---
license: gpl-3.0
datasets:
- detection-datasets/coco
language:
- en
pipeline_tag: object-detection
tags:
- object-detection
- yolo
- compressed
- aiminify
- pruning
- coco
---
# YOLOv5s Compressed with AIminify

## Overview

This repository provides a **compressed version of YOLOv5s** using [AIminify](https://www.aiminify.com). The original YOLOv5s model was pretrained on the [COCO dataset](https://cocodataset.org/) by Ultralytics. Leveraging AIminify’s pruning and fine-tuning strategies, we reduced the model size and FLOPS while maintaining strong performance on COCO benchmarks.

### Key features
- **Pruned** to remove unneeded parameters and reduce computational overhead.  
- **Fine-tuned** on the COCO dataset post-compression to restore or retain high accuracy.  
- **Minimal performance Loss** across various compression strengths, preserving mAP in most scenarios.

## Model architecture

The base architecture is **YOLOv5s** from Ultralytics. Modifications include:
- **Pruning** certain channels/kernels based on AIminify’s pruning algorithm.
- **Automatic Fine-Tuning** post-pruning to recover performance.

Despite its reduced size, this model maintains similar detection capabilities for common objects as the original YOLOv5s.

## Intended use

- **Primary use case**: General object detection (people, vehicles, animals, etc.) in images and videos.  
- **Industries**: Could be applied in retail (store analytics), security, robotics, autonomous vehicles, or any scenario where a fast, lightweight detector is beneficial.  
- **Resource-constrained environments**: Ideal for devices or deployments where GPU/CPU resources are limited or when high throughput is required.

### Limitations

- **Dataset bias**: Trained on COCO, which may not generalize to highly domain-specific use cases (e.g., medical imaging, satellite imagery). Additional domain-specific fine-tuning might be necessary.  
- **Performance variations**: Depending on the chosen compression strength, there might be a slight reduction in accuracy relative to the uncompressed YOLOv5s model.

## Metrics and Performance

Below is a summary of performance across different compression strengths on COCO’s mAP50-95 metric, FLOPS, number of parameters, and model size:

| Compression Strength | GFLOPS | Parameters   | Model Size (MB) | mAP50-95 |
|----------------------|--------|-------------|-----------------|----------|
| 0 (baseline)         | 24.0   | ~9.1M       | 36.8            | 0.412    |
| 1                    | 22.3   | ~8.6M       | 34.8            | 0.406    |
| 2                    | 21.1   | ~8.3M       | 33.3            | 0.400    |
| 3                    | 19.9   | ~7.9M       | 31.7            | 0.394    |
| 4                    | 18.6   | ~7.5M       | 30.1            | 0.377    |
| 5                    | 17.3   | ~7.0M       | 28.4            | 0.365    |

Even at the highest compression strength (`5`), the model **significantly outperforms** a smaller YOLOv5n baseline while being more resource-efficient than the original YOLOv5s.

## How to use
```python
import torch

if __name__ == '__main__':
    model_file = 'YOLOv5_compression_strength_5_unquantized.pt'
    model = torch.load(model_file, map_location='cpu')
    model.eval()

    inputs = torch.randn(1, 3, 640, 640)
    results = model(inputs)
```