---
license: other
base_model: nvidia/mit-b0
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b0-finetuned-segments-SixrayKnife8-20-2024
  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-SixrayKnife8-20-2024

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: 0.2632
- Mean Iou: 0.7518
- Mean Accuracy: 0.8442
- Overall Accuracy: 0.9846
- Accuracy Bkg: 0.9934
- Accuracy Knife: 0.6638
- Accuracy Gun: 0.8755
- Iou Bkg: 0.9864
- Iou Knife: 0.5722
- Iou Gun: 0.6969

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------:|:--------------:|:------------:|:-------:|:---------:|:-------:|
| 0.7462        | 5.0   | 20   | 0.8680          | 0.5725   | 0.7955        | 0.9552           | 0.9653       | 0.6150         | 0.8064       | 0.9557  | 0.3394    | 0.4223  |
| 0.5675        | 10.0  | 40   | 0.5259          | 0.5797   | 0.6730        | 0.9685           | 0.9873       | 0.3829         | 0.6486       | 0.9690  | 0.3247    | 0.4455  |
| 0.5079        | 15.0  | 60   | 0.4394          | 0.6394   | 0.7578        | 0.9723           | 0.9859       | 0.5491         | 0.7385       | 0.9731  | 0.4658    | 0.4794  |
| 0.3976        | 20.0  | 80   | 0.3820          | 0.6781   | 0.7446        | 0.9792           | 0.9942       | 0.5443         | 0.6952       | 0.9802  | 0.4938    | 0.5601  |
| 0.3527        | 25.0  | 100  | 0.3454          | 0.7173   | 0.8050        | 0.9816           | 0.9928       | 0.6128         | 0.8094       | 0.9829  | 0.5373    | 0.6318  |
| 0.3571        | 30.0  | 120  | 0.3192          | 0.7336   | 0.8386        | 0.9826           | 0.9917       | 0.6508         | 0.8734       | 0.9843  | 0.5518    | 0.6646  |
| 0.3201        | 35.0  | 140  | 0.2858          | 0.7399   | 0.8390        | 0.9834           | 0.9924       | 0.6540         | 0.8706       | 0.9851  | 0.5637    | 0.6709  |
| 0.3205        | 40.0  | 160  | 0.2774          | 0.7482   | 0.8301        | 0.9846           | 0.9944       | 0.6447         | 0.8512       | 0.9864  | 0.5673    | 0.6911  |
| 0.2899        | 45.0  | 180  | 0.2677          | 0.7497   | 0.8399        | 0.9845           | 0.9937       | 0.6581         | 0.8679       | 0.9864  | 0.5679    | 0.6948  |
| 0.2672        | 50.0  | 200  | 0.2632          | 0.7518   | 0.8442        | 0.9846           | 0.9934       | 0.6638         | 0.8755       | 0.9864  | 0.5722    | 0.6969  |


### Framework versions

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