---
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: 0.1501
- Mean Iou: 0.4560
- Mean Accuracy: 0.9040
- Overall Accuracy: 0.9643
- Accuracy Unlabeled: nan
- Accuracy Wear: 0.8404
- Accuracy Tool: 0.9675
- Iou Unlabeled: 0.0
- Iou Wear: 0.4034
- Iou Tool: 0.9646

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-------------:|:-------------:|:-------------:|:--------:|:--------:|
| 0.4464        | 1.82  | 20   | 0.6527          | 0.3325   | 0.5116        | 0.9740           | nan                | 0.0242        | 0.9990        | 0.0           | 0.0235   | 0.9740   |
| 0.3069        | 3.64  | 40   | 0.3300          | 0.4958   | 0.8505        | 0.9661           | nan                | 0.7288        | 0.9723        | 0.0           | 0.5213   | 0.9662   |
| 0.276         | 5.45  | 60   | 0.2597          | 0.4089   | 0.9324        | 0.9368           | nan                | 0.9278        | 0.9370        | 0.0           | 0.2909   | 0.9358   |
| 0.2648        | 7.27  | 80   | 0.2321          | 0.4338   | 0.8839        | 0.9567           | nan                | 0.8071        | 0.9607        | 0.0           | 0.3441   | 0.9572   |
| 0.245         | 9.09  | 100  | 0.2298          | 0.4021   | 0.9265        | 0.9359           | nan                | 0.9167        | 0.9364        | 0.0           | 0.2715   | 0.9348   |
| 0.2047        | 10.91 | 120  | 0.1897          | 0.4379   | 0.8814        | 0.9446           | nan                | 0.8147        | 0.9480        | 0.0           | 0.3684   | 0.9455   |
| 0.1695        | 12.73 | 140  | 0.1681          | 0.4561   | 0.8444        | 0.9636           | nan                | 0.7188        | 0.9701        | 0.0           | 0.4026   | 0.9657   |
| 0.1556        | 14.55 | 160  | 0.1741          | 0.4289   | 0.9060        | 0.9494           | nan                | 0.8603        | 0.9517        | 0.0           | 0.3372   | 0.9497   |
| 0.1435        | 16.36 | 180  | 0.1528          | 0.4746   | 0.8851        | 0.9679           | nan                | 0.7978        | 0.9723        | 0.0           | 0.4549   | 0.9689   |
| 0.1208        | 18.18 | 200  | 0.1648          | 0.4379   | 0.9126        | 0.9577           | nan                | 0.8650        | 0.9601        | 0.0           | 0.3560   | 0.9577   |
| 0.1425        | 20.0  | 220  | 0.1587          | 0.4451   | 0.9116        | 0.9576           | nan                | 0.8631        | 0.9601        | 0.0           | 0.3774   | 0.9578   |
| 0.1124        | 21.82 | 240  | 0.1515          | 0.4291   | 0.9044        | 0.9491           | nan                | 0.8574        | 0.9515        | 0.0           | 0.3380   | 0.9493   |
| 0.1509        | 23.64 | 260  | 0.1501          | 0.4560   | 0.9040        | 0.9643           | nan                | 0.8404        | 0.9675        | 0.0           | 0.4034   | 0.9646   |


### Framework versions

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