transfuser This repository contains the code for the CVPR 2021 paper Multi-Modal Fusion Transformer for End-to-End Autonomous Driving. If you find our code or paper useful, please cite @inproceedings{Prakash2021CVPR, author = {Prakash, Aditya and Chitta, Kashyap and Geiger, Andreas}, title = {Multi-Modal Fusion Transformer for End-to-End Autonomous Driving}, booktitle = {Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2021} } Setup Install anaconda wget https://repo.anaconda.com/archive/Anaconda3-2020.11-Linux-x86_64.sh bash Anaconda3-2020.11-Linux-x86_64.sh source ~/.profile Clone the repo and build the environment git clone […]

Detect waste AI4Good project for detecting waste in environment. Did you know that we produce 300 million tons of plastic every year? And only the part of it is properly recycled. The idea of detect waste project is to use Artificial Intelligence to detect plastic waste in the environment. Our solution is applicable for video and photography. Our goal is to use AI for Good. In Detect Waste in Pomerania project we used 9 publicity available datasets, and additional data […]

TensorHive TensorHive is an open source tool for monitoring and managing computing resources across multiple hosts. It solves the most common problems and nightmares about accessing and sharing your AI-oriented infrastructure across multiple, often competing users. It’s designed with simplicity, flexibility and configuration-friendliness in mind. Main features: GPU Reservation calendarEach column represents all reservation events for a GPU on a given day. In order to make a new reservation simply click and drag with your mouse, select GPU(s), add some […]

zeus zeus is a Python implementation of the Ensemble Slice Sampling method. Fast & Robust Bayesian Inference, Efficient Markov Chain Monte Carlo (MCMC), Black-box inference, no hand-tuning, Excellent performance in terms of autocorrelation time and convergence rate, Scale to multiple CPUs without any extra effort. Example For instance, if you wanted to draw samples from a 10-dimensional Gaussian, you would do something like: import zeus import numpy as np def log_prob(x, ivar): return – 0.5 * np.sum(ivar * x**2.0) nsteps, […]

Reinforcement Learning (PyTorch) This repo will contain PyTorch implementation of various fundamental RL algorithms. It’s aimed at making it easy to start playing and learning about RL. The problem I came across investigating other DQN projects is that they either: Don’t have any evidence that they’ve actually achieved the published results Don’t have a “smart” replay buffer (i.e. they allocate (1M, 4, 84, 84) ~ 28 GBs! instead of (1M, 84, 84) ~ 7 GB) Lack of visualizations and debugging […]

CFPNet-M This repository contains the implementation of a novel light-weight real-time network (CFPNet-Medicine: CFPNet-M) to segment different types of biomedical images. It is a medical version of CFPNet, and the dataset we used from top to bottom are DRIVE, ISBI-2012, Infrared Breast, CVC-ClinicDB and ISIC 2018. The details of CFPNet-M and CFPNet can be found here respectively. Architecture of CFPNet-M CFP module CFPNet-M Dataset In this project, we test five datasets: [x] Infrared Breast Dataset [x] Endoscopy (CVC-ClinicDB) [x] Electron […]

ResMLP – Pytorch Implementation of ResMLP, an all MLP solution to image classification out of Facebook AI, in Pytorch Install $ pip install res-mlp-pytorch Usage import torch from res_mlp_pytorch import ResMLP model = ResMLP( image_size = 256, patch_size = 16, dim = 512, depth = 12, num_classes = 1000 ) img = torch.randn(1, 3, 256, 256) pred = model(img) # (1, 1000) Citations @misc{touvron2021resmlp, title = {ResMLP: Feedforward networks for image classification with data-efficient training}, author = {Hugo Touvron and […]

Barlow-Twins-TF This repository iimplements Barlow Twins (Barlow Twins: Self-Supervised Learning via Redundancy Reduction) in TensorFlow and demonstrates it on the CIFAR10 dataset. Summary: With a ResNet20 as a trunk and a 3-layer MLP (each layer containing 2048 units) and 100 epochs of pre-training, this training notebook can give 62.61% accuracy on the CIFAR10 test set. The pre-training total takes ~23 minutes on a single Tesla V100. There are minor differences from the original implementation. However, the original loss function and […]