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# segformer-b0-finetuned-segments-thin-sections-oct2

This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the mmcint/ara-sections-test dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5032
- Mean Iou: 0.0229
- Mean Accuracy: 0.0343
- Overall Accuracy: 0.0268
- Accuracy No-label: nan
- Accuracy Liptinite: 0.0686
- Accuracy Vitrinite: 0.0
- Accuracy Inertinite: nan
- Iou No-label: 0.0
- Iou Liptinite: 0.0686
- Iou Vitrinite: 0.0
- Iou Inertinite: nan

## 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: 6
- eval_batch_size: 6
- 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 No-label | Accuracy Liptinite | Accuracy Vitrinite | Accuracy Inertinite | Iou No-label | Iou Liptinite | Iou Vitrinite | Iou Inertinite |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-----------------:|:------------------:|:------------------:|:-------------------:|:------------:|:-------------:|:-------------:|:--------------:|
| 1.1412        | 5.0   | 20   | 1.3339          | 0.1628   | 0.4001        | 0.3155           | nan               | 0.7892             | 0.0110             | nan                 | 0.0          | 0.6403        | 0.0110        | 0.0            |
| 0.8928        | 10.0  | 40   | 1.0736          | 0.0046   | 0.0092        | 0.0072           | nan               | 0.0183             | 0.0                | nan                 | 0.0          | 0.0183        | 0.0           | 0.0            |
| 0.785         | 15.0  | 60   | 0.7552          | 0.0027   | 0.0055        | 0.0043           | nan               | 0.0109             | 0.0                | nan                 | 0.0          | 0.0109        | 0.0           | 0.0            |
| 0.6991        | 20.0  | 80   | 0.6564          | 0.0022   | 0.0033        | 0.0026           | nan               | 0.0067             | 0.0                | nan                 | 0.0          | 0.0067        | 0.0           | nan            |
| 0.6379        | 25.0  | 100  | 0.5975          | 0.0041   | 0.0062        | 0.0049           | nan               | 0.0124             | 0.0                | nan                 | 0.0          | 0.0124        | 0.0           | nan            |
| 0.7257        | 30.0  | 120  | 0.5259          | 0.0016   | 0.0023        | 0.0018           | nan               | 0.0047             | 0.0                | nan                 | 0.0          | 0.0047        | 0.0           | nan            |
| 0.613         | 35.0  | 140  | 0.5320          | 0.0131   | 0.0197        | 0.0154           | nan               | 0.0394             | 0.0                | nan                 | 0.0          | 0.0394        | 0.0           | nan            |
| 0.5534        | 40.0  | 160  | 0.5038          | 0.0135   | 0.0202        | 0.0158           | nan               | 0.0405             | 0.0                | nan                 | 0.0          | 0.0405        | 0.0           | nan            |
| 0.5424        | 45.0  | 180  | 0.5016          | 0.0278   | 0.0417        | 0.0327           | nan               | 0.0835             | 0.0                | nan                 | 0.0          | 0.0835        | 0.0           | nan            |
| 0.533         | 50.0  | 200  | 0.5032          | 0.0229   | 0.0343        | 0.0268           | nan               | 0.0686             | 0.0                | nan                 | 0.0          | 0.0686        | 0.0           | nan            |


### Framework versions

- Transformers 4.33.3
- Pytorch 2.0.1
- Datasets 2.14.5
- Tokenizers 0.13.3

{"license": "other", "tags": ["vision", "image-segmentation", "generated_from_trainer"], "base_model": "nvidia/mit-b0", "model-index": [{"name": "segformer-b0-finetuned-segments-thin-sections-oct2", "results": []}]}
transformers pytorch segformer vision image-segmentation generated_from_trainer base_model:nvidia/mit-b0 license:other endpoints_compatible region:us