SMCA replication There are no extra compiled components in SMCA DETR and package dependencies are minimal

Overview

Usage

There are no extra compiled components in SMCA DETR and package dependencies are minimal, so the code is very simple to use. We provide instructions how to install dependencies via conda. First, clone the repository locally:

git clone https://github.com/facebookresearch/detr.git

Then, install PyTorch 1.5+ and torchvision 0.6+:

conda install -c pytorch pytorch torchvision

Install pycocotools (for evaluation on COCO) and scipy (for training):

conda install cython scipy
pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'

That's it, should be good to train and evaluate detection models.

(optional) to work with panoptic install panopticapi:

pip install git+https://github.com/cocodataset/panopticapi.git

Data preparation

Download and extract COCO 2017 train and val images with annotations from http://cocodataset.org. We expect the directory structure to be the following:

path/to/coco/
  annotations/  # annotation json files
  train2017/    # train images
  val2017/      # val images

Training

To train Single Scale SMCA on a single node with 8 gpus for 300 epochs run:

python -m torch.distributed.launch --nproc_per_node=8 --use_env main.py --coco_path /path/to/coco --batch_size 2 --lr_drop 40 --num_queries 300 --epochs 50 --dynamic_scale type3 --output_dir smca_single_scale


A single epoch takes 30 minutes, so 50 epoch training takes around 25 hours on a single machine with 8 V100 cards.

name backbone schedule box AP
0 SMCA(single scale) R50 50 41.0
1 SMCA(single scale) R50 108 42.7
2 SMCA(single scale) R50 250 43.5
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