This is the official code for the CVPR 2022 paper: "iFS-RCNN: An Incremental Few-shot Instance Segmenter"

This codebase is primarily based on this codebase of https://github.com/ucbdrive/few-shot-object-detection

• iFS-RCNN: An Incremental Few-shot Instance Segmenter
  • Table of Contents
  • Installation
  • Code Structure
  • Data Preparation
  • Getting Started
    • Training & Evaluation in Command Line
    • Multiple Runs

Requirements

• Linux with Python >= 3.6
• PyTorch >= 1.4
• torchvision that matches the PyTorch installation
• CUDA 10.0, 10.1, 10.2
• GCC >= 4.9

Build fsdet

• Create a virtual environment.

python3 -m venv fsdet
source fsdet/bin/activate

You can also use conda to create a new environment.

conda create --name fsdet
conda activate fsdet

• Install Pytorch 1.6 with CUDA 10.2

pip install torch torchvision

You can choose the Pytorch and CUDA version according to your machine. Just to make sure your Pytorch version matches the prebuilt detectron2

• Install Detectron2 v0.2.1

python3 -m pip install detectron2 -f \
  https://dl.fbaipublicfiles.com/detectron2/wheels/cu102/torch1.6/index.html

• Install other requirements.

python3 -m pip install -r requirements.txt

• configs: Configuration files
• datasets: Dataset files (see Data Preparation for more details)
• iFS-RCNN
  • checkpoint: Checkpoint code.
  • config: Configuration code and default configurations.
  • engine: Contains training and evaluation loops and hooks.
  • layers: Implementations of different layers used in models.
  • modeling: Code for models, including backbones, proposal networks, and prediction heads.
• tools
  • train_net.py: Training script.
  • test_net.py: Testing script.
  • ckpt_surgery.py: Surgery on checkpoints.
  • run_experiments.py: Running experiments across many seeds.
  • aggregate_seeds.py: Aggregating results from many seeds.

We evaluate our models on two datasets:

• COCO: We use COCO 2014 and extract 5k images from the val set for evaluation and use the rest for training. We use the 20 object classes that are the same with PASCAL VOC as novel classes and use the rest as base classes.
• LVIS: We treat the frequent and common classes as the base classes and the rare categories as the novel classes.

See datasets/README.md for more details.

• To train a model on base classes, refer to the train.sh script

• To fine-tune a model on novel classes, refer to the finetuning.sh script

• To evaluate the trained models, refer to the test.sh script

• For multiple running experiments, refer to run_experiments.sh script

• To aggregate results of multiple runs, refer to aggregate_seeds.sh script