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https://github.com/fatescript/centernet-better
An easy to understand and better performance version of CenterNet
https://github.com/fatescript/centernet-better
computer-vision deep-learning object-detection
Last synced: 3 days ago
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An easy to understand and better performance version of CenterNet
- Host: GitHub
- URL: https://github.com/fatescript/centernet-better
- Owner: FateScript
- License: apache-2.0
- Created: 2020-02-23T07:30:28.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2021-04-12T03:42:16.000Z (over 3 years ago)
- Last Synced: 2024-12-29T10:12:32.471Z (10 days ago)
- Topics: computer-vision, deep-learning, object-detection
- Language: Python
- Size: 186 KB
- Stars: 549
- Watchers: 15
- Forks: 104
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# CenterNet
This repo is implemented based on my dl_lib, some parts of code in my dl_lib is based on [detectron2](https://github.com/facebookresearch/detectron2).
## Motivation
[Objects as Points](https://arxiv.org/abs/1904.07850) is one of my favorite paper in object detection area. However, its [code](https://github.com/xingyizhou/CenterNet/blob/master/README.md) is a little difficult to understand. I believe that CenterNet could get higher pts and implemented in a more elegant way, so I write this repo.
## Performance
This repo use less training time to get a better performance, it nearly spend half training time and get 1~2 pts higher mAP compared with the old repo. Here is the table of performance.
| Backbone | mAP | FPS | V100 FPS | trained model |
|:------------:|:-------:|:-------:|:---------:|:-----------------:|
|ResNet-18 | 29.8 | 92 | 113 | [google drive](https://drive.google.com/open?id=1D3tO95sdlsh9egOjOg0N-2HHmMfqbt5X) |
|ResNet-50 | 34.9 | 57 | 71 | [google drive](https://drive.google.com/open?id=1t5Bw520_fJrn3aeSVxDBYNIgwpNdLR5s) |
|ResNet-101 | 36.8 | 43 | 50 | [google drive](https://drive.google.com/open?id=1762Y93i9QreUTHq-87Ir73R2nNcrHuk0) |
## What\'s New?
* **treat config as a object.** You could run your config file and check the config value, which is really helpful for debug.
* **Common training / testing scripts in default.** you just need to invoke `dl_train/test --num-gpus x` in your playground and your projects only need to include all project-specific configs and network modules.
* **Performance report is dumped automaticly.** After your training is over, we will evaluate your model automatically and generate a markdown file.
* **Vectorize some operations.** This improves the speed and efficiency.
## What\'s comming
- [ ] Support DLA backbone
- [ ] Support Hourglass backbone
- [ ] Support KeyPoints dataset
## Get started
### Requirements
* Python >= 3.6
* PyTorch >= 1.3
* torchvision that matches the PyTorch installation.
* OpenCV
* pycocotools
```shell
pip install cython; pip install 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'
```
* GCC >= 4.9
```shell
gcc --version
```
### Installation
Make sure that your get at least one gpu when compiled. Run:
```shell
pip install -e .
```
### Training
For example, if you want to train CenterNet with resnet-18 backbone, run:
```shell
cd playground/centernet.res18.coco.512size
dl_train --num-gpus 8
```
After training process, a README.md file will be generated automatically and this file will report your model\'s performance.
NOTE: For ResNet-18 and ResNet-50 backbone, we suppose your machine has over 150GB Memory for training. If your memory is not enough, please change NUM_WORKER (in config.py) to a smaller value.
### Testing and Evaluation
```shell
dl_test --num-gpus 8
```
test downloaded model:
```shell
dl_test --num-gpus 8 MODEL.WEIGHTS path/to/your/save_dir/ckpt.pth
```
## Acknowledgement
* [detectron2](https://github.com/facebookresearch/detectron2)
* [CenterNet](https://github.com/xingyizhou/CenterNet)
## Coding style
please refer to [google python coding style](https://zh-google-styleguide.readthedocs.io/en/latest/google-python-styleguide/python_style_rules/)
## Citing CenterNet-better
If you use CenterNet-better in your research or wish to refer to the baseline results published in this repo, please use the following BibTeX entry.
```BibTeX
@misc{wang2020centernet_better,
author = {Feng Wang},
title = {CenterNet-better},
howpublished = {\url{https://github.com/FateScript/CenterNet-better}},
year = {2020}
}
```