Popular GitHub Repositories for Black Box Explanations

Enhancing code comprehension with AI-driven explanations and a secure code snippet repository.

FastGPT is a knowledge-based platform built on the LLMs, offers a comprehensive suite of out-of-the-box capabilities such as data processing, RAG retrieval, and visual AI workflow orchestration, letting you easily develop and deploy complex question-answering systems without the need for extensive setup or configuration.

BoxMOT: pluggable SOTA tracking modules for segmentation, object detection and pose estimation models

[ECCV 2022] ByteTrack: Multi-Object Tracking by Associating Every Detection Box

Accompanying source code for Machine Learning with TensorFlow. Refer to the book for step-by-step explanations.

Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation

A library for prototyping realtime hand detection (bounding box), directly in the browser.

Deep learning software for colorizing black and white images with a few clicks.

Just draw a bounding box and you can remove the object you want to remove.

A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.

Algorithms for explaining machine learning models

基于图像识别的自动化黑盒测试框架 | An automation black-box testing framework based on image recognition

?? Build Your Own chatgpt Bot|? Discord/Slack/Kook/Telegram |? ToolCall|? Plugin Support | ? out-of-box | gpt-4o

OneDiff: An out-of-the-box acceleration library for diffusion models.

MobileNetV1, MobileNetV2, VGG based SSD/SSD-lite implementation in Pytorch 1.0 / Pytorch 0.4. Out-of-box support for retraining on Open Images dataset. ONNX and Caffe2 support. Experiment Ideas like CoordConv.

Official Repo for "TheoremExplainAgent: Towards Multimodal Explanations for LLM Theorem Understanding"

C++-based high-performance parallel environment execution engine (vectorized env) for general RL environments.

Data Augmentation For Object Detection

Object Detection Metrics. 14 object detection metrics: mean Average Precision (mAP), Average Recall (AR), Spatio-Temporal Tube Average Precision (STT-AP). This project supports different bounding box formats as in COCO, PASCAL, Imagenet, etc.

Coloring black and white images with deep learning.