Deep Learning
Showing 36 of 937 projects
FLAME dataset and deep learning models for fire detection in aerial imagery using UAVs, supporting classification and segmentation tasks.
An end-to-end Python outlier detection system with database support, automated machine learning, and unified APIs for statistical, ML, and deep learning models.
A lightweight neural network library for Deno with CPU, GPU, and WASM backends, designed for serverless and edge environments.
A curated list of open-source software tools for medical imaging research, including segmentation, visualization, and deep learning libraries.
Ankh is a state-of-the-art protein language model for general-purpose protein modeling and engineering tasks.
A transformer-based model for predicting drug-target interactions using substructural pattern mining and augmented transformer encoders.
Neural machine translation between Shakespearean and modern English using TensorFlow.
A PyTorch frontend for JAX that enables running PyTorch code on TPUs and provides seamless PyTorch-JAX interoperability.
A real-time object-level reconstruction system for 6D pose estimation using volumetric fusion and multi-object reasoning.
Automated 3D cell detection and classification in large-scale volumetric brain images using deep learning.
A ROS2 intelligent visual grasp solution for industrial robots, integrating OpenVINO grasp detection with MoveIt motion planning.
A PyTorch-based Python package for deep and machine learning analysis of microscopy data, designed for domain scientists.
A self-supervised deep learning model for extrinsic calibration between LiDAR and camera sensors using 3D spatial transformer networks.
A Python-based CAPTCHA breaking solution using Keras and OpenCV, developed for a data science competition.
A foundation model for cell segmentation that achieves state-of-the-art performance across diverse cellular targets and imaging modalities.
A deep learning model that reads IRCTC captchas with 98% accuracy, demonstrating their vulnerability to automated booking.
CUDA backend implementation for Torch's neural network package, enabling GPU acceleration for deep learning models.
A proof-of-concept neural network library in Rust with implementations for MNIST digit recognition and char-rnn LSTM models.
A Python library for building lazy data processing and machine learning workflows that handle datasets larger than memory.
A TensorFlow CNN implementation for Chinese character recognition, achieving 92.5% top-1 accuracy with batch normalization.
A benchmark dataset and meta self-learning method for multi-source domain adaptation in scene text recognition.
A vision transformer architecture that aggregates nested local transformers on image blocks for better accuracy, data efficiency, and convergence.
A Scala and JVM machine learning toolbox for research, education, and industry with an interactive REPL and end-to-end pipelines.
A PyTorch-based segmentation toolbox for electron microscopy connectomics, enabling neural structure analysis in 3D volumes.
A layer library for JAX-based machine learning projects, optimized for large-scale ML.
A pure Crystal machine learning library for building and training neural networks with CPU/GPU support and PyTorch compatibility.
A simulation-based deep learning approach to enhance the resolution of 3D lidar point clouds for ground vehicles.
A collection of optimization algorithms and logging utilities for Torch machine learning models.
A lightweight multilayer perceptron neural network library for MicroPython, designed for embedded systems like ESP32 and Pycom modules.
A Python library for logging ML metrics, parameters, and models in simple file formats, compatible with DVC and Git.
A Torch7 package providing extended neural network modules, criterions, and utilities for deep learning research.
A simple, flexible, and extensible object-oriented template for PyTorch projects.
A Docker container for face detection using Faster R-CNN deep learning, processing videos and images with bounding box outputs.
Shallow and deep convolutional neural networks for predicting visual saliency in images using a data-driven approach.
A curated collection of tools, tutorials, models, and resources for mastering TensorFlow.js.
Integrates Intel OpenVINO with ROS 2 for efficient deep learning inference in computer vision applications on Intel hardware.
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