Implementation of the DETR (DEtection TRansformer) network (Carion, Nicolas, et al., 2020) in Tensorflow. The model was originally developed by Facebook Inc. and implemented in PyTorch. This repository solely aims to make the architecture accessible for Tensorflow users.

• Research Paper: End-to-End Object Detection with Transformers
• Original PyTorch Implementation: GitHub

We use the following packages:

• python 3.7.3
• tensorflow 2.1.0
• scipy 1.4.1
• numpy 1.18.3
• numba 0.49.0

git clone https://github.com/auvisusAI/detr-tensorflow.git 			# Clone Repository
cd detr-tensorflow/								# Change to directory
pip3 install -e .								# Install repository and required packages

storage_path/				# path/to/data_storage
	labels/				#.txt files where each line corresponds to one object in image
	images/				#.jpg files

You can easily start the training procedure from detr_models/detr/ using:

python3 train.py --storage_path <PATH> --output_dir <PATH> <Additional Parameters>

Additional parameters such as epochs, batch_size etc. can be set. Please take a look at the help text for a complete overview using:

python3 train.py --help

If no additional parameters are used, the defaults as specified in detr_models/detr/config.py will be used.

If you want to execute training (e.g. on a pre-trained model) or just get a quick overview over the model architecture, you can also use the jupyter notebook DETR.ipynb provided in /notebooks.

• [] Adjust data_feeder/loadlabel to handle Coco annotations
• [] Include unittests to verify code
• [] Take max_obj into config
• [] Include mask head to model for segmentation
• [] Parameterize to handle images with varying shape and paddings
• [] Parameterize backbone config
• [] Include inference script
• [] Include inference notebook

As you can see, there are still many open to-dos. We are happy for all contributions to improve this Tensorflow implementation.