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# foot-finetune-28-jan

This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the suncy13/FootImg dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1107
- Mean Iou: 0.0
- Mean Accuracy: nan
- Overall Accuracy: nan
- Accuracy Foot: nan
- Iou Foot: 0.0

## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Foot | Iou Foot |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------:|:--------:|
| 0.356         | 2.0   | 20   | 0.5295          | 0.0      | nan           | nan              | nan           | 0.0      |
| 0.2927        | 4.0   | 40   | 0.3244          | 0.0      | nan           | nan              | nan           | 0.0      |
| 0.2511        | 6.0   | 60   | 0.2386          | 0.0      | nan           | nan              | nan           | 0.0      |
| 0.2458        | 8.0   | 80   | 0.2305          | 0.0      | nan           | nan              | nan           | 0.0      |
| 0.2152        | 10.0  | 100  | 0.2065          | 0.0      | nan           | nan              | nan           | 0.0      |
| 0.1996        | 12.0  | 120  | 0.1905          | 0.0      | nan           | nan              | nan           | 0.0      |
| 0.1878        | 14.0  | 140  | 0.1823          | 0.0      | nan           | nan              | nan           | 0.0      |
| 0.1902        | 16.0  | 160  | 0.1743          | 0.0      | nan           | nan              | nan           | 0.0      |
| 0.1646        | 18.0  | 180  | 0.1572          | 0.0      | nan           | nan              | nan           | 0.0      |
| 0.1512        | 20.0  | 200  | 0.1552          | 0.0      | nan           | nan              | nan           | 0.0      |
| 0.1438        | 22.0  | 220  | 0.1415          | 0.0      | nan           | nan              | nan           | 0.0      |
| 0.1355        | 24.0  | 240  | 0.1424          | 0.0      | nan           | nan              | nan           | 0.0      |
| 0.1342        | 26.0  | 260  | 0.1322          | 0.0      | nan           | nan              | nan           | 0.0      |
| 0.1355        | 28.0  | 280  | 0.1307          | 0.0      | nan           | nan              | nan           | 0.0      |
| 0.1198        | 30.0  | 300  | 0.1238          | 0.0      | nan           | nan              | nan           | 0.0      |
| 0.1179        | 32.0  | 320  | 0.1229          | 0.0      | nan           | nan              | nan           | 0.0      |
| 0.1108        | 34.0  | 340  | 0.1196          | 0.0      | nan           | nan              | nan           | 0.0      |
| 0.1145        | 36.0  | 360  | 0.1182          | 0.0      | nan           | nan              | nan           | 0.0      |
| 0.1097        | 38.0  | 380  | 0.1168          | 0.0      | nan           | nan              | nan           | 0.0      |
| 0.1199        | 40.0  | 400  | 0.1164          | 0.0      | nan           | nan              | nan           | 0.0      |
| 0.1185        | 42.0  | 420  | 0.1138          | 0.0      | nan           | nan              | nan           | 0.0      |
| 0.1026        | 44.0  | 440  | 0.1115          | 0.0      | nan           | nan              | nan           | 0.0      |
| 0.1039        | 46.0  | 460  | 0.1100          | 0.0      | nan           | nan              | nan           | 0.0      |
| 0.1091        | 48.0  | 480  | 0.1107          | 0.0      | nan           | nan              | nan           | 0.0      |
| 0.1074        | 50.0  | 500  | 0.1107          | 0.0      | nan           | nan              | nan           | 0.0      |


### Framework versions

- Transformers 4.37.1
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1

{"license": "other", "tags": ["vision", "image-segmentation", "generated_from_trainer"], "base_model": "nvidia/mit-b0", "model-index": [{"name": "foot-finetune-28-jan", "results": []}]}
transformers tensorboard safetensors segformer vision image-segmentation generated_from_trainer base_model:nvidia/mit-b0 license:other endpoints_compatible region:us