---
license: other
base_model: nvidia/mit-b0
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformerSAAD
  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. -->

# segformerSAAD

This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the saad7489/SixGUN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7611
- Mean Iou: 0.5823
- Mean Accuracy: 0.8994
- Overall Accuracy: 0.9474
- Accuracy Bkg: 0.9505
- Accuracy Knife: 0.8767
- Accuracy Gun: 0.8711
- Iou Bkg: 0.9471
- Iou Knife: 0.4452
- Iou Gun: 0.3544

## 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: 30

### 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------:|:--------------:|:------------:|:-------:|:---------:|:-------:|
| 0.9115        | 10.0  | 20   | 1.0252          | 0.5076   | 0.9065        | 0.9181           | 0.9188       | 0.8625         | 0.9382       | 0.9176  | 0.3218    | 0.2833  |
| 0.766         | 20.0  | 40   | 0.8278          | 0.5811   | 0.8914        | 0.9486           | 0.9523       | 0.8691         | 0.8527       | 0.9485  | 0.4288    | 0.3661  |
| 0.7862        | 30.0  | 60   | 0.7611          | 0.5823   | 0.8994        | 0.9474           | 0.9505       | 0.8767         | 0.8711       | 0.9471  | 0.4452    | 0.3544  |


### Framework versions

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