TENET

This repository is the official implementation for TENET introduced in the paper:

Time-rEversed diffusioN tEnsor Transformer: A new TENET of Few-Shot Object Detection

Shan Zhang, Naila Murray, Lei Wang, Piotr Koniusz

ECCV 2022

Getting Started

Clone the repo:

git clone https://github.com/ZS123-lang/TENET.git

Requirements

Tested under python3.

python packages
- pytorch==0.4.1
- torchvision>=0.2.0
- cython
- matplotlib
- numpy
- scipy
- opencv
- pyyaml==3.12
- packaging
- pandas
- pycocotools — for COCO dataset, also available from pip.
- tensorboardX — for logging the losses in Tensorboard
An NVIDAI GPU and CUDA 9.0 are required. (Do not use other versions)
NOTICE: different versions of Pytorch package have different memory usages.

Compilation

Compile the CUDA code:

cd lib  # please change to this directory
sh make.sh

If your are using Volta GPUs, uncomment this line in lib/mask.sh and remember to postpend a backslash at the line above. CUDA_PATH defaults to /usr/loca/cuda. If you want to use a CUDA library on different path, change this line accordingly.

It will compile all the modules you need, including NMS, ROI_Pooing, ROI_Crop and ROI_Align. (Actually gpu nms is never used ...)

Note that, If you use CUDA_VISIBLE_DEVICES to set gpus, make sure at least one gpu is visible when compile the code.

Data Preparation

Please add data in the fsod directory and the structure is :

YOUR_PATH
    └── fsod
          ├── code files
          └── data
                ├──── fsod
                |       ├── annotations
                │       │       ├── fsod_train.json
                │       │       └── fsod_test.json
                │       └── images
                │             ├── part_1
                │             └── part_2
                │ 
                └──── pretrained_model
                        └── model_final.pkl (from detectron model zoo: End-to-End Faster & Mask R-CNN Baselines R-50-C4 Faster 2x model)

You can download the model_final.pkl from here: Model link