---
license: other
base_model: nvidia/mit-b0
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b0-finetuned-100by100PNG-50epochs
  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-b0-finetuned-100by100PNG-50epochs

This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the JCAI2000/100By100BranchPNG dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1488
- Mean Iou: 0.0
- Mean Accuracy: nan
- Overall Accuracy: nan
- Accuracy 0: nan
- Iou 0: 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 0 | Iou 0 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:----------:|:-----:|
| 0.5785        | 2.63  | 50   | 0.4624          | 0.0      | nan           | nan              | nan        | 0.0   |
| 0.3842        | 5.26  | 100  | 0.3092          | 0.0      | nan           | nan              | nan        | 0.0   |
| 0.3916        | 7.89  | 150  | 0.2549          | 0.0      | nan           | nan              | nan        | 0.0   |
| 0.2686        | 10.53 | 200  | 0.2442          | 0.0      | nan           | nan              | nan        | 0.0   |
| 0.163         | 13.16 | 250  | 0.2046          | 0.0      | nan           | nan              | nan        | 0.0   |
| 0.118         | 15.79 | 300  | 0.1911          | 0.0      | nan           | nan              | nan        | 0.0   |
| 0.1432        | 18.42 | 350  | 0.1718          | 0.0      | nan           | nan              | nan        | 0.0   |
| 0.1692        | 21.05 | 400  | 0.1877          | 0.0      | nan           | nan              | nan        | 0.0   |
| 0.1444        | 23.68 | 450  | 0.1637          | 0.0      | nan           | nan              | nan        | 0.0   |
| 0.084         | 26.32 | 500  | 0.1649          | 0.0      | nan           | nan              | nan        | 0.0   |
| 0.1204        | 28.95 | 550  | 0.1601          | 0.0      | nan           | nan              | nan        | 0.0   |
| 0.0816        | 31.58 | 600  | 0.1560          | 0.0      | nan           | nan              | nan        | 0.0   |
| 0.1041        | 34.21 | 650  | 0.1618          | 0.0      | nan           | nan              | nan        | 0.0   |
| 0.0906        | 36.84 | 700  | 0.1565          | 0.0      | nan           | nan              | nan        | 0.0   |
| 0.0644        | 39.47 | 750  | 0.1500          | 0.0      | nan           | nan              | nan        | 0.0   |
| 0.0774        | 42.11 | 800  | 0.1552          | 0.0      | nan           | nan              | nan        | 0.0   |
| 0.0687        | 44.74 | 850  | 0.1504          | 0.0      | nan           | nan              | nan        | 0.0   |
| 0.0931        | 47.37 | 900  | 0.1465          | 0.0      | nan           | nan              | nan        | 0.0   |
| 0.0852        | 50.0  | 950  | 0.1488          | 0.0      | nan           | nan              | nan        | 0.0   |


### Framework versions

- Transformers 4.33.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3