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A coding-free framework built on PyTorch for reproducible deep learning studies. PyTorch Ecosystem. 🏆28 knowledge distillation methods presented at TPAMI, CVPR, ICLR, ECCV, NeurIPS, ICCV, AAAI, etc are implemented so far. 🎁 Trained models, training logs and configurations are available for ensuring the reproducibiliy and benchmark.

The Python ensemble sampling toolkit for affine-invariant MCMC

  • python
  • mcmc
  • mcmc-sampler
  • probabilistic-data-analysis

[CVPR 2023] Official implementation of the paper "Mask DINO: Towards A Unified Transformer-based Framework for Object Detection and Segmentation"

  • instance-segmentation
  • object-detection
  • panoptic-segmentation
  • semantic-segmentation

LLM-compiled knowledge bases for any AI agent. Parallel multi-agent research, thesis-driven investigation, source ingestion, wiki compilation, querying, and artifact generation.

InternAgent-1.5: A Unified Agentic Framework for Long-Horizon Autonomous Scientific Discovery

  • agentic-framework
  • ai-scientists
  • automatic-paper-survey
  • automatic-scientific-discovery

MetPy is a collection of tools in Python for reading, visualizing and performing calculations with weather data.

  • python
  • atmospheric-science
  • meteorology
  • weather

IT 140 Module Two assignment for developing a beginner Python name-and-age program while practicing the Software Development Life Cycle (SDLC), including analysis, design, construction, testing, and debugging.

  • beginner-programming
  • computer-science-education
  • introduction-to-programming
  • programming-education

TranslatedA completion of the landscape-format full character reference sheet based on the ZipZipPipe (胖次狐, "Panties Fox") AI girl character art: it is broken down into separate assets for three-view turnarounds / expressions / outfits / props, the side and back views are inferred and completed, and everything is stitched together at native pixels 1:1. CC BY-NC-SA 4.0, non-commercial.

  • ai-art
  • anime
  • bilibili
  • cc-by-nc-sa

AgML is a centralized framework for agricultural machine learning. AgML provides access to public agricultural datasets for common agricultural deep learning tasks, with standard benchmarks and pretrained models, as well the ability to generate synthetic data and annotations.