💎A high level pipeline for face landmarks detection, it supports training, evaluating, exporting, inference(Python/C++) and 100+ data augmentations, can easily install via pip.

Classify Skin cancer from the skin lesion images using Image classification. The dataset for the project is obtained from the Kaggle SIIM-ISIC-Melanoma-Classification competition.

The service for the demonstration of transforms in Albumentations library

Lightweight & fast OCR models for license plate text recognition.

Augmentation package for 3d data based on albumentaitons

My solution to the Global Data Science Challenge

画像データ拡張ライブラリAlbumentationsのJupyter上での実行例。

Image data augmentation scheduler for albumentations transforms

Object detection and instance segmentation on MaskRCNN with torchvision, albumentations, tensorboard and cocoapi. Supports custom coco datasets with positive/negative samples.

Clean, reproducible, boilerplate-free deep learning project template.

Modification of PyTorch implementation of YOLOv3 Object Detection.

A high-performance image processing library designed to optimize and extend the Albumentations library with specialized functions for advanced image transformations. Perfect for developers working in computer vision who require efficient and scalable image augmentation.

This is a capstone project on a real dataset related to segmenting low-grade glioma. This capstone project is included in the UpSchool Machine Learning & Deep Learning Program in partnership with Google Developers.

Sample implementation codes for a variety of popular image augmentation Python packages

First position in Gran Canary Datathon 2021

Source code of top 3% solution for the Kaggle APTOS 2019 Blindness Detection challenge.

Projet de Classification d'Images de Fruits

Implemented Unet++ models for medical image segmentation to detect and classify colorectal polyps.

Our project uses state-of-the-art deep learning techniques to tackle a vital medical task: polyp segmentation from colonoscopy images. We harness the Unet++ architecture and a robust tech stack to precisely detect and isolate polyps, advancing healthcare diagnostics and patient care. 🏥💡

Build a computer vision-based technology to process and detect the potholes present in an image.