0.9820 | |
|
| 0.0861 | 15.0 | 1875 | 0.9815 | nan | 0.9815 | 0.0 | 0.0902 | 0.9815 | 0.4907 | 0.9815 | |
|
| 0.0803 | 16.0 | 2000 | 0.9793 | nan | 0.9793 | 0.0 | 0.0929 | 0.9793 | 0.4897 | 0.9793 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.49.0.dev0 |
|
- Pytorch 2.5.1+cu124 |
|
- Datasets 3.2.0 |
|
- Tokenizers 0.21.0 |
|
|
