Converter

A repository for a universal I/O spec for TAMP, along with scripts to convert from popular specs to our spec

A repository for a universal I/O spec for TAMP, along with scripts to convert from popular specs to our spec Installation Dependencies This repository requires Python 3.8+. We recommend you create a conda env or virtual env with an appropriate Python version before installing the other dependencies. If you are creating a virtual environment within the project directory, then you might want to call it one of .env, env, .venv, venv as the code checks have been configured to exclude […]

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Gesture

Gesture Volume Control With Python

In this project I am going to learn how to use Gesture Control to change the volume of a computer. I first look into hand tracking and then I will use the hand landmarks to find gesture of my hand to change the volume. Features Can track your hand in real-time Can change your computer’s volume based on your hand activity How to install Clone this repository on your computer https://github.com/paveldat/gesture_volume_control.git Install all the requirements run libraries.bat Run the program […]

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Self-Supervised

Watermarking Images in Self-Supervised Latent-Spaces

PyTorch implementation and pretrained models for the paper. For details, see Watermarking Images in Self-Supervised Latent-Spaces. If you find this repository useful, please consider giving a star ⭐ and please cite as: @inproceedings{fernandez2022sslwatermarking, title={Watermarking Images in Self-Supervised Latent Spaces}, author={Fernandez, Pierre and Sablayrolles, Alexandre and Furon, Teddy and Jégou, Hervé and Douze, Matthijs}, booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, year={2022}, organization={IEEE}, } [Webpage] [arXiv] [Spaces] [Colab] Introduction   To finish reading, please visit source site

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Dataset

Experiments with Circle Loss on AIC 2021’s Vehicle Retrieval Dataset

Info Usage Extract metadata The dataset provided metadata in the form of an XML file train_label.xml which can be hard to processed. We first convert this into a more accessible JSON file. The result will be saved as list/train_image_metadata.json. Split data Since we use the data above for training, evaluation, and testing, we split it into corresponding CSV files. The results are stored in the list folder as CSVs file of tuples of (image_id, vehicle_id, cam_id): reid_train.csv: contains the training […]

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Transformer

MetaMorph: Learning Universal Controllers with Transformers

This is the code for the paper MetaMorph: Learning Universal Controllers with Transformers Agrim Gupta, Linxi Fan, Surya Ganguli, Fei-Fei Li Multiple domains like vision, natural language, and audio are witnessing tremendous progress by leveraging Transformers for large scale pre-training followed by task specific fine tuning. In contrast, in robotics we primarily train a single robot for a single task. However, modular robot systems now allow for the flexible combination of general-purpose building blocks into task optimized morphologies. However, given […]

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Dataset

Dataset Distillation by Matching Training Trajectories

Project Page | Paper This repo contains code for training expert trajectories and distilling synthetic data from our Dataset Distillation by Matching Training Trajectories paper (CVPR 2022). Please see our project page for more results. Dataset Distillation by Matching Training Trajectories George Cazenavette, Tongzhou Wang, Antonio Torralba, Alexei A. Efros, Jun-Yan Zhu CMU, MIT, UC Berkeley CVPR 2022 The task of “Dataset Distillation” is to learn a small number of synthetic images such that a model trained on this set […]

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Scripts

Implementation of some unbalanced loss like focal_loss, dice_loss, DSC Loss, GHM Loss et.al

Implementation of some unbalanced loss for NLP task like focal_loss, dice_loss, DSC Loss, GHM Loss et.al Summary Here is a loss implementation repository included unbalanced loss How to use? You can find all the loss usage information in test_loss.py. Here is a simple demo of usage: import torch from unbalanced_loss.focal_loss import MultiFocalLoss batch_size, num_class = 64, 10 Loss_Func = MultiFocalLoss(num_class=num_class, gamma=2.0, reduction=

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Wrapper

Python wrapper kernel for Crystal

Simple Python wrapper kernel for Crystal language. ICrystal is the widely used Jupyter kernel for Crystal, which uses ICR. On the other hand, this crystal_kernel uses the official Crystal interpreter. Forked from bash_kernel installation Make sure the Crystal’s interpreter starts with crystal i. Then type the following commands. pip install crystal_kernel python -m crystal_kernel.install Development Something is better than nothing. GitHub View Github    

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