---
base_model: nvidia/mit-b0
license: other
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: 0.9038
- Mean Iou: 0.4486
- Mean Accuracy: 0.8693
- Overall Accuracy: 0.8708
- Accuracy Bkg: 0.8709
- Accuracy Gun: 0.8680
- Accuracy Knife: 0.8689
- Iou Bkg: 0.8700
- Iou Gun: 0.1670
- Iou Knife: 0.3088

## 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 Bkg | Accuracy Gun | Accuracy Knife | Iou Bkg | Iou Gun | Iou Knife |
|:-------------:|:------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------:|:------------:|:--------------:|:-------:|:-------:|:---------:|
| 1.0919        | 1.4286 | 20   | 1.1033          | 0.1925   | 0.6509        | 0.3960           | 0.3783       | 0.8281       | 0.7462         | 0.3783  | 0.0314  | 0.1677    |
| 1.0576        | 2.8571 | 40   | 1.0473          | 0.3242   | 0.7738        | 0.6681           | 0.6608       | 0.8826       | 0.7781         | 0.6607  | 0.0600  | 0.2518    |
| 0.9844        | 4.2857 | 60   | 0.9913          | 0.3823   | 0.8265        | 0.7783           | 0.7750       | 0.8873       | 0.8171         | 0.7748  | 0.0928  | 0.2793    |
| 0.9604        | 5.7143 | 80   | 0.9385          | 0.4206   | 0.8552        | 0.8396           | 0.8386       | 0.8813       | 0.8457         | 0.8381  | 0.1334  | 0.2902    |
| 0.9418        | 7.1429 | 100  | 0.9073          | 0.4389   | 0.8651        | 0.8592           | 0.8588       | 0.8795       | 0.8570         | 0.8581  | 0.1520  | 0.3065    |
| 0.9029        | 8.5714 | 120  | 0.8989          | 0.4474   | 0.8672        | 0.8683           | 0.8684       | 0.8712       | 0.8620         | 0.8677  | 0.1621  | 0.3123    |
| 0.9277        | 10.0   | 140  | 0.9038          | 0.4486   | 0.8693        | 0.8708           | 0.8709       | 0.8680       | 0.8689         | 0.8700  | 0.1670  | 0.3088    |


### Framework versions

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