---
license: other
tags:
- vision
- image-segmentation
- 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 the HorcruxNo13/toolwear_segmentsai dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2940
- Mean Iou: 0.4104
- Mean Accuracy: 0.8207
- Overall Accuracy: 0.8207
- Accuracy Unlabeled: nan
- Accuracy Tool: nan
- Accuracy Wear: 0.8207
- Iou Unlabeled: 0.0
- Iou Tool: nan
- Iou Wear: 0.8207

## 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 Tool | Accuracy Wear | Iou Unlabeled | Iou Tool | Iou Wear |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-------------:|:-------------:|:-------------:|:--------:|:--------:|
| 0.843         | 1.82  | 20   | 0.8832          | 0.4637   | 0.9274        | 0.9274           | nan                | nan           | 0.9274        | 0.0           | nan      | 0.9274   |
| 0.6849        | 3.64  | 40   | 0.5914          | 0.4361   | 0.8722        | 0.8722           | nan                | nan           | 0.8722        | 0.0           | nan      | 0.8722   |
| 0.5085        | 5.45  | 60   | 0.5178          | 0.4628   | 0.9256        | 0.9256           | nan                | nan           | 0.9256        | 0.0           | nan      | 0.9256   |
| 0.4027        | 7.27  | 80   | 0.5099          | 0.4598   | 0.9195        | 0.9195           | nan                | nan           | 0.9195        | 0.0           | nan      | 0.9195   |
| 0.3536        | 9.09  | 100  | 0.4262          | 0.4365   | 0.8730        | 0.8730           | nan                | nan           | 0.8730        | 0.0           | nan      | 0.8730   |
| 0.3657        | 10.91 | 120  | 0.3891          | 0.4228   | 0.8457        | 0.8457           | nan                | nan           | 0.8457        | 0.0           | nan      | 0.8457   |
| 0.3234        | 12.73 | 140  | 0.4221          | 0.4377   | 0.8754        | 0.8754           | nan                | nan           | 0.8754        | 0.0           | nan      | 0.8754   |
| 0.2874        | 14.55 | 160  | 0.3355          | 0.4098   | 0.8197        | 0.8197           | nan                | nan           | 0.8197        | 0.0           | nan      | 0.8197   |
| 0.2335        | 16.36 | 180  | 0.3570          | 0.4266   | 0.8531        | 0.8531           | nan                | nan           | 0.8531        | 0.0           | nan      | 0.8531   |
| 0.2167        | 18.18 | 200  | 0.3238          | 0.4404   | 0.8808        | 0.8808           | nan                | nan           | 0.8808        | 0.0           | nan      | 0.8808   |
| 0.2201        | 20.0  | 220  | 0.3103          | 0.4185   | 0.8370        | 0.8370           | nan                | nan           | 0.8370        | 0.0           | nan      | 0.8370   |
| 0.205         | 21.82 | 240  | 0.2881          | 0.4115   | 0.8230        | 0.8230           | nan                | nan           | 0.8230        | 0.0           | nan      | 0.8230   |
| 0.241         | 23.64 | 260  | 0.2940          | 0.4104   | 0.8207        | 0.8207           | nan                | nan           | 0.8207        | 0.0           | nan      | 0.8207   |


### Framework versions

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