Popular GitHub Repositories for Cherry Pick

? Cherry Studio is a desktop client that supports for multiple LLM providers. Support deepseek-r1

DockerComposeMaker (DCM) is a self-hostable website to help you pick and create a docker-compose.yml file for your home server. Discover new containers, discover and share a config in a couple of clicks!

? This is the mobile version of Cherry Studio.

Train robotic agents to learn pick and place with deep learning for vision-based manipulation in PyBullet. Transporter Nets, CoRL 2020.

A MuJoCo/Gym environment for robot control using Reinforcement Learning. The task of agents in this environment is pixel-wise prediction of grasp success chances.

Code for the paper "PICK: Processing Key Information Extraction from Documents using Improved Graph Learning-Convolutional Networks" (ICPR 2020)

Implementations of (theoretical) generative adversarial networks and comparison without cherry-picking

ComfyUI node that let you pick the way in which prompt weights are interpreted

A complete end-to-end demonstration in which we collect training data in Unity and use that data to train a deep neural network to predict the pose of a cube. This model is then deployed in a simulated robotic pick-and-place task.

MIT-Princeton Vision Toolbox for Robotic Pick-and-Place at the Amazon Robotics Challenge 2017 - Robotic Grasping and One-shot Recognition of Novel Objects with Deep Learning.

DockerComposeMaker (DCM) is a self-hostable website to help you pick and create a docker-compose.yml file for your home server. Discover new containers, discover and share a config in a couple of clicks!

Tools and libraries to run neural networks in Minecraft :pick:

This is a pick-your-problem style guide I created to educate everyone from my leadership team to my ML engineers on the process of how to work with AI in production settings.

Baxter Pick and Place using GR-ConvNet. IROS 2020.

myGym enables fast prototyping of RL in the area of robotic manipulation and navigation.You can train different robots, in several environments on various tasks. There is automatic evaluation and benchmark tool. From version 2.1 there is support for multi-step tasks, multi-reward training and multi-network architectures.

Customisable Unified Physical Simulations (CUPS) for Reinforcement Learning. Experiments run on the ai2thor environment (http://ai2thor.allenai.org/) e.g. using A3C, RainbowDQN and A3C_GA (Gated Attention multi-modal fusion) for Task-Oriented Language Grounding (tasks specified by natural language instructions) e.g. "Pick up the Cup or else"

Implement Google Deep Minds DQN for multiple agents for a grid world environment where vehicles must pick up customers.

AI Assistant to pick your favourite movie - and didactic demo for RAG

? Grasp - Pick-and-place with a robotic hand ??‍?

Simulation source code and examples for applying machine learning on Sawyer Robot