MOSES OLAFENWA

A python library built to empower developers to build applications and systems with self-contained Computer Vision capabilities

A Python library that lets you add image and video analysis to your app with just a few lines of code, detect objects, classify images, or train a custom model on your own photos.

A deep learning model for detecting fire in video and camera streams

IdenProf dataset is a collection of images of identifiable professionals. It is been collected to enable the development of AI systems that can serve by identifying people and the nature of their job by simply looking at an image, just like humans can do.

A dataset of traffic, fire and accident images for training deep learning models.

A DeepStack custom model for detecting common objects in dark/night images and videos.

A dataset of images for artificial intelligence models to recognize human actions.

A custom DeepStack model for detecting 16 human actions.

A DeepStack custom model for detecting 352 common logos

Official English Documentation of ImageAI

IntelliP (Intelligent Photos) is a Windows photo gallery that intelligently organizes the pictures in your computer into 12 unique and related categories.

A project developed and maintained as part of the aim at bringing current capabilities in machine learning and artificial intelligence into practical use for non-programmers and average computer users.

A collection of experiences utilizing machine learning models with Fritz

A dataset for detecting healthy and damaged apples

Deep Learning for humans

Code implementation of major Convolutional Neural Networks

3D Capability and Intelligence for the real world.

ByteTrack: Multi-Object Tracking by Associating Every Detection Box

The World's Leading Cross Platform AI Engine for Edge Devices

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Golang Practice

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DenseNet Implementation in Keras with ImageNet Pretrained Models

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Repository top show you how to setup and use Apache's wealth of OSS projects

Port of Facebook's LLaMA model in C/C++

Python bindings for llama.cpp

The repository provides code for running inference and training for "Segment and Caption Anything" (SCA) , links for downloading the trained model checkpoints, and example notebooks / gradio demo that show how to use the model.

[NeurIPS 2023 Oral] Visual Instruction Tuning: LLaVA (Large Language-and-Vision Assistant) built towards multimodal GPT-4 level capabilities.

PyTorch code for BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation