---
license: apache-2.0
base_model: hustvl/yolos-tiny
tags:
- generated_from_trainer
- medical
- science
model-index:
- name: yolos-tiny-Brain_Tumor_Detection
  results: []
datasets:
- Francesco/brain-tumor-m2pbp
language:
- en
pipeline_tag: object-detection
---

# yolos-tiny-Brain_Tumor_Detection

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

## 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/Brain%20Tumors/Brain_Tumor_m2pbp_Object_Detection_YOLOS.ipynb

**If you intend on trying this project yourself, I highly recommend using (at least) the yolos-small checkpoint.

## 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/brain-tumor-m2pbp

**Example**

![Example Image](https://raw.githubusercontent.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/main/Computer%20Vision/Object%20Detection/Brain%20Tumors/Images/Example.png)

## 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: 20

### Training results

| Metric Name | IoU | Area | maxDets | Metric Value |
|:-----:|:-----:|:-----:|:-----:|:-----:|
| Average Precision  (AP) | IoU=0.50:0.95 | area=   all | maxDets=100 | 0.185
| Average Precision  (AP) | IoU=0.50      | area=   all | maxDets=100 | 0.448
| Average Precision  (AP) | IoU=0.75      | area=   all | maxDets=100 | 0.126
| Average Precision  (AP) | IoU=0.50:0.95 | area= small | maxDets=100 | 0.001
| Average Precision  (AP) | IoU=0.50:0.95 | area=medium | maxDets=100 | 0.080
| Average Precision  (AP) | IoU=0.50:0.95 | area= large | maxDets=100 | 0.296
| Average Recall     (AR) | IoU=0.50:0.95 | area=   all | maxDets=  1 | 0.254
| Average Recall     (AR) | IoU=0.50:0.95 | area=   all | maxDets= 10 | 0.353
| Average Recall     (AR) | IoU=0.50:0.95 | area=   all | maxDets=100 | 0.407
| Average Recall     (AR) | IoU=0.50:0.95 | area= small | maxDets=100 | 0.036
| Average Recall     (AR) | IoU=0.50:0.95 | area=medium | maxDets=100 | 0.312
| Average Recall     (AR) |IoU=0.50:0.95 | area= large | maxDets=100 | 0.565

### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.2
- Tokenizers 0.13.3