restnet50

以图搜图基于Towhee（resnet50 模型） + Milvus

ResNet50 for Image Classification

Deep Learning based Skin Cancer Detection using multiple CNN architectures (VGG, ResNet, DenseNet, EfficientNet, Inception) with image preprocessing using ESRGAN and performance comparison for clinical AI research.

Basic recommendation system with mySQL database and API

Open source project for waste detection developed by students of postgraduate course Artificial Intelligence with Deep Learning UPC

cat-dog classifer

Python script leveraging pre-trained ResNet18 for extracting video features from the YouTube Dataset, enabling LSTM-based action recognition models.

Context Understanding from Videos analyzes video content by extracting frames and audio, then detecting objects, faces, emotions, and actions. It uses Python with OpenCV, MoviePy, and YOLO. Future plans include embedding models for improved context analysis.

Atreus is an advanced bot designed to play the popular game GTA 5 using cutting-edge computer vision and deep learning techniques. By implementing AlexNet, a deep convolutional neural network, Atreus can interpret game visuals and make strategic decisions in real-time.

we classify the images with keras pre trained models like vgg 16 model ,Resnet-50 model and inception-v3 model

Deep learning classifier (ResNet-50, 87.2% Val Acc) predicting 8 blood group types from fingerprints with Grad-CAM heatmaps & FastAPI.

An AI-driven system that monitors student attentiveness during online lectures using computer vision. It analyzes facial expressions, eye movement, and head pose to provide real-time insights into engagement levels

💡Utilizing deep learning techniques 🧠 and models such as ResNet50, VGG16, ResNet101, VGG19, DenseNet201, EfficientNetB4, and MobileNetV2 🤖 through transfer learning and fine-tuning 🔧 to improve lung cancer detection from CT scans 🏥.

A FastAPI-powered web application using a fine-tuned ResNet50 model to analyze HAM10000 dermatoscopic images with 80%+ accuracy.

Final Submission Repo for Day 1 Finale Round 1

Developing a RESTful API using FastAPI to accept an image and return the image type using the ResNet50 (ImageNet) model.

VisionInspect AI helps automate quality inspection by detecting defective products from images using a fine-tuned ResNet50 model, with FastAPI and Docker for deployment.

Image captioning on Pascal VOC 2012 with a ResNet-50 + LSTM encoder–decoder, trained on synthetic captions generated from object annotations.

Full-stack AI diagnostic tool — ResNet-50 chest X-ray classifier with Grad-CAM explainability, Flask REST API, and HIPAA-aware confidence thresholding

A machine learning-based system for detecting and classifying skin diseases through image analysis, utilizing deep learning models and classification pipelines.