yolos-small-Cell_Tower_Detection Open-source Model - Achieve precise and efficient cell tower detection functions

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Yolos Small Cell Tower Detection

Developed by DunnBC22

This model is a fine-tuned version of hustvl/yolos-small, specifically designed for detecting cell towers.

Object Detection

Transformers

EnglishOpen Source License:Apache-2.0 #Small object detection #Telecommunication infrastructure #YOLOS architecture

Downloads 21

Release Time : 8/19/2023

Model Overview

This model aims to demonstrate the capability of applying technology to solve complex problems, primarily for cell tower object detection tasks.

Model Features

Efficient Detection

Based on the YOLOS-small architecture, it efficiently detects cell tower objects in images.

Fine-Tuning Optimization

Fine-tuned on specific datasets to improve the accuracy of cell tower detection.

Model Capabilities

Object detection

Cell tower recognition

Use Cases

Infrastructure Monitoring

Cell Tower Detection

Used to detect cell towers in images, supporting the monitoring and management of infrastructure.

Average Precision (AP) IoU=0.50 is 0.636

🚀 yolos-small-Cell_Tower_Detection

This model is a fine - tuned version of hustvl/yolos-small, used for object detection of cell towers.

🚀 Quick Start

This model is a fine - tuned version of hustvl/yolos-small.

📚 Documentation

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

📄 License

The model is released under the Apache 2.0 license.

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