---
license: apache-2.0
---
<img src="https://cdn-uploads.huggingface.co/production/uploads/64c1fef5b9d81735a12c3fcc/sebNQgVO1hUapijWvVwTl.jpeg" width=600>

# YOLOv5: Target Detection

Yolov5 is a one-stage structure target detection network framework, in which the main structure consists of 4 parts, including the network backbone composed of modified CSPNet, the high-resolution feature fusion module composed of FPN (Feature Paramid Network), composed of SPP (Spatial Pyramid Pooling) constitutes a pooling module, and three different detection heads are used to detect targets of different sizes.

The YOLOv5 model can be found [here](https://github.com/ultralytics/yolov5)

## CONTENTS
  - [Performance](#performance)
  - [Model Conversion](#model-conversion)
  - [Inference](#inference)

## Performance

|Device|SoC|Runtime|Model|Size (pixels)|Inference Time (ms)|Precision|Compute Unit|Model Download|
|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|
|AidBox QCS6490|QCS6490|QNN|YOLOv5s(cutoff)|640|6.7|INT8|NPU|[model download](https://huggingface.co/aidlux/YOLOv5/blob/main/Models/QCS6490/cutoff_yolov5s_int8.qnn.serialized.bin)|
|AidBox QCS6490|QCS6490|QNN|YOLOv5s(cutoff)|640|15.2|INT16|NPU|[model download](https://huggingface.co/aidlux/YOLOv5/blob/main/Models/QCS6490/cutoff_yolov5s_int16.qnn.serialized.bin)|
|AidBox QCS6490|QCS6490|SNPE|YOLOv5s(cutoff)|640|5.5|INT8|NPU|[model download](https://huggingface.co/aidlux/YOLOv5/blob/main/Models/QCS6490/cutoff_yolov5s_int8_htp_snpe2.dlc)|
|AidBox QCS6490|QCS6490|SNPE|YOLOv5s(cutoff)|640|13.4|INT16|NPU|[model download](https://huggingface.co/aidlux/YOLOv5/blob/main/Models/QCS6490/cutoff_yolov5s_int16_htp_snpe2.dlc)|
|APLUX QCS8550|QCS8550|QNN|YOLOv5s(cutoff)|640|4.1|INT8|NPU|[model download](https://huggingface.co/aidlux/YOLOv5/blob/main/Models/QCS8550/cutoff_yolov5s_640_int8.qnn.serialized.bin)|
|APLUX QCS8550|QCS8550|QNN|YOLOv5s(cutoff)|640|13.4|INT16|NPU|[model download](https://huggingface.co/aidlux/YOLOv5/blob/main/Models/QCS8550/cutoff_yolov5s_640_int16.qnn.serialized.bin)|
|APLUX QCS8550|QCS8550|SNPE|YOLOv5s(cutoff)|640|2.3|INT8|NPU|[model download](https://huggingface.co/aidlux/YOLOv5/blob/main/Models/QCS8550/cutoff_yolov5s_int8_htp_snpe2.dlc)|
|APLUX QCS8550|QCS8550|SNPE|YOLOv5s(cutoff)|640|5.8|INT16|NPU|[model download](https://huggingface.co/aidlux/YOLOv5/blob/main/Models/QCS8550/cutoff_yolov5s_int16_htp_snpe2.dlc)|
|AidBox GS865|QCS8250|SNPE|YOLOv5s(cutoff)|640|21|INT8|NPU|[model download]()|

## Model Conversion

Demo models converted from [**AIMO(AI Model Optimizier)**](https://aidlux.com/en/product/aimo).

The source model **YOLOv5s.onnx** can be found [here](https://huggingface.co/aplux/YOLOv5/blob/main/yolov5s.onnx).

The demo model conversion step on AIMO can be found blow:

|Device|SoC|Runtime|Model|Size (pixels)|Precision|Compute Unit|AIMO Conversion Steps|
|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|
|AidBox QCS6490|QCS6490|QNN|YOLOv5s(cutoff)|640|INT8|NPU|[View Steps](https://huggingface.co/aplux/YOLOv5/blob/main/AIMO/QCS6490/aimo_yolov5s_qnn_int8.png)|
|AidBox QCS6490|QCS6490|QNN|YOLOv5s(cutoff)|640|INT16|NPU|[View Steps](https://huggingface.co/aplux/YOLOv5/blob/main/AIMO/QCS6490/aimo_yolov5s_qnn_int16.png)|
|AidBox QCS6490|QCS6490|SNPE|YOLOv5s(cutoff)|640|INT8|NPU|[View Steps](https://huggingface.co/aplux/YOLOv5/blob/main/AIMO/QCS6490/aimo_yolov5s_snpe_int8.png)|
|AidBox QCS6490|QCS6490|SNPE|YOLOv5s(cutoff)|640|INT16|NPU|[View Steps](https://huggingface.co/aplux/YOLOv5/blob/main/AIMO/QCS6490/aimo_yolov5s_snpe_int16.png)|
|APLUX QCS8550|QCS8550|QNN|YOLOv5s(cutoff)|640|INT8|NPU|[View Steps](https://huggingface.co/aplux/YOLOv5/blob/main/AIMO/QCS8550/aimo_yolov5s_qnn_int8.png)|
|APLUX QCS8550|QCS8550|QNN|YOLOv5s(cutoff)|640|INT16|NPU|[View Steps](https://huggingface.co/aplux/YOLOv5/blob/main/AIMO/QCS8550/aimo_yolov5s_qnn_int16.png)|
|APLUX QCS8550|QCS8550|SNPE|YOLOv5s(cutoff)|640|INT8|NPU|[View Steps](https://huggingface.co/aplux/YOLOv5/blob/main/AIMO/QCS8550/aimo_yolov5s_snpe_int8.png)|
|APLUX QCS8550|QCS8550|SNPE|YOLOv5s(cutoff)|640|INT16|NPU|[View Steps](https://huggingface.co/aplux/YOLOv5/blob/main/AIMO/QCS8550/aimo_yolov5s_snpe_int16.png)|
|AidBox GS865|QCS8250|SNPE|YOLOv5s(cutoff)|640|INT8|NPU|[View Steps]()|

## Inference

### Step1: convert model

a. Prepare source model in onnx format. The source model can be found [here](https://huggingface.co/aplux/YOLOv5/blob/main/yolov5s.onnx).

b. Login AIMO and convert source model to target format. The model conversion step can follow **AIMO Conversion Step** in [Model Conversion sheet](#model-conversion).

c. After conversion task done, download target model file.

### Step2: install AidLite SDK

The installation guide of AidLite SDK can be found [here](https://huggingface.co/datasets/aplux/AIToolKit/blob/main/AidLite%20SDK%20Development%20Documents.md#installation).

### Step3: run demo program