YOLO Nano implementation with Pytorch
This is my implementation of YOLO Nano with Pytorch.
If you like it, please star it.
Introduction
YOLO Nano paper: YOLO Nano: a Highly Compact You Only Look Once Convolutional Neural Network for Object Detection
The network structure of YOLO Nano:
The performance of YOLO Nano:
How to run training code
- Make sure you have powerfull GPU computer and you are good at Python coding.
- $ git clone git@github.com:ardeal/yolo_nano.git
- Install Pytorch and necessary packages
- Prepare validating images. Training and validating image and label files are all specified in file config/coco.data.
- Customize opt.py file according to your environment
- Run train_yolonano.py
How to run this predicting code
- Prepare validating images and corresponding label files. In this code base, the example image and label files are downloaded from COCO.
- Make sure the pth file path in test_yolonano.py code is correct.
- The evaluate function in test_yolonano.py file is the main code of validating. Uncomment out code in evaluate function in test_yolonano.py file to show the image and corresponding algorithms result.
The performance of the trained model based on my code
will be here soon......
About the pre-trained model
(which is being trained at present, and it will be ready in a few weeks.) The size of the model is too big to be shared on github directly. If you are interested in the pre-trained model, please join the QQ group
More information
- The code will be further optimized.
- Star the code you like is the best respect to the author.
- Please ask your questions on the Issues tab on this page or in the QQ group:
