Tracker

Track, Check, Repeat: An EM Approach to Unsupervised Tracking

This is the official code release for our CVPR21 paper on unsupervised detection and tracking. It produces results slightly better than reported in the paper. [Paper] [Project Page] We use ensemble agreement between 2d and 3d models, as well as motion cues, to unsupervisedly learn object detectors from scratch. Top: 3d detections. Middle: 2d segmentation. Bottom-left: unprojected 2d segmentation, in a bird’s eye view. Bottom-right: 3d detections, in a bird’s eye view. Overview An EM approach to unsupervised tracking. We […]

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Scripts

A script that lists all current Bisq offers in the terminal

A script that lists all current Bisq offers in the terminal Bisq is a decentralized bitcoin exchange that allows us to buy or sell bitcoin peer-to-peer. This script shows all current buy or sell offers in the Bisq network in our preferred fiat currency. We can indicate the following paramenters in our script header: avoid_methods: payment methods to hide in order to get a cleaner output lsbisq.py -t -f -d Where: : (string) BUY or SELL (example: -t BUY). Default […]

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OBJECT DETECTION

Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection from Point Clouds (CVPR2022)

Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection from Point Clouds (CVPR2022)[paper] Authors: Chenhang He, Ruihuang Li, Shuai Li, Lei Zhang. This project is built on OpenPCDet. Introduction Transformer has demonstrated promising performance in many 2D vision tasks. However, it is cumbersome to compute the self-attention on large-scale point cloud data because point cloud is a long sequence and unevenly distributed in 3D space. To solve this issue, existing methods usually compute self-attention locally by grouping the points […]

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Helper

Display your data in an attractive way in your notebook

Bloxs is a simple python package that helps you display information in an attractive way (formed in blocks). Perfect for building dashboards, reports and apps in the notebook. It works with: Jupyter Notebook, Google Colab, Deepnote, Kaggle Notebook, Mercury. Get started Install bloxs: Import and create a bloxs: from bloxs import B B(1234, “Bloxs in notebook!”) Exmaples Bloxs Code B(1234, “Bloxs in notebook!”) B(1999, “Percent change!”, percent_change=10)    

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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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