---
license: other
base_model: nvidia/mit-b5
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b5-finetuned-segments-instryde-foot-test
  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-b5-finetuned-segments-instryde-foot-test

This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the inStryde/inStrydeSegmentationFoot dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0149
- Mean Iou: 0.4800
- Mean Accuracy: 0.9599
- Overall Accuracy: 0.9599
- Per Category Iou: [0.0, 0.9599216842864238]
- Per Category Accuracy: [nan, 0.9599216842864238]

## 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: 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 | Per Category Iou          | Per Category Accuracy     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------------:|:-------------------------:|
| 0.1024        | 0.27  | 20   | 0.2085          | 0.4534   | 0.9067        | 0.9067           | [0.0, 0.9067344993758137] | [nan, 0.9067344993758137] |
| 0.0431        | 0.53  | 40   | 0.0487          | 0.4604   | 0.9207        | 0.9207           | [0.0, 0.9207331455341442] | [nan, 0.9207331455341442] |
| 0.0354        | 0.8   | 60   | 0.0319          | 0.4577   | 0.9155        | 0.9155           | [0.0, 0.9154662028576415] | [nan, 0.9154662028576415] |
| 0.0389        | 1.07  | 80   | 0.0276          | 0.4629   | 0.9257        | 0.9257           | [0.0, 0.9257162800419576] | [nan, 0.9257162800419576] |
| 0.0208        | 1.33  | 100  | 0.0244          | 0.4702   | 0.9404        | 0.9404           | [0.0, 0.9403945317069335] | [nan, 0.9403945317069335] |
| 0.0241        | 1.6   | 120  | 0.0212          | 0.4703   | 0.9406        | 0.9406           | [0.0, 0.9406131407017349] | [nan, 0.9406131407017349] |
| 0.0167        | 1.87  | 140  | 0.0208          | 0.4761   | 0.9521        | 0.9521           | [0.0, 0.9521215619420916] | [nan, 0.9521215619420916] |
| 0.0156        | 2.13  | 160  | 0.0205          | 0.4612   | 0.9224        | 0.9224           | [0.0, 0.9224359945462809] | [nan, 0.9224359945462809] |
| 0.0156        | 2.4   | 180  | 0.0208          | 0.4734   | 0.9468        | 0.9468           | [0.0, 0.9467575875538612] | [nan, 0.9467575875538612] |
| 0.0167        | 2.67  | 200  | 0.0182          | 0.4833   | 0.9667        | 0.9667           | [0.0, 0.9666659635383208] | [nan, 0.9666659635383208] |
| 0.0145        | 2.93  | 220  | 0.0243          | 0.4351   | 0.8702        | 0.8702           | [0.0, 0.8702122233110058] | [nan, 0.8702122233110058] |
| 0.0114        | 3.2   | 240  | 0.0176          | 0.4686   | 0.9373        | 0.9373           | [0.0, 0.93726765603217]   | [nan, 0.93726765603217]   |
| 0.0155        | 3.47  | 260  | 0.0161          | 0.4770   | 0.9541        | 0.9541           | [0.0, 0.9540767701096305] | [nan, 0.9540767701096305] |
| 0.0158        | 3.73  | 280  | 0.0169          | 0.4684   | 0.9368        | 0.9368           | [0.0, 0.9368239181251786] | [nan, 0.9368239181251786] |
| 0.0114        | 4.0   | 300  | 0.0162          | 0.4777   | 0.9554        | 0.9554           | [0.0, 0.9554348305492647] | [nan, 0.9554348305492647] |
| 0.0112        | 4.27  | 320  | 0.0159          | 0.4839   | 0.9678        | 0.9678           | [0.0, 0.9677532556440432] | [nan, 0.9677532556440432] |
| 0.0131        | 4.53  | 340  | 0.0154          | 0.4811   | 0.9622        | 0.9622           | [0.0, 0.9622032718479555] | [nan, 0.9622032718479555] |
| 0.0101        | 4.8   | 360  | 0.0156          | 0.4683   | 0.9367        | 0.9367           | [0.0, 0.9366846987126999] | [nan, 0.9366846987126999] |
| 0.0102        | 5.07  | 380  | 0.0152          | 0.4758   | 0.9517        | 0.9517           | [0.0, 0.9516509773164403] | [nan, 0.9516509773164403] |
| 0.0101        | 5.33  | 400  | 0.0169          | 0.4884   | 0.9768        | 0.9768           | [0.0, 0.9768393358121804] | [nan, 0.9768393358121804] |
| 0.0082        | 5.6   | 420  | 0.0150          | 0.4761   | 0.9522        | 0.9522           | [0.0, 0.9522462074215836] | [nan, 0.9522462074215836] |
| 0.01          | 5.87  | 440  | 0.0152          | 0.4788   | 0.9576        | 0.9576           | [0.0, 0.9575745140264517] | [nan, 0.9575745140264517] |
| 0.0098        | 6.13  | 460  | 0.0148          | 0.4783   | 0.9565        | 0.9565           | [0.0, 0.9565489693736469] | [nan, 0.9565489693736469] |
| 0.0088        | 6.4   | 480  | 0.0153          | 0.4795   | 0.9591        | 0.9591           | [0.0, 0.959051850601846]  | [nan, 0.959051850601846]  |
| 0.0091        | 6.67  | 500  | 0.0152          | 0.4828   | 0.9656        | 0.9656           | [0.0, 0.965590177169167]  | [nan, 0.965590177169167]  |
| 0.0102        | 6.93  | 520  | 0.0149          | 0.4800   | 0.9599        | 0.9599           | [0.0, 0.9599216842864238] | [nan, 0.9599216842864238] |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.0.1
- Datasets 2.16.1
- Tokenizers 0.15.1