End to end detection in crowded scenes
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train.py
train_boost_ip_split.py
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README.md

ReInspect Domain Adapation

Unsupervised domain adaptation architercture for pedestrian detection. See the paper for details or the ppt for a demonstration.

Installation

Prerequisite - install ApolloCaffe

Some new layers used for this project are implemented in my forked ApolloCaffe. Please firstly pull and compile the ApolloCaffe. If you have problems installing ApolloCaffe, you can refer to http://apollocaffe.com.

$ git clone https://github.com/LihangLiu93/apollocaffe.git

Install ReInspect

With ApolloCaffe installed, you can run ReInspect with:

$ git clone https://github.com/LihangLiu93/ReInspect.git
$ cd reinspect

Data

The data consists of:

  1. The source domain data from Russell's project, which this project is built on. The data can be found here.

  2. The target domain data collected for domain adaptation, please put the data from the following links into the corresponding directories.

    dir:./multi_scene_data/annnotation/second_carteen/ link: https://pan.baidu.com/s/1c16O1Hm password: d9kc

    dir:./multi_scene_data/pre_data/images_640_480/second_carteens[01-03]/ link: https://pan.baidu.com/s/1dFqhGWh password: rs9b

Run

$ python train_boost_ip_split.py --gpu=0 --config=config_boost_ip_split.json  --weights=./data/brainwash_800000.h5

Q&A

If you have further questions regarding the project, please email backchord at gmail dot com.