https://github.com/dataxujing/cornernet-lite-pytorch
:rotating_light::rotating_light::rotating_light: CornerNet:基于虚拟仿真环境下的自动驾驶交通标志识别
https://github.com/dataxujing/cornernet-lite-pytorch
anchor-free cornernet cornernet-lite cv detection python3 pytorch
Last synced: 2 months ago
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:rotating_light::rotating_light::rotating_light: CornerNet:基于虚拟仿真环境下的自动驾驶交通标志识别
- Host: GitHub
- URL: https://github.com/dataxujing/cornernet-lite-pytorch
- Owner: DataXujing
- License: bsd-3-clause
- Created: 2019-12-01T16:43:48.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2019-12-24T03:42:10.000Z (almost 6 years ago)
- Last Synced: 2024-07-31T21:53:48.530Z (about 1 year ago)
- Topics: anchor-free, cornernet, cornernet-lite, cv, detection, python3, pytorch
- Language: Python
- Homepage:
- Size: 11.6 MB
- Stars: 38
- Watchers: 4
- Forks: 15
- Open Issues: 3
-
Metadata Files:
- Readme: README-CornerNet-lite.md
- License: LICENSE
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README
# CornerNet-Lite: Training, Evaluation and Testing Code
Code for reproducing results in the following paper:
[**CornerNet-Lite: Efficient Keypoint Based Object Detection**](https://arxiv.org/abs/1904.08900)
Hei Law, Yun Teng, Olga Russakovsky, Jia Deng
*arXiv:1904.08900*
## Getting Started
### Software Requirement
- Python 3.7
- PyTorch 1.0.0
- CUDA 10
- GCC 4.9.2 or above
### Installing Dependencies
Please first install [Anaconda](https://anaconda.org) and create an Anaconda environment using the provided package list `conda_packagelist.txt`.
```
conda create --name CornerNet_Lite --file conda_packagelist.txt --channel pytorch
```
After you create the environment, please activate it.
```
source activate CornerNet_Lite
```
### Compiling Corner Pooling Layers
Compile the C++ implementation of the corner pooling layers. (GCC4.9.2 or above is required.)
```
cd /core/models/py_utils/_cpools/
python setup.py install --user
```
### Compiling NMS
Compile the NMS code which are originally from [Faster R-CNN](https://github.com/rbgirshick/py-faster-rcnn/blob/master/lib/nms/cpu_nms.pyx) and [Soft-NMS](https://github.com/bharatsingh430/soft-nms/blob/master/lib/nms/cpu_nms.pyx).
```
cd /core/external
make
```
### Downloading Models
In this repo, we provide models for the following detectors:
- [CornerNet-Saccade](https://drive.google.com/file/d/1MQDyPRI0HgDHxHToudHqQ-2m8TVBciaa/view?usp=sharing)
- [CornerNet-Squeeze](https://drive.google.com/file/d/1qM8BBYCLUBcZx_UmLT0qMXNTh-Yshp4X/view?usp=sharing)
- [CornerNet](https://drive.google.com/file/d/1e8At_iZWyXQgLlMwHkB83kN-AN85Uff1/view?usp=sharing)
Put the CornerNet-Saccade model under `/cache/nnet/CornerNet_Saccade/`, CornerNet-Squeeze model under `/cache/nnet/CornerNet_Squeeze/` and CornerNet model under `/cache/nnet/CornerNet/`. (\* Note we use underscore instead of dash in both the directory names for CornerNet-Saccade and CornerNet-Squeeze.)
Note: The CornerNet model is the same as the one in the original [CornerNet repo](https://github.com/princeton-vl/CornerNet). We just ported it to this new repo.
### Running the Demo Script
After downloading the models, you should be able to use the detectors on your own images. We provide a demo script `demo.py` to test if the repo is installed correctly.
```
python demo.py
```
This script applies CornerNet-Saccade to `demo.jpg` and writes the results to `demo_out.jpg`.
In the demo script, the default detector is CornerNet-Saccade. You can modify the demo script to test different detectors. For example, if you want to test CornerNet-Squeeze:
```python
#!/usr/bin/env python
import cv2
from core.detectors import CornerNet_Squeeze
from core.vis_utils import draw_bboxes
detector = CornerNet_Squeeze()
image = cv2.imread("demo.jpg")
bboxes = detector(image)
image = draw_bboxes(image, bboxes)
cv2.imwrite("demo_out.jpg", image)
```
### Using CornerNet-Lite in Your Project
It is also easy to use CornerNet-Lite in your project. You will need to change the directory name from `CornerNet-Lite` to `CornerNet_Lite`. Otherwise, you won't be able to import CornerNet-Lite.
```
Your project
│ README.md
│ ...
│ foo.py
│
└───CornerNet_Lite
│
└───directory1
│
└───...
```
In `foo.py`, you can easily import CornerNet-Saccade by adding:
```python
from CornerNet_Lite import CornerNet_Saccade
def foo():
cornernet = CornerNet_Saccade()
# CornerNet_Saccade is ready to use
image = cv2.imread('/path/to/your/image')
bboxes = cornernet(image)
```
If you want to train or evaluate the detectors on COCO, please move on to the following steps.
## Training and Evaluation
### Installing MS COCO APIs
```
mkdir -p /data
cd /data
git clone git@github.com:cocodataset/cocoapi.git coco
cd /data/coco/PythonAPI
make install
```
### Downloading MS COCO Data
- Download the training/validation split we use in our paper from [here](https://drive.google.com/file/d/1dop4188xo5lXDkGtOZUzy2SHOD_COXz4/view?usp=sharing) (originally from [Faster R-CNN](https://github.com/rbgirshick/py-faster-rcnn/tree/master/data))
- Unzip the file and place `annotations` under `/data/coco`
- Download the images (2014 Train, 2014 Val, 2017 Test) from [here](http://cocodataset.org/#download)
- Create 3 directories, `trainval2014`, `minival2014` and `testdev2017`, under `/data/coco/images/`
- Copy the training/validation/testing images to the corresponding directories according to the annotation files
To train and evaluate a network, you will need to create a configuration file, which defines the hyperparameters, and a model file, which defines the network architecture. The configuration file should be in JSON format and placed in `/configs/`. Each configuration file should have a corresponding model file in `/core/models/`. i.e. If there is a `.json` in `/configs/`, there should be a `.py` in `/core/models/`. There is only one exception which we will mention later.
### Training and Evaluating a Model
To train a model:
```
python train.py
```
We provide the configuration files and the model files for CornerNet-Saccade, CornerNet-Squeeze and CornerNet in this repo. Please check the configuration files in `/configs/`.
To train CornerNet-Saccade:
```
python train.py CornerNet_Saccade
```
Please adjust the batch size in `CornerNet_Saccade.json` to accommodate the number of GPUs that are available to you.
To evaluate the trained model:
```
python evaluate.py CornerNet_Saccade --testiter 500000 --split
```
If you want to test different hyperparameters during evaluation and do not want to overwrite the original configuration file, you can do so by creating a configuration file with a suffix (`-.json`). There is no need to create `-.py` in `/core/models/`.
To use the new configuration file:
```
python evaluate.py --testiter --split --suffix
```
We also include a configuration file for CornerNet under multi-scale setting, which is `CornerNet-multi_scale.json`, in this repo.
To use the multi-scale configuration file:
```
python evaluate.py CornerNet --testiter --split --suffix multi_scale