---
license: other
base_model: nvidia/mit-b0
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b0-finetuned-segments-SixrayKnife8-21-2024_saad1
  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_saad1

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: 0.1146
- Mean Iou: 0.8471
- Mean Accuracy: 0.9142
- Overall Accuracy: 0.9899
- Accuracy Bkg: 0.9952
- Accuracy Knife: 0.8462
- Accuracy Gun: 0.9012
- Iou Bkg: 0.9906
- Iou Knife: 0.7813
- Iou Gun: 0.7695

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------:|:--------------:|:------------:|:-------:|:---------:|:-------:|
| 0.2793        | 5.0   | 20   | 0.2864          | 0.8107   | 0.9046        | 0.9864           | 0.9921       | 0.8291         | 0.8925       | 0.9869  | 0.7282    | 0.7169  |
| 0.2448        | 10.0  | 40   | 0.2176          | 0.8159   | 0.9001        | 0.9871           | 0.9932       | 0.8241         | 0.8829       | 0.9876  | 0.7319    | 0.7284  |
| 0.2061        | 15.0  | 60   | 0.1960          | 0.8225   | 0.9093        | 0.9875           | 0.9930       | 0.8324         | 0.9024       | 0.9881  | 0.7476    | 0.7317  |
| 0.1731        | 20.0  | 80   | 0.1698          | 0.8291   | 0.8991        | 0.9884           | 0.9947       | 0.8120         | 0.8907       | 0.9890  | 0.7555    | 0.7428  |
| 0.1513        | 25.0  | 100  | 0.1435          | 0.8371   | 0.8993        | 0.9891           | 0.9954       | 0.8245         | 0.8780       | 0.9897  | 0.7643    | 0.7574  |
| 0.1401        | 30.0  | 120  | 0.1334          | 0.8400   | 0.9112        | 0.9893           | 0.9947       | 0.8399         | 0.8990       | 0.9899  | 0.7720    | 0.7582  |
| 0.1359        | 35.0  | 140  | 0.1222          | 0.8449   | 0.9050        | 0.9898           | 0.9957       | 0.8335         | 0.8859       | 0.9904  | 0.7753    | 0.7691  |
| 0.1498        | 40.0  | 160  | 0.1196          | 0.8460   | 0.9092        | 0.9898           | 0.9955       | 0.8367         | 0.8955       | 0.9905  | 0.7780    | 0.7696  |
| 0.1255        | 45.0  | 180  | 0.1160          | 0.8475   | 0.9109        | 0.9899           | 0.9955       | 0.8423         | 0.8948       | 0.9906  | 0.7810    | 0.7710  |
| 0.1247        | 50.0  | 200  | 0.1146          | 0.8471   | 0.9142        | 0.9899           | 0.9952       | 0.8462         | 0.9012       | 0.9906  | 0.7813    | 0.7695  |


### Framework versions

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