MOSES OLAFENWA's Projects
A dataset of images for artificial intelligence models to recognize human actions.
A dataset for detecting healthy and damaged apples
PyTorch code for BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation
ByteTrack: Multi-Object Tracking by Associating Every Detection Box
Code implementation of major Convolutional Neural Networks
The World's Leading Cross Platform AI Engine for Edge Devices
A custom DeepStack model for detecting 16 human actions.
A DeepStack custom model for detecting common objects in dark/night images and videos.
A DeepStack custom model for detecting 352 common logos
DenseNet Implementation in Keras with ImageNet Pretrained Models
A deep learning model for detecting fire in video and camera streams
Golang Practice
A collection of experiences utilizing machine learning models with Fritz
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 python library built to empower developers to build applications and systems with self-contained Computer Vision capabilities
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.
Deep Learning for humans
Python bindings for llama.cpp
Port of Facebook's LLaMA model in C/C++
[NeurIPS 2023 Oral] Visual Instruction Tuning: LLaVA (Large Language-and-Vision Assistant) built towards multimodal GPT-4 level capabilities.
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.
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.
A dataset of traffic, fire and accident images for training deep learning models.