YOLOV5-SWIFT
This repository is based on ultralytics/yolov5. Seeed has modified it to make it more suitable for AIoT hardware devices launched by Seeed.
Model test results on the coco dataset
| Model | Input_size | FLOPs | Params | Size（M） | Map@0.5 | Map@.5:0.95 |
|---|---|---|---|---|---|---|
| yolov5n6-xiao | 192×192 | 0.07G | 0.4M | 0.5 | 0.28 | 0.15 |
Test speed in different situations
| Equipment | Computing backend | Input | specification | time(ms) |
|---|---|---|---|---|
| Intel | i5-9400@2.90GHz | 192×192 | pt | 15 |
| Himax6537 | 400MHz | 192×192 | tflite | 700 |
Quick start 
Install
[**Python>=3.7.0**](https://www.python.org/) is required with all [requirements.txt](https://github.com/Seeed-Studio/yolov5-swift/blob/master/requirements.txt) installed including [**PyTorch>=1.7**](https://pytorch.org/get-started/locally/): ```bash $ git clone https://github.com/Seeed-Studio/yolov5-swift $ cd YOLOv5-swift $ pip install -r requirements.txt ```Inference with detect.py
`detect.py` runs inference on a variety of sources, downloading models automatically from the [YOLOv5n6-xiao](https://github.com/ultralytics/yolov5/releases) and saving results to `runs/detect`. ```bash $ python detect.py --source 0 # webcam file.jpg # image file.mp4 # video path/ # directory path/*.jpg # glob 'https://youtu.be/NUsoVlDFqZg' # YouTube 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream ```Training
```bash $ python train.py --data coco.yaml --cfg yolov5n6-xiao.yaml --weights yolov5n6-xiao.pt --batch-size 128 ```Export TFlite
The trained pt format model can be exported to the int8 type tflite model by the following command ```bash $ python export.py --data coco.yaml --cfg yolov5n6-xiao.yaml --weights yolov5n6-xiao.pt --imgsz 192 --int8 ```