2D facial landmarks detection with neural networks
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Updated
Jun 8, 2023 - Jupyter Notebook
2D facial landmarks detection with neural networks
This project uses PyTorch to classify bone fractures. As well as fine-tuning some famous CNN architectures (like VGG 19, MobileNetV3, RegNet,...), we designed our own architecture. Additionally, we used Transformer architectures (such as Vision Transformer and Swin Transformer). This dataset is Bone Fracture Multi-Region X-ray, available on Kaggle.
Bushfires detector using mobilenet as feature extractor
Deep convolutional neural network implementation for brain tumor segmentation.
Biometric face tracking attendance system for tracking and verifying employee’s or student’s attendance using their unique facial characteristics
Face Recognition training and testing framework with tensorflow 2.0 based on the well implemented arcface-tf2. Changes are added to provide tensorflow lite conversion, and provide additional backbones, loss functions.
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MobileNetV3 implementation from scratch using PyTorch.
A computer vision-driven Face Mask Detection system, leveraging OpenCV alongside deep learning frameworks such as Tensorflow and Keras and Real-time social distancing surveillance using an IP camera, enhanced for superior performance through multi-threading
Introduction Course to Deep Learning using Google Colab.
대충 TFJS 쓰던 사람이 Python TF 배워보겠다고 만든 이미지 인식 프로젝트
Examining different architectures of famous artificial intelligence networks using fer2013 dataset
GUI testing automation and testing tools automation attempt
Background Replacement in Video Conferencing (IJNDI 2023)
Raspberry Pi Object Detection using MobileNetV3
This repository holds the downstream task of Face Mask Classification performed on Self Currated Custom Dataset with various State of the Art deep learning models like ViT, BeIT, DeIT, LeViT, ConvNeXt, VGG16, EfficientNetV2, RegNet and MobileNetV3.
Processing image advertisements to predict the context conveyed through them using CNNs. The images are further visualised using GradCAM to understand how the first and the last layers perceive the image dataset for the classification.
Implementation of MobileNetV3 in pytorch
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