Repulsion Loss

This repository implements the code for Repulsion Loss: Detecting pedestrians in a crowd. This page(code, project and presentation) is the submission for Group B for final project for the course CS 256: Topics in Artificial Intelligence, Section 2 led by Prof. Mashhour Soul, PhD. The group members include:

• Vidish Naik
• Joel Alvares
• Charulata Lodha
• Rakesh Nagaraju

The code maybe used for educational and commercial use under no warranties. For questions on this project and code please reach out to: Vidish Naik at vidish.naik@sjsu.edu

Requirements:

• PyTorch: 0.4.0
• TorchVision: 0.2.0
• CUDA: 8.0

Preparation

Clone this repository

git clone https://github.com/VidishNaik/RepulsionLoss.git

cd into the folder

cd RepulsionLoss

Export the path for CUDA:

export CUDA_HOME="/usr/local/cuda-8.0"
export LD_LIBRARY_PATH="/usr/local/cuda-8.0/lib64":$LD_LIBRARY_PATH
export PATH="/usr/local/cuda-8.0/bin:$PATH"

Verify that CUDA is indeed set to CUDA8

nvcc -V

nvcc: NVIDIA (R) Cuda compiler driver Copyright (c) 2005-2016 NVIDIA Corporation Built on Tue_Jan_10_13:22:03_CST_2017 Cuda compilation tools, release 8.0, V8.0.61

Install the requirements

pip install -r requirements.txt

Run the startup.sh script

sh startup.sh

This will compile the required libraries in the lib folder and also download PASCAL VOC dataset in the dataset folder as well as download a pre-trained model in data/pretrained_model directory.

NOTE: The libraries in the current state are compiled for Nvidia Tesla K80 GPU. Change the CUDA_ARCH argument in lib/make.sh if you're using a different GPU. Refer this link for the correct architecture for your GPU. You can also refer the CUDA WIKI to get the correct architecture.

Running the model

python trainval_net.py --dataset pascal_voc --net vgg16 --bs 4 --nw 1 --cuda

• Keep batch size (bs) < 4 on a Tesla K80 GPU as it will run out of memory
• Currently only works for vgg16. Future updates might include resnet101.
• dataset can be changed to pascal_voc_0712 for PASCAL VOC 2012

Model download

For testing the model download the pre-trained weights and store it in models/vgg16/pascal_voc/faster_rcnn_1_10_2504.pth

Testing

python test_net.py --net vgg16 --checksession 1 --checkepoch 10 --checkpoint 2504 --cuda

Demo

Store the images that you need to run in the images folder. New images with detections will be stored in the same folder

python demo.py --cuda --load_dir models --net vgg16 --checksession 1 --checkepoch 10 --checkpoint 2504

Credits

This project was conducted with free credits provided by AWS educate team.