---
license: other
base_model: nvidia/mit-b0
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b0-finetuned-segments-SixrayKnife8-21-2024_saad5
  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-SixrayKnife8-21-2024_saad5

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: 0.2923
- Mean Iou: 0.8066
- Mean Accuracy: 0.9078
- Overall Accuracy: 0.9857
- Accuracy Bkg: 0.9917
- Accuracy Knife: 0.8251
- Accuracy Gun: 0.9065
- Iou Bkg: 0.9869
- Iou Knife: 0.7133
- Iou Gun: 0.7196

## 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: 20
- eval_batch_size: 20
- 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 Bkg | Accuracy Knife | Accuracy Gun | Iou Bkg | Iou Knife | Iou Gun |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------:|:--------------:|:------------:|:-------:|:---------:|:-------:|
| 0.8263        | 5.0   | 20   | 0.9552          | 0.5735   | 0.8760        | 0.9414           | 0.9464       | 0.8112         | 0.8703       | 0.9424  | 0.4242    | 0.3540  |
| 0.6154        | 10.0  | 40   | 0.6184          | 0.6297   | 0.7711        | 0.9652           | 0.9800       | 0.6061         | 0.7272       | 0.9657  | 0.4854    | 0.4379  |
| 0.5165        | 15.0  | 60   | 0.5098          | 0.6805   | 0.8233        | 0.9714           | 0.9826       | 0.7375         | 0.7498       | 0.9720  | 0.5691    | 0.5003  |
| 0.4503        | 20.0  | 80   | 0.4561          | 0.7103   | 0.8818        | 0.9735           | 0.9805       | 0.7960         | 0.8690       | 0.9742  | 0.5898    | 0.5670  |
| 0.4154        | 25.0  | 100  | 0.3958          | 0.7526   | 0.8997        | 0.9791           | 0.9852       | 0.8206         | 0.8934       | 0.9800  | 0.6237    | 0.6540  |
| 0.3659        | 30.0  | 120  | 0.3529          | 0.7810   | 0.8969        | 0.9832           | 0.9899       | 0.7932         | 0.9076       | 0.9844  | 0.6814    | 0.6773  |
| 0.3616        | 35.0  | 140  | 0.3253          | 0.7949   | 0.8937        | 0.9848           | 0.9918       | 0.8004         | 0.8889       | 0.9858  | 0.6954    | 0.7035  |
| 0.3666        | 40.0  | 160  | 0.3110          | 0.8018   | 0.9085        | 0.9852           | 0.9911       | 0.8255         | 0.9087       | 0.9863  | 0.7079    | 0.7112  |
| 0.3082        | 45.0  | 180  | 0.2983          | 0.8011   | 0.9037        | 0.9852           | 0.9914       | 0.8195         | 0.9002       | 0.9863  | 0.6982    | 0.7189  |
| 0.3097        | 50.0  | 200  | 0.2923          | 0.8066   | 0.9078        | 0.9857           | 0.9917       | 0.8251         | 0.9065       | 0.9869  | 0.7133    | 0.7196  |


### Framework versions

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