/Adaptive-deformable-convolution

Pytorch-based adaptive deformable convolution

Primary LanguagePythonMIT LicenseMIT

pytorch-adaptive-deformable-convolution

Introduction

This is a PyTorch(0.4.1) implementation of Adaptive deformable convnet. For the result please refer to our paper under review.

Installation

The code was tested with Anaconda and Python 3.6. After installing the Anaconda environment:

  1. Clone the repo:

  2. Install dependencies:

    For PyTorch dependency, see pytorch.org for more details.

    For custom dependencies:

    pip install matplotlib pillow tensorboardX tqdm

Training

Fellow steps below to train your model:

  1. Configure your dataset path in mypath.py.

  2. Input arguments: (see full input arguments via python train.py --help):

    usage: train.py [-h] [--backbone {resnet,xception,drn,mobilenet}]
            [--out-stride OUT_STRIDE] [--dataset {pascal,coco,cityscapes}]
            [--use-sbd] [--workers N] [--base-size BASE_SIZE]
            [--crop-size CROP_SIZE] [--sync-bn SYNC_BN]
            [--freeze-bn FREEZE_BN] [--loss-type {ce,focal}] [--epochs N]
            [--start_epoch N] [--batch-size N] [--test-batch-size N]
            [--use-balanced-weights] [--lr LR]
            [--lr-scheduler {poly,step,cos}] [--momentum M]
            [--weight-decay M] [--nesterov] [--no-cuda]
            [--gpu-ids GPU_IDS] [--seed S] [--resume RESUME]
            [--checkname CHECKNAME] [--ft] [--eval-interval EVAL_INTERVAL]
            [--no-val]

Acknowledgement

PyTorch-Encoding

Synchronized-BatchNorm-PyTorch

[drn]

pytorch-deeplab-xception

mmdetection

Deformable-ConvNets