---
license: other
base_model: nvidia/mit-b0
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformerSAAD_26Aug_new1
  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_26Aug_new1

This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the saad7489/SixGUNNew dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2892
- eval_mean_iou: 0.7470
- eval_mean_accuracy: 0.8404
- eval_overall_accuracy: 0.9883
- eval_accuracy_BKG: 0.9937
- eval_accuracy_Knife: nan
- eval_accuracy_Gun: 0.6872
- eval_iou_BKG: 0.9882
- eval_iou_Knife: nan
- eval_iou_Gun: 0.5059
- eval_runtime: 3.6938
- eval_samples_per_second: 3.79
- eval_steps_per_second: 0.271
- epoch: 4.0
- step: 20

## 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: 5e-05
- train_batch_size: 30
- eval_batch_size: 30
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50

### Framework versions

- Transformers 4.42.4
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1