[PYTORCH] Faster R-CNN (Region-Based Convolutional Neural Network)

Introduction

Here is my pytorch implementation of the model described in the paper VOC2012 paper.

How to use my code

With my code, you can:

• Train your model from scratch
• Train your model with my trained model
• Evaluate test images with either my trained model or yours

Requirements:

• python

• pytorch

• opencv (cv2)

• tensorboard

• numpy

  Datasets:

I used dataset: VOC2012. Statistics of datasets I used for experiments is shown below

Dataset	Classes	#Train images/objects	#Validation images/objects
VOC2007	20	5011/12608	4952/-
VOC2012	20	5717/13609	5823/13841
COCO2014	80	83k/-	41k/-
COCO2017	80	118k/-	5k/-

• VOC: Download the voc images and annotations from VOC2012. Make sure to put the files as the following structure:

  VOCDevkit
  ├── VOC2007
  │   ├── Annotations  
  │   ├── ImageSets
  │   ├── JPEGImages
  │   └── ...
  └── VOC2012
      ├── Annotations  
      ├── ImageSets
      ├── JPEGImages
      └── ...
  

• COCO: Download the coco images and annotations from coco website. Make sure to put the files as the following structure:

  COCO
  ├── annotations
  │   ├── instances_train2014.json
  │   ├── instances_train2017.json
  │   ├── instances_val2014.json
  │   └── instances_val2017.json
  │── images
  │   ├── train2014
  │   ├── train2017
  │   ├── val2014
  │   └── val2017
  └── anno_pickle
      ├── COCO_train2014.pkl
      ├── COCO_val2014.pkl
      ├── COCO_train2017.pkl
      └── COCO_val2017.pkl
  

  Results

Some output predictions for experiments for each dataset are shown below:

• VOC2012

My model's output.