---
license: apache-2.0
base_model: hustvl/yolos-small
tags:
- generated_from_trainer
- medical
model-index:
- name: yolos-small-Axial_MRIs
  results: []
datasets:
- Francesco/axial-mri
language:
- en
pipeline_tag: object-detection
---

# yolos-small-Axial_MRIs

This model is a fine-tuned version of [hustvl/yolos-small](https://huggingface.co/hustvl/yolos-small).

## Model description

For more information on how it was created, check out the following link: https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Computer%20Vision/Object%20Detection/Axial%20MRIs/Axial_MRIs_Object_Detection_YOLOS.ipynb

## Intended uses & limitations

This model is intended to demonstrate my ability to solve a complex problem using technology.

## Training and evaluation data

Dataset Source: https://huggingface.co/datasets/Francesco/axial-mri

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25

### Training results

| Metric Name | IoU | Area| maxDets | Metric Value |
|:-----:|:-----:|:-----:|:-----:|:-----:|
| Average Precision (AP) | IoU=0.50:0.95 | all | maxDets=100 | 0.284 |
| Average Precision (AP) | IoU=0.50 | all | maxDets=100 | 0.451 |
| Average Precision (AP) | IoU=0.75 | all | maxDets=100 | 0.351 |
| Average Precision (AP) | IoU=0.50:0.95 | small | maxDets=100 | 0.000 |
| Average Precision (AP) | IoU=0.50:0.95 | medium | maxDets=100 | 0.182 |
| Average Precision (AP) | IoU=0.50:0.95 | large | maxDets=100 | 0.663 |
| Average Recall (AR) | IoU=0.50:0.95 | all | maxDets=1 | 0.388 |
| Average Recall (AR) | IoU=0.50:0.95 | all | maxDets=10 | 0.524 |
| Average Recall (AR) | IoU=0.50:0.95 | all | maxDets=100 | 0.566 |
| Average Recall (AR) | IoU=0.50:0.95 | small | maxDets=100 | 0.000 |
| Average Recall (AR) | IoU=0.50:0.95 | medium | maxDets=100 | 0.502 |
| Average Recall (AR) | IoU=0.50:0.95 | large | maxDets=100 | 0.791 |

### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3