---
license: other
tags:
- generated_from_trainer
model-index:
- name: segformer-b0-finetuned-segments-toolwear
  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-segments-toolwear

This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co./nvidia/mit-b0) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1338
- Mean Iou: 0.4591
- Mean Accuracy: 0.7164
- Overall Accuracy: 0.9595
- Accuracy Unlabeled: nan
- Accuracy Wear: 0.4489
- Accuracy Tool: 0.9838
- Iou Unlabeled: 0.0
- Iou Wear: 0.4154
- Iou Tool: 0.9618

## 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: 25

### Training results

| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Wear | Accuracy Tool | Iou Unlabeled | Iou Wear | Iou Tool |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-------------:|:-------------:|:-------------:|:--------:|:--------:|
| 0.5488        | 1.82  | 20   | 0.7199          | 0.3293   | 0.5153        | 0.9405           | nan                | 0.0476        | 0.9830        | 0.0           | 0.0475   | 0.9404   |
| 0.5195        | 3.64  | 40   | 0.3507          | 0.3622   | 0.5634        | 0.9239           | nan                | 0.1667        | 0.9600        | 0.0           | 0.1629   | 0.9236   |
| 0.2738        | 5.45  | 60   | 0.2569          | 0.4662   | 0.7496        | 0.9435           | nan                | 0.5363        | 0.9629        | 0.0           | 0.4547   | 0.9438   |
| 0.2461        | 7.27  | 80   | 0.2220          | 0.4491   | 0.7057        | 0.9482           | nan                | 0.4389        | 0.9725        | 0.0           | 0.3982   | 0.9492   |
| 0.1999        | 9.09  | 100  | 0.1962          | 0.4492   | 0.7084        | 0.9597           | nan                | 0.4319        | 0.9848        | 0.0           | 0.3860   | 0.9616   |
| 0.2004        | 10.91 | 120  | 0.1890          | 0.4031   | 0.6239        | 0.9537           | nan                | 0.2610        | 0.9867        | 0.0           | 0.2539   | 0.9553   |
| 0.4753        | 12.73 | 140  | 0.1704          | 0.4360   | 0.6760        | 0.9494           | nan                | 0.3753        | 0.9768        | 0.0           | 0.3562   | 0.9518   |
| 0.1606        | 14.55 | 160  | 0.1579          | 0.4483   | 0.7028        | 0.9580           | nan                | 0.4222        | 0.9835        | 0.0           | 0.3822   | 0.9625   |
| 0.1388        | 16.36 | 180  | 0.1519          | 0.4829   | 0.7940        | 0.9565           | nan                | 0.6152        | 0.9728        | 0.0           | 0.4900   | 0.9586   |
| 0.138         | 18.18 | 200  | 0.1374          | 0.5120   | 0.8119        | 0.9643           | nan                | 0.6443        | 0.9795        | 0.0           | 0.5693   | 0.9668   |
| 0.1078        | 20.0  | 220  | 0.1400          | 0.4541   | 0.7066        | 0.9606           | nan                | 0.4271        | 0.9860        | 0.0           | 0.3985   | 0.9638   |
| 0.1426        | 21.82 | 240  | 0.1323          | 0.4530   | 0.7053        | 0.9581           | nan                | 0.4272        | 0.9834        | 0.0           | 0.3978   | 0.9611   |
| 0.3498        | 23.64 | 260  | 0.1338          | 0.4591   | 0.7164        | 0.9595           | nan                | 0.4489        | 0.9838        | 0.0           | 0.4154   | 0.9618   |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3