---
license: other
base_model: nvidia/mit-b0
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b0-finetuned-segments-SixrayKnife8-19-2024
  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-SixrayKnife8-19-2024

This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the saad7489/SixraygunTest dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5986
- Mean Iou: 0.5110
- Mean Accuracy: 0.8118
- Overall Accuracy: 0.8081
- Accuracy Object1: nan
- Accuracy Object2: 0.7529
- Accuracy Object3: 0.8707
- Iou Object1: 0.0
- Iou Object2: 0.7267
- Iou Object3: 0.8065

## 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: 1e-05
- train_batch_size: 5
- eval_batch_size: 5
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Object1 | Accuracy Object2 | Accuracy Object3 | Iou Object1 | Iou Object2 | Iou Object3 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:----------------:|:----------------:|:----------------:|:-----------:|:-----------:|:-----------:|
| 0.7244        | 1.4286 | 20   | 0.6924          | 0.4978   | 0.8311        | 0.8263           | nan              | 0.7536           | 0.9087           | 0.0         | 0.7153      | 0.7782      |
| 0.7251        | 2.8571 | 40   | 0.6470          | 0.5105   | 0.8256        | 0.8213           | nan              | 0.7572           | 0.8940           | 0.0         | 0.7246      | 0.8069      |
| 0.6558        | 4.2857 | 60   | 0.6246          | 0.5148   | 0.8243        | 0.8210           | nan              | 0.7714           | 0.8772           | 0.0         | 0.7350      | 0.8095      |
| 0.6463        | 5.7143 | 80   | 0.5954          | 0.5100   | 0.8126        | 0.8098           | nan              | 0.7680           | 0.8572           | 0.0         | 0.7295      | 0.8004      |
| 0.63          | 7.1429 | 100  | 0.5892          | 0.5082   | 0.8073        | 0.8043           | nan              | 0.7582           | 0.8564           | 0.0         | 0.7254      | 0.7991      |
| 0.5935        | 8.5714 | 120  | 0.5923          | 0.5134   | 0.8155        | 0.8127           | nan              | 0.7694           | 0.8616           | 0.0         | 0.7369      | 0.8031      |
| 0.6137        | 10.0   | 140  | 0.5986          | 0.5110   | 0.8118        | 0.8081           | nan              | 0.7529           | 0.8707           | 0.0         | 0.7267      | 0.8065      |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1