머신러닝 · GitHub

[ICCV 2021 Oral] PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers

🦓 AI-powered OpenStreetMap tool for importing zebra crossings

Text-to-Image generation. The repo for NeurIPS 2021 paper "CogView: Mastering Text-to-Image Generation via Transformers".

A visual-based graph node editor for training computer vision models.

MNIST와 유사한 패션 상품 데이터베이스. 벤치마크 :point_down:

PyTorch based Probabilistic Time Series forecasting framework based on GluonTS backend

Concrete ML: Privacy Preserving ML framework using Fully Homomorphic Encryption (FHE), built on top of Concrete, with bindings to traditional ML frameworks.

• Built-in models: Ready-to-use FHE-friendly models with a user interface that is equivalent to their the scikit-learn and XGBoost counterparts
• Customs models: Concrete ML supports models that can use quantization-aware training. These are developed by the user using PyTorch or keras/tensorflow and are imported into Concrete ML through ONNX

SFML is the first fully local AI solar forecast for Home Assistant, powered by a local Attention Transformer. No external AI — such as ChatGPT, Gemini, or Grok — required. Runs entirely on your device for complete privacy.

• The forecast - Hourly for 72 hours, learned from your roof: shading, local weather, panel groups — and forecast quality is measured, not claimed.
• Smart Charge - Charges the battery from the grid when electricity is cheap and the sun will not be enough — and leaves room for solar power otherwise.

MobileNet build with Tensorflow

ZenML 🙏: 파이프라인부터 에이전트까지 아우르는 하나의 AI 플랫폼. https://zenml.io.

Evaluation of Deep Learning Frameworks

Chess reinforcement learning by AlphaGo Zero methods.

基于Yolov5车牌检测,更快更准.

A tiny Transformer built from scratch in PyTorch, with an English-to-Chinese translation demo | 用 PyTorch 从零实现的小型 Transformer，附英译中演示，帮助理解模型原理

• One Python file: data processing, the model, training, and translation all live in transformer.py.
• Core components implemented by hand: scaled dot-product attention, multi-head attention, causal masking, and cross-attention.

使用机器学习算法完成对12306验证码的自动识别