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algo-by-python

All Algorithms implemented in Python

att_unet_samantic

Pytorch implementation of U-Net, R2U-Net, Attention U-Net, and Attention R2U-Net.

awesome-autonomous-driving

awesome-autonomous-driving

awesome-federated-machine-learning

Everything about federated learning, including research papers, books, codes, tutorials, videos and beyond

awesome-llm

Awesome-LLM: a curated list of Large Language Model

awesome-multimodal-reasoning

Collection of papers and resources on Multimodal Reasoning, including Vision-Language Models, Multimodal Chain-of-Thought, Visual Inference, and others.

awesome-python

A curated list of awesome Python frameworks, libraries, software and resources

awesome-self-driving-car

An awesome list of self-driving cars

axial-deeplab

This is a PyTorch re-implementation of Axial-DeepLab (ECCV 2020 Spotlight)

bev-perception

Bird's Eye View Perception

bevfusion

[ICRA'23] BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation

birds-eye-view-perception

[IEEE T-PAMI] Awesome BEV perception research and cookbook for all level audience in autonomous diriving

birdseye-view-segmentation

bisenet

Add bisenetv2. My implementation of BiSeNet

bisenetv2

Implement a model of real-time semantic segmentation for autonomous driving

brain-inspired-deep-imitation-learning-for-autonomous-driving-systems

Autonomous driving vehicles have drawn a great deal of interests from both academia (e.g. Oxford, MIT) and industry (e.g. Google, Tesla). However, we find that it is very difficult to directly achieve fully autonomous driving (SAE Level 5) due to generalised knowledge. To deal with the problem, deep imitation learning is a promising solution which learns knowledge from the demonstration of human. In this project, we worked on how to use deep imitation learning to achieve vehicle dynamic control (e.g. steering angle, speed). We used a dataset and simulator provided by Udacity (https://github.com/udacity/self-driving-car-sim) and the real-world comma.ai dataset.

cam2bev-tf

TensorFlow Implementation for Computing a Semantically Segmented Bird's Eye View (BEV) Image Given the Images of Multiple Vehicle-Mounted Cameras.

camvid

CamVid original data set, and the generated 11 category labels and training grayscale images.

carla-ros-bridge

ROS bridge for CARLA Simulator

carla_dataset_tools

Tools for dataset generation based on CARLA simulator. (Data Collector)

carla_invs

multi-agent data collection and distributed learning in CARLA simulation

carla_ros_slam

RGBD-SLAM , GMAPPING, HECTORMAPPING, OCTOGRAPH , KARTOSLAM , GOOGLE CARTOGRAPHER SLAM implementation in CARLA SIMULATOR

cityscapes-segmentation

PyTorch implementation for Semantic Segmentation on Cityscapes dataset using R2UNET and its modified version.

cityscapes-to-coco-conversion

Cityscapes to CoCo Format Conversion Tool for Mask-RCNN and Detectron

cityscapesscripts

README and scripts for the Cityscapes Dataset

co-sne

code for CO-SNE: Dimensionality Reduction and Visualization for Hyperbolic Data (CVPR 2022)

crchfl

d-igpt

deeplabv3

Implementation of DeepLabV3 paper using Pytorch

faster-rcnn-pytorch

这是一个faster-rcnn的pytorch实现的库，可以利用voc数据集格式的数据进行训练。