Search

Ultralytics YOLO27, YOLO26, YOLO11, YOLOv8 — object detection, instance segmentation, semantic segmentation, image classification, pose estimation, object tracking

Fast Rust library for PDF inspection, classification, and text extraction. Intelligently detects scanned vs text-based PDFs to enable smart routing decisions.

A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch

Ultralytics YOLOv5 in PyTorch for object detection, instance segmentation, classification, training, and export.

DSH Web UI plugin: auto-sends 「继续」 after non-human interruptions, with error classification, adaptive backoff, pause, idempotency/loop guards, resume templates, and stats—all configurable in settings UI | DSH 前端 UI 插件：非人为中断后自动发送「继续」，支持错误分类、自适应退避、暂停、幂等/循环保护、续跑模板与统计，均可在设置界面配置

A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

This project applies machine learning techniques in Python to analyze data, visualize feature relationships, and perform classification using models such as Decision Tree and Support Vector Machine. Model performance is evaluated through confusion matrices and graphical analysis.

Classify Kaggle Consumer Finance Complaints into 11 classes. Build the model with CNN (Convolutional Neural Network) and Word Embeddings on Tensorflow.

Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.

The second version of the Kraken taxonomic sequence classification system

The squeezenet image classification android example

Transformers for Information Retrieval, Text Classification, NER, QA, Language Modelling, Language Generation, T5, Multi-Modal, and Conversational AI

PyTorch Implementation Of WS-DAN(See Better Before Looking Closer: Weakly Supervised Data Augmentation Network for Fine-Grained Visual Classification)

AI based multi-label girl image classification system, implemented by using TensorFlow.

Metabuli: specific and sensitive metagenomic classification via joint analysis of DNA and amino acid.

A curated list of papers, code and resources pertaining to weak-shot classification, detection, and segmentation.

Attention temporal convolutional network for EEG-based motor imagery classification

TransferAttack is a pytorch framework to boost the adversarial transferability for image classification.

Autonomous penetration testing using a swarm of AI agents. Orchestrates recon, classification, exploitation, and reporting specialists with ReAct reasoning — supports bug bounty, continuous monitoring, and CTF modes. Built with Go and 7+ native security tools.

[CVPR26] TESSERA is a foundation model that can process time-series satellite imagery for applications such as land classification and canopy height prediction. Developed at the University of Cambridge, it enables efficient extraction of temporal patterns from Earth observation

ESAA-Security applies the ESAA architecture to automated security auditing. LLM-based agents execute a structured audit across 16 security domains, governed by the same immutable append-only event log, boundary contracts, and hash-verified projections that ESAA provides for software engineering workflows. Every finding, classification, and remediat

TRACER: replace 90%+ of your LLM classification calls with a traditional ML model. Formal parity guarantees. Self-improving.

Research into how agentic AI coding assistants work. Reconstructed prompt patterns, agent coordination, and security classification

LIDARLearn: A Unified Deep Learning Library for 3D Point Cloud Classification, Segmentation, and Self-Supervised Representation Learning

AI-powered agents for automating 3D content workflows using Vision-Language Models (VLMs). Content Agents analyze 3D assets and automate material assignment, physics property classification, and texture generation for USD files.

TabFM (Tabular Foundation Model) is a pretrained tabular foundation model developed by Google Research for tabular data regression and classification.

Zero-shot text classification over plain HTTP — no API key, no account. One Cloudflare Worker, a CLI, and an MCP server. https://classifier.dev

Fine-tunes of Qwen3.5 and Gemma 4 for zero-shot classification