GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration.
- pytorch
- gan
- deep-learning
- super-resolution
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GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration.
🦉 OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation
Open-source, secure environment with real-world tools for enterprise-grade agents.
HexStrike AI MCP Agents is an advanced MCP server that lets AI agents (Claude, GPT, Copilot, etc.) autonomously run 150+ cybersecurity tools for automated pentesting, vulnerability discovery, bug bounty automation, and security research. Seamlessly bridge LLMs with real-world offensive security capabilities.
Cleanlab's open-source library is the standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
Agent Reinforcement Trainer: train multi-step agents for real-world tasks using GRPO. Give your agents on-the-job training. Reinforcement learning for Qwen3.6, GPT-OSS, Llama, and more!
SWE-bench: Can Language Models Resolve Real-world Github Issues?
SwinIR: Image Restoration Using Swin Transformer (official repository)
Fun real-world computer vision demos!
Prov-GigaPath: A whole-slide foundation model for digital pathology from real-world data
Modular autonomous driving platform running on the CARLA simulator and real-world vehicles.
Official Code for OpenVLHarness: Real-World Visual Agentic Tool‑Use with Multimodal Memory
Official implementation of RelayVSR: Large-Small Model Collaboration for Efficient Real-World Video Super-Resolution
The official code of Agent-World: Scaling Real-World Environment Synthesis for Evolving General Agent Intelligence
Build innovative Skills with McDonald’s MCP, solve real-world problems, and compete for exclusive 1024 Programmers’ Day prizes.