Training code for torchvision FasterRCNN model with custom COCO dataset

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Faster RCNN :

Faster RCNN is an object detection model introduced in Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks paper.

The architechure of Faster RCNN model is shown below,

Faster R-CNN, is composed of two modules. The first module is a deep fully convolutional network that proposes regions, and the second module is the Fast R-CNN detector that uses the proposed regions.

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Environment :

• Python version used : 3.9.16
• Create a python or conda environment using requirements.txt

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Training Instructions :

To train the Faster RCNN model follow the below steps :

1. Prepare dataset :

   • Prepare dataset in COCO format. It should have the below 2 files & folders
     • Image folder
     • Annotation file (Json file) in coco format

2. Run :

   python train.py --epoch 10 --train_image_dir <train_image_folder> --val_image_dir <val_image_folder> --train_coco_json <train_coco_json> --val_coco_json <val_coco_json> --batch_size 16 --exp_folder <experiment_folder>

   The training weights and tensorboard logs will be saved in experiment folder

   The training and validation logs can be visualized in tensorboard as shown below :

   Train logs Val Logs

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Inference :

The instruction about inference with a trained model are discussed in demo_inference.ipynb notebook