vggnet

Deep Learning Course Assignment on CNNs and NetVLAD

Multi class Deep learning model

Using VGG and Transfer Learning in Keras

Insights and Analysis - Using Various Deep Learning Architectures on Image Classification Datasets

Building and experimenting with architectures like VGG, ResNet and AlexNet to understand their efficiencies in classifying dog breeds(AWS Nanodegree Project)

VGGnet CNN

This repository features our Work-In-Progress ASl fingerspelling hand pose recognition pipeline. Our formal repo is at mutual-ProjectLink.

Block-by-block implementation from scratch of CNNs and GANs using PyTorch, with language models to be added later.

This repository contains code for classfying images from KAN based VGG11 model with prototypical networks.

慕课学习TensorFlow

Image Detection using TensorFlow 2.0 and VGG16 pre-trained model

This repository contains an implementation of the VGG-16 convolutional neural network trained on the Tiny ImageNet dataset using PyTorch. The script includes advanced data augmentation, learning rate scheduling, and evaluation routines designed for effective training on a mid-sized image classification dataset.

A deep learning–based multiclass image classification system that uses pre-trained models (VGG16 and MobileNet) to accurately identify different fruit categories from images.

As a big Simpsons fan, I have watched a lot (and still watching) of The Simpson episodes -multiple times each- over the years. I wanted to build a neural network which can recognize characters.

A Deep learning computer vision project that classifies weather conditions from images using VGG16 transfer learning. The pipeline includes advanced preprocessing, structured data splitting, feature extraction, and progressive fine-tuning. The model achieves strong generalization with optimized training, regularization, and performance analysis.

A comprehensive implementation of deep learning for computer vision using Python and TensorFlow

This project is a real-time traffic sign recognition system built using Python, OpenCV, and a pre-trained CNN model, capable of detecting and recognizing traffic signs from images.

A sleek Streamlit app for flower enthusiasts. With a choice between a custom-trained model and a fine-tuned ResNet50, users can effortlessly classify flowers—roses, daisies, dandelions, sunflowers, and tulips. Upload or pick random images to witness the magic of AI-driven classification.

vanilla vqa_v1 PyTorch implementation