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Sylph: A Hypernetwork Framework for Incremental Few-shot Object Detection

License: CC BY-NC 4.0

Authors: Li Yin, Juan M Perez-Rua, Kevin J Liang

This repository is the official PyTorch implementation of Sylph: A Hypernetwork Framework for Incremental Few-shot Object Detection, accepted to CVPR 2022.

Citation

If you find any part of our paper or this codebase useful, please consider citing our paper:

@inproceedings{yin2022sylph,
  title={Sylph: A Hypernetwork Framework for Incremental Few-shot Object Detection},
  author={Yin, Li and Perez-Rua, Juan M and Liang, Kevin J},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={9035--9045},
  year={2022}
}

License

Please see LICENSE.md for more details.

Install packages

Requires Python 3.8+.

Install virtualenv if not installed yet:

python3 -m pip install --user virtualenv

Create an environment:

python3 -m venv env

Activate the environment:

source env/bin/activate

Install dependencies:

pip install torch
pip3 install -r requirements.txt --find-links https://download.pytorch.org/whl/nightly/cu102/torch_nightly.html

Note: If installation with requirements.txt does not work, try installing everything in requirements.txt separately.

If you run into problem install d2go. Follow the instruction here: https://github.com/facebookresearch/d2go/blob/main/README.md. Use python setup.py install.

Prepare Sylph module, run python setup.py develop.

META_ARCHITECTURE

Name: MetaOneStageDetector

Specify function to build backbone: build_fcos_resnet_fpn_backbone

The architecture constructs the model, including the base detector, backbone, and few-shot hypernetwork.

Few-shot Hypernetwork Code Structure

Code generator

To customize or add another code generator, go to directory sylph/modeling/code_generator/. An example is to add roi_encoder.

Please refer to build.py for initializing the code generator, and forward for the inputs needed to run a code generator and the expected outputs.

Runner

As different code generators likely differ in code generator parameters, it is better to add a new runner for each code generator we add. For this, refer to sylph/runner/meta_fcos_roi_encoder_runner.py. This runner should inherit from MetaFCOSRunner for training and testing data loaders and logic.

Datasets

All datasets are located at /datasets

Download COCO images and annotations here: https://cocodataset.org/#download.

wget http://images.cocodataset.org/zips/train2017.zip
wget http://images.cocodataset.org/zips/val2017.zip
wget http://images.cocodataset.org/annotations/annotations_trainval2017.zip

Expected dataset file structure for COCO:

coco/
  annotations/
    instances_{train,val}2017.json
  {train,val}2017/
    # image files that are mentioned in the corresponding json

Expected dataset file structure for LVIS:

Download annotation from here: https://www.lvisdataset.org/dataset. LVIS uses the same image directory as COCO.

wget https://s3-us-west-2.amazonaws.com/dl.fbaipublicfiles.com/LVIS/lvis_v1_train.json.zip
wget https://s3-us-west-2.amazonaws.com/dl.fbaipublicfiles.com/LVIS/lvis_v1_val.json.zip

coco/
  {train,val}2017/
lvis/
  lvis_v1_{train,val}.json

Config files

All config files are located at /configs, to specify a config file, use prefix sylph://.

configs="sylph://LVIS-Meta-FCOS-Detection/Meta_FCOS_MS_R_50_1x.yaml"

Sylph Few-shot Detection Training & Evaluation

Under the root directory sylph/, run tools/train_net.py, and specify --config-file and --runner.

Provide the test mode, the number of steps, the number of GPUs, and the batch size, set to a very small number to test the workflow end to end. Use export SYLPH_TEST_MODE=true to turn it on.

Train Meta-FCOS

Run at the root directory.

COCO

Pre-training on 60 COCO base classes.

python3 tools/train_net.py --runner sylph.runner.MetaFCOSRunner --config-file "sylph://COCO-Detection/Meta-FCOS/Meta-FCOS-pretrain.yaml" --num-processes 3  --output-dir output/meta-fcos/coco/pretrain/

Meta-learning on 60 base classes, and meta-test on 20:

First, find the weight checkpoint you want to use from pretraining, and modify MODEL.WEIGHTS.

python3 tools/train_net.py --runner sylph.runner.MetaFCOSRunner --config-file "sylph://COCO-Detection/Meta-FCOS/Meta-FCOS-finetune.yaml" --num-processes 3  --output-dir output/meta-fcos/coco/meta-train/ MODEL.WEIGHTS output/meta-fcos/coco/pretrain/model_final.pth

LVIS

Pre-training

python3 tools/train_net.py --runner sylph.runner.MetaFCOSRunner --config-file "sylph://LVISv1-Detection/Meta-FCOS/Meta-FCOS-pretrain.yaml" --num-processes 3  --output-dir output/meta-fcos/lvis/pretrain/

Meta-leraning. Initialize the weights to the pth model in the pre-training stage.

python3 tools/train_net.py --runner sylph.runner.MetaFCOSRunner --config-file "sylph://LVISv1-Detection/Meta-FCOS/Meta-FCOS-finetune.yaml" --num-processes 3  --output-dir output/meta-fcos/lvis/meta-train/ MODEL.WEIGHTS output/meta-fcos/coco/pretrain/model_final.pth

To change the code generator, change the config file and switch to a different Runner (TODO: merge them together to support differnt code generator in the same runner)

TFA-FCOS Train & Eval

TBD.

Test

TBD.