README.md

---
license: apache-2.0
base_model: hustvl/yolos-small
tags:
- generated_from_trainer
model-index:
- name: yolos-small-Cell_Tower_Detection
  results: []
datasets:
- Francesco/cell-towers
language:
- en
pipeline_tag: object-detection
---

# yolos-small-Cell_Tower_Detection

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/Cell%20Tower%20Object%20Detection/Cell%20Tower%20Detection%20YOLOS.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/cell-towers

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-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: 30

### Training results

| Metric Name | IoU | Area | maxDets | Metric Value |
|:-----:|:-----:|:-----:|:-----:|:-----:|
| Average Precision (AP) | IoU=0.50:0.95 | area=   all | maxDets=100 | 0.287 |
| Average Precision (AP) | IoU=0.50      | area=   all | maxDets=100 | 0.636 |
| Average Precision (AP) | IoU=0.75      | area=   all | maxDets=100 | 0.239 |
| Average Precision (AP) | IoU=0.50:0.95 | area= small | maxDets=100 | 0.069 |
| Average Precision (AP) | IoU=0.50:0.95 | area=medium | maxDets=100 | 0.289 |
| Average Precision (AP) | IoU=0.50:0.95 | area= large | maxDets=100 | 0.556 |
| Average Recall (AR) | IoU=0.50:0.95 | area=   all | maxDets=  1 | 0.192 |
| Average Recall (AR) | IoU=0.50:0.95 | area=   all | maxDets= 10 | 0.460 |
| Average Recall (AR) | IoU=0.50:0.95 | area=   all | maxDets=100 | 0.492 |
| Average Recall (AR) | IoU=0.50:0.95 | area= small | maxDets=100 | 0.151 |
| Average Recall (AR) | IoU=0.50:0.95 | area=medium | maxDets=100 | 0.488 |
| Average Recall (AR) | IoU=0.50:0.95 | area= large | maxDets=100 | 0.760 |

### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3