Minimalistic open-source library for metric learning written in TensorFlow2, TF-Addons, Numpy, OpenCV(CV2) and Annoy. This repository contains a TensorFlow2+/tf.keras implementation some of the loss functions and miners. This repo
Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.
TorchShard is a lightweight engine for slicing a PyTorch tensor into parallel shards. It can reduce GPU memory and scale up the training when the model has massive linear layers (e.g., ViT, BERT and GPT) or huge classes (millions
Arbitrary-scale super-resolution is a raising research topic with tremendous application potentials. Prior CNN-based SR approaches usually apply to only one fixed resolution scale, and thus unable to adjust their output dimension
This contains a PyTorch implementation of Differentiable Optimizers with Perturbations in Tensorflow. All credit belongs to the original authors which can be found below. The source code, tests, and examples given below are a one-
TorchPQ is a python library for Approximate Nearest Neighbor Search (ANNS) and Maximum Inner Product Search (MIPS) on GPU using Product Quantization (PQ) algorithm. TorchPQ is implemented mainly with PyTorch, with some extra CUDA
ImageNet-1K serves as the primary dataset for pretraining deep learning models for computer vision tasks. ImageNet-21K dataset, which contains more pictures and classes, is used less frequently for pretraining, mainly due to its c
Neural Ordinary Differential Equations (NODEs) use a neural network to model the instantaneous rate of change in the state of a system. However, despite their apparent suitability for dynamics-governed time-series, NODEs present a