Tool

Python tools for running and evaluating DM-VIO

When using this project in academic work, please consider citing: @article{stumberg22dmvio, author = {L. von Stumberg and D. Cremers}, title = {{DM-VIO}: Delayed Marginalization Visual-Inertial Odometry}, journal = {{IEEE} Robotics and Automation Letters ({RA-L})}, year = {2022}, volume = {7}, number = {2}, pages = {1408-1415}, doi = {10.1109/LRA.2021.3140129} } Dependencies: Install dependencies with: pip3 install tqdm ruamel.yaml pyquaternion matplotlib tabulate Step 0: Download and build    

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Blender

glTF extras: Speakers and Blender Object Types addon

This addon exports Speakers data and Blender Object types into extra fields of a glTF. extras are custom fields you can write into the glTF. They will mean nothing for a official, standard glTF importer. To actually make use of those data, a parsing function must be coded in the importer, I’ll provide a sample method for three.js, that’s the environment this addon was built for. THOSE ARE NOT NATIVE glTF FUNCTIONALITIES, NOR THIS ADDON WILL ADD ANY FUNCTIONALITY TO […]

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Transformer

Ensembling Hugging Face transformers made easy

Ensembling Hugging Face Transformers made easy! Why Ensemble Transformers? Ensembling is a simple yet powerful way of combining predictions from different models to increase performance. Since multiple models are used to derive a prediction, ensembling offers a way of decreasing variance and increasing robustness. Ensemble Transformers provides an intuitive interface for ensembling pretrained models available in Hugging Face transformers. Installation Ensemble Transformers is available on PyPI and can easily be installed with the pip package manager. pip install -U pip […]

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YOLO

Implement mosaic image augmentation with YOLO format

This is an implementation for mosaic image augmention with YOLO format Run code to perform mosaic augmentation: python main.py –width 800 –height 800 –scale_x 0.4 –scale_y 0.6 –min_area 500 –min_vi 0.3 You can change parameters to fit with your dataset. To know details about arguments in the command above, run –width: width of mosaic-augmented image –height: height of mosaic-augmented image –scale_x: scale_x – scale by width => define width of the top left image –scale_y: scale_y – scale by height […]

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OBJECT DETECTION

Expanding Low-Density Latent Regions for Open-Set Object Detection

OpenDet Expanding Low-Density Latent Regions for Open-Set Object Detection (CVPR2022) Jiaming Han, Yuqiang Ren, Jian Ding, Xingjia Pan, Ke Yan, Gui-Song Xia. arXiv preprint. OpenDet2: OpenDet is implemented based on detectron2. Setup The code is based on detectron2 v0.5. Here is a from-scratch setup script. conda create -n opendet2 python=3.8 -y conda activate opendet2 conda install pytorch=1.8.1 torchvision cudatoolkit=10.1 -c pytorch -y pip install detectron2==0.5 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu101/torch1.8/index.html git clone    

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Adaptation

Efficient and Degradation-Adaptive Network for Real-World Image Super-Resolution

Paper Efficient and Degradation-Adaptive Network for Real-World Image Super-Resolution Jie Liang*, Hui Zeng*, and Lei Zhang. In arxiv preprint. Abstract Efficient and effective real-world image super-resolution (Real-ISR) is a challenging task due to the unknown complex degradation of real-world images and the limited computation resources in practical applications. Recent research on Real-ISR has achieved significant progress by modeling the image degradation space; however, these methods largely rely on heavy backbone networks and they are inflexible to handle images of different […]

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Distributed

A library for building and serving multi-node distributed faiss indices

Distributed faiss index service. A lightweight library that lets you work with FAISS indexes which don’t fit into a single server memory. It follows a simple concept of a set of index server processes runing in a complete isolation from each other. All the coordination is done at the client side. This siplified many-vs-many client-to-server relationship architecture is flexible and is specifically designed for research projects vs more complicated solutions that aims mostly at production usage and transactionality support. The […]

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Tool

A tool for evaluating the predictive performance on activity cliff compounds of machine learning models

Molecule Activity Cliff Estimation (MoleculeACE) is a tool for evaluating the predictive performance on activity cliff compounds of machine learning models. MoleculeACE can be used to: Analyze and compare the performance on activity cliffs of machine learning methods typically employed in QSAR. Identify best practices to enhance a model’s predictivity in the presence of activity cliffs. Design guidelines to consider when developing novel QSAR approaches. Benchmark study In a benchmark study we collected and curated bioactivity data on 30 macromolecular […]

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Transformer

BoxeR: Box-Attention for 2D and 3D Transformers

By Duy-Kien Nguyen, Jihong Ju, Olaf Booij, Martin R. Oswald, Cees Snoek. This repository is an official implementation of the paper BoxeR: Box-Attention for 2D and 3D Transformers. Introduction TL; DR. BoxeR is a Transformer-based network for end-to-end 2D object detection and instance segmentation, along with 3D object detection. The core of the network is Box-Attention which predicts regions of interest to attend by learning the transformation (translation, scaling, and rotation) from reference windows, yielding competitive performance on several vision […]

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Neural Network

CoNeRF: Controllable Neural Radiance Fields

This is the official implementation for “CoNeRF: Controllable Neural Radiance Fields” The codebase is based on HyperNeRF implemente in JAX, building on JaxNeRF. Setup The code can be run under any environment with Python 3.8 and above. (It may run with lower versions, but we have not tested it). We recommend using Miniconda and setting up an environment: conda create –name conerf python=3.8 Next, install the required packages: pip install -r requirements.txt Install the appropriate JAX distribution    

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