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🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.
Tensors and Dynamic neural networks in Python with strong GPU acceleration
A high-throughput and memory-efficient inference and serving engine for LLMs
The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (V…
OpenMMLab Detection Toolbox and Benchmark
Pretrain, finetune ANY AI model of ANY size on 1 or 10,000+ GPUs with zero code changes.
FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.
Fast and memory-efficient exact attention
Python Implementation of Reinforcement Learning: An Introduction
Hackable and optimized Transformers building blocks, supporting a composable construction.
OpenMMLab Semantic Segmentation Toolbox and Benchmark.
OpenMMLab's next-generation platform for general 3D object detection.
A PyTorch implementation of NeRF (Neural Radiance Fields) that reproduces the results.
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
A PyTorch Library for Accelerating 3D Deep Learning Research
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
FCOS: Fully Convolutional One-Stage Object Detection (ICCV'19)
Papers and Datasets about Point Cloud.
PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud, CVPR 2019.
SECOND for KITTI/NuScenes object detection
Deep Hough Voting for 3D Object Detection in Point Clouds
Frustum PointNets for 3D Object Detection from RGB-D Data
Fast, modular reference implementation and easy training of Semantic Segmentation algorithms in PyTorch.
[CVPR2022] Geometric Transformer for Fast and Robust Point Cloud Registration
Kernel Point Convolution implemented in PyTorch
[ICCV 2021 Oral] PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers
[CVPR 2021, Oral] PREDATOR: Registration of 3D Point Clouds with Low Overlap.
A faster implementation of PointNet++ based on PyTorch.