---
license: apache-2.0
base_model: hustvl/yolos-tiny
tags:
- generated_from_trainer
- NFL
- Sports
- Helmets
datasets:
- nfl-object-detection
model-index:
- name: yolos-tiny-NFL_Object_Detection
  results: []
language:
- en
pipeline_tag: object-detection
---

# *** This model is not completely trained!!! *** #

<hr/>

## This model requires more training than what the resouces I have can offer!!! #


# yolos-tiny-NFL_Object_Detection

This model is a fine-tuned version of [hustvl/yolos-tiny](https://huggingface.co/hustvl/yolos-tiny) on the nfl-object-detection dataset.

## Model description

For more information on how it was created, check out the following link: https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/tree/main/Computer%20Vision/Object%20Detection/Trained%2C%20But%20to%20Standard/NFL%20Object%20Detection/Successful%20Attempt

* Fine-tuning and evaluation of this model are in separate files.

** If you plan on fine-tuning an Object Detection model on the NFL Helmet detection dataset, I would 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/keremberke/nfl-object-detection

## 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: 18

### Training results
 
| Metric Name | IoU | Area | maxDets | Metric Value |
|:-----:|:-----:|:-----:|:-----:|:-----:|
| Average Precision  (AP) | IoU=0.50:0.95 | area=   all | maxDets=100 | 0.003 |
| Average Precision  (AP) | IoU=0.50      | area=   all | maxDets=100 | 0.010 |
| Average Precision  (AP) | IoU=0.75      | area=   all | maxDets=100 | 0.000 |
| Average Precision  (AP) | IoU=0.50:0.95 | area= small | maxDets=100 | 0.002 |
| Average Precision  (AP) | IoU=0.50:0.95 | area=medium | maxDets=100 | 0.014 |
| Average Precision  (AP) | IoU=0.50:0.95 | area= large | maxDets=100 | 0.000 |
| Average Recall     (AR) | IoU=0.50:0.95 | area=   all | maxDets=  1 | 0.002 |
| Average Recall     (AR) | IoU=0.50:0.95 | area=   all | maxDets= 10 | 0.014 |
| Average Recall     (AR) | IoU=0.50:0.95 | area=   all | maxDets=100 | 0.029 |
| Average Recall     (AR) | IoU=0.50:0.95 | area= small | maxDets=100 | 0.026 |
| Average Recall     (AR) | IoU=0.50:0.95 | area=medium | maxDets=100 | 0.105 |
| Average Recall     (AR) | IoU=0.50:0.95 | area= large | maxDets=100 | 0.000 |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.1
- Tokenizers 0.13.3