Note: this repo is currently under heavy development. It's not ready for general consumption. So, please refrain yourself from using it in production.
The goal of this project is to buid a single end-to-end deep learning model for more accurate and faster (near real-time) multi-object detection that can be train in single-pass of multiple different pieces:
Single Shot MultiBox Detector (SSD)
YOLOv3 real-time properties
Focal loss for dense object detection (RetinaNet)
Non Maximum Suppression (NMS)
Scalable object detection using deep neural networks
Faster R-CNN tricks
These techniques and methods from various research papers will be implemented using PyTorch.
We will be using Pascal VOC2007 dataset.
Requirements
Python 3
Pytorch 0.4
numpy
fastai PyTorch library
Training
# Select the script that you want to train for reproducing a results./retina_ce_sgd_0.001.sh# For the focal loss use ./retina_focal_sgd_0.0001.sh
You can see the details in trainer.py
VOC Dataset
Download VOC2007 trainval & test
# specify a directory for dataset to be downloaded into, else default is ~/data/sh data/scripts/VOC2007.sh # <directory>
Download VOC2012 trainval
# specify a directory for dataset to be downloaded into, else default is ~/data/sh data/scripts/VOC2012.sh # <directory>