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‎LICENSE‎

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BSD 3-Clause License
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Copyright (c) 2019, Princeton University
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All rights reserved.
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Redistribution and use in source and binary forms, with or without
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modification, are permitted provided that the following conditions are met:
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* Redistributions of source code must retain the above copyright notice, this
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list of conditions and the following disclaimer.
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* Redistributions in binary form must reproduce the above copyright notice,
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this list of conditions and the following disclaimer in the documentation
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and/or other materials provided with the distribution.
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* Neither the name of the copyright holder nor the names of its
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contributors may be used to endorse or promote products derived from
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this software without specific prior written permission.
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
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FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

‎README-CornerNet-lite.md‎

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# CornerNet-Lite: Training, Evaluation and Testing Code
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Code for reproducing results in the following paper:
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[**CornerNet-Lite: Efficient Keypoint Based Object Detection**](https://arxiv.org/abs/1904.08900)
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Hei Law, Yun Teng, Olga Russakovsky, Jia Deng
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*arXiv:1904.08900*
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## Getting Started
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### Software Requirement
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- Python 3.7
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- PyTorch 1.0.0
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- CUDA 10
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- GCC 4.9.2 or above
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### Installing Dependencies
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Please first install [Anaconda](https://anaconda.org) and create an Anaconda environment using the provided package list `conda_packagelist.txt`.
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```
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conda create --name CornerNet_Lite --file conda_packagelist.txt --channel pytorch
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```
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After you create the environment, please activate it.
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```
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source activate CornerNet_Lite
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```
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### Compiling Corner Pooling Layers
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Compile the C++ implementation of the corner pooling layers. (GCC4.9.2 or above is required.)
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```
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cd <CornerNet-Lite dir>/core/models/py_utils/_cpools/
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python setup.py install --user
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```
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### Compiling NMS
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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).
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```
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cd <CornerNet-Lite dir>/core/external
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make
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```
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### Downloading Models
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In this repo, we provide models for the following detectors:
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- [CornerNet-Saccade](https://drive.google.com/file/d/1MQDyPRI0HgDHxHToudHqQ-2m8TVBciaa/view?usp=sharing)
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- [CornerNet-Squeeze](https://drive.google.com/file/d/1qM8BBYCLUBcZx_UmLT0qMXNTh-Yshp4X/view?usp=sharing)
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- [CornerNet](https://drive.google.com/file/d/1e8At_iZWyXQgLlMwHkB83kN-AN85Uff1/view?usp=sharing)
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Put the CornerNet-Saccade model under `<CornerNet-Lite dir>/cache/nnet/CornerNet_Saccade/`, CornerNet-Squeeze model under `<CornerNet-Lite dir>/cache/nnet/CornerNet_Squeeze/` and CornerNet model under `<CornerNet-Lite dir>/cache/nnet/CornerNet/`. (\* Note we use underscore instead of dash in both the directory names for CornerNet-Saccade and CornerNet-Squeeze.)
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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.
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### Running the Demo Script
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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.
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```
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python demo.py
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```
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This script applies CornerNet-Saccade to `demo.jpg` and writes the results to `demo_out.jpg`.
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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:
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```python
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#!/usr/bin/env python
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import cv2
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from core.detectors import CornerNet_Squeeze
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from core.vis_utils import draw_bboxes
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detector = CornerNet_Squeeze()
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image = cv2.imread("demo.jpg")
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bboxes = detector(image)
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image = draw_bboxes(image, bboxes)
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cv2.imwrite("demo_out.jpg", image)
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```
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### Using CornerNet-Lite in Your Project
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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.
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```
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Your project
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│ README.md
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│ ...
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│ foo.py
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│
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└───CornerNet_Lite
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│
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└───directory1
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│
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└───...
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```
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In `foo.py`, you can easily import CornerNet-Saccade by adding:
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```python
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from CornerNet_Lite import CornerNet_Saccade
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def foo():
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cornernet = CornerNet_Saccade()
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# CornerNet_Saccade is ready to use
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image = cv2.imread('/path/to/your/image')
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bboxes = cornernet(image)
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```
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If you want to train or evaluate the detectors on COCO, please move on to the following steps.
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## Training and Evaluation
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### Installing MS COCO APIs
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```
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mkdir -p <CornerNet-Lite dir>/data
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cd <CornerNet-Lite dir>/data
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git clone git@github.com:cocodataset/cocoapi.git coco
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cd <CornerNet-Lite dir>/data/coco/PythonAPI
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make install
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```
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### Downloading MS COCO Data
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- 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))
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- Unzip the file and place `annotations` under `<CornerNet-Lite dir>/data/coco`
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- Download the images (2014 Train, 2014 Val, 2017 Test) from [here](http://cocodataset.org/#download)
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- Create 3 directories, `trainval2014`, `minival2014` and `testdev2017`, under `<CornerNet-Lite dir>/data/coco/images/`
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- Copy the training/validation/testing images to the corresponding directories according to the annotation files
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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 `<CornerNet-Lite dir>/configs/`. Each configuration file should have a corresponding model file in `<CornerNet-Lite dir>/core/models/`. i.e. If there is a `<model>.json` in `<CornerNet-Lite dir>/configs/`, there should be a `<model>.py` in `<CornerNet-Lite dir>/core/models/`. There is only one exception which we will mention later.
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### Training and Evaluating a Model
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To train a model:
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```
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python train.py <model>
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```
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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 `<CornerNet-Lite dir>/configs/`.
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To train CornerNet-Saccade:
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```
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python train.py CornerNet_Saccade
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```
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Please adjust the batch size in `CornerNet_Saccade.json` to accommodate the number of GPUs that are available to you.
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To evaluate the trained model:
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```
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python evaluate.py CornerNet_Saccade --testiter 500000 --split <split>
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```
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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 (`<model>-<suffix>.json`). There is no need to create `<model>-<suffix>.py` in `<CornerNet-Lite dir>/core/models/`.
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To use the new configuration file:
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```
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python evaluate.py <model> --testiter <iter> --split <split> --suffix <suffix>
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```
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We also include a configuration file for CornerNet under multi-scale setting, which is `CornerNet-multi_scale.json`, in this repo.
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To use the multi-scale configuration file:
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```
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python evaluate.py CornerNet --testiter <iter> --split <split> --suffix multi_scale

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