The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNet-V3/V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more

Projekt u sklopu predmeta Analiza slika u biomedicini

EfficientNet-Based Classification of the Food101 Dataset

Insightface Keras implementation

Based on my published research paper, this project uses a "One-vs-All" deep learning approach with EfficientNet B4 to classify cassava leaf diseases. Integrated into an Android app, it helps farmers detect diseases early, supporting sustainable farming and reducing crop losses.

This is a Major Project undertaken for the fulfillment of a Bachelors Degree in Computer Engineering where People can interact with their computer devices using gestures

Python toolkit for speech processing

A comparative analysis of deep learning algorithms for multi label image classification using microscopic images.

A PyTorch implementation of "EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks".

Reimplementations of various computer vision papers in pytorch

Mobile application developed for detecting quality of fruits.

EfficientNetV2 (Efficientnetv2-b2) and quantization int8 and fp32 (QAT and PTQ) on CK+ dataset . fine-tuning, augmentation, solving imbalanced dataset, etc.

Binary classification, SHAP (Explainable Artificial Intelligence), and Grid Search (for tuning hyperparameters) using EfficientNetV2-B0 on Cat VS Dog dataset.

Breast Cancer H&E classification of Images and Image Generation

🍔🍟🍗 Food analysis baseline with Theseus. Integrate object detection, image classification and multi-class semantic segmentation 🍞🍖🍕

Comparative Analysis on Emotion Recognition using Deep Learning

A Simple Template For A PyTorch Classification Project 😄

Knowledge Distillation for Skin Lesion Classification

Welcome to the repository of our garbage classification project! We have developed a model using PyTorch and EfficientNet-B4 that classifies garbage into twelve different types. The model has achieved an impressive accuracy of 98.45%.

Computer Vision (Pytorch) Birds Classification