---
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: 0.1817
- Mean Iou: 0.8160
- Mean Accuracy: 0.8823
- Overall Accuracy: 0.9881
- Accuracy Bkg: 0.9954
- Accuracy Gun: 0.7759
- Accuracy Knife: 0.8755
- Iou Bkg: 0.9890
- Iou Gun: 0.7014
- Iou Knife: 0.7574

## 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 Gun | Accuracy Knife | Iou Bkg | Iou Gun | Iou Knife |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------:|:------------:|:--------------:|:-------:|:-------:|:---------:|
| 0.4406        | 5.0   | 20   | 0.4093          | 0.7210   | 0.7883        | 0.9804           | 0.9938       | 0.6719       | 0.6991         | 0.9807  | 0.5730  | 0.6092    |
| 0.3699        | 10.0  | 40   | 0.3327          | 0.7327   | 0.7880        | 0.9819           | 0.9954       | 0.6559       | 0.7128         | 0.9824  | 0.5724  | 0.6432    |
| 0.31          | 15.0  | 60   | 0.3035          | 0.7698   | 0.8614        | 0.9842           | 0.9926       | 0.7207       | 0.8709         | 0.9853  | 0.6217  | 0.7023    |
| 0.2852        | 20.0  | 80   | 0.2649          | 0.7817   | 0.8711        | 0.9850           | 0.9928       | 0.7453       | 0.8752         | 0.9860  | 0.6423  | 0.7168    |
| 0.2583        | 25.0  | 100  | 0.2329          | 0.7936   | 0.8693        | 0.9863           | 0.9943       | 0.7497       | 0.8639         | 0.9873  | 0.6628  | 0.7307    |
| 0.2521        | 30.0  | 120  | 0.2194          | 0.7975   | 0.8778        | 0.9867           | 0.9942       | 0.7530       | 0.8862         | 0.9879  | 0.6731  | 0.7316    |
| 0.2357        | 35.0  | 140  | 0.2044          | 0.8042   | 0.8804        | 0.9871           | 0.9944       | 0.7635       | 0.8833         | 0.9881  | 0.6789  | 0.7456    |
| 0.2198        | 40.0  | 160  | 0.1929          | 0.8126   | 0.8789        | 0.9878           | 0.9953       | 0.7685       | 0.8728         | 0.9888  | 0.6937  | 0.7552    |
| 0.1909        | 45.0  | 180  | 0.1837          | 0.8151   | 0.8810        | 0.9880           | 0.9954       | 0.7726       | 0.8750         | 0.9890  | 0.6997  | 0.7568    |
| 0.1908        | 50.0  | 200  | 0.1817          | 0.8160   | 0.8823        | 0.9881           | 0.9954       | 0.7759       | 0.8755         | 0.9890  | 0.7014  | 0.7574    |


### Framework versions

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