以图搜图基于Towhee（resnet50 模型） + Milvus

ResNet50 for Image Classification

A breast cancer analysis project using advanced image processing techniques. This research employed CNNs, specifically VGG16, DenseNet121, and ResNet150V2, to analyze histopathological images from the BreakHis dataset on Kaggle, enhancing breast cancer detection and classification.

Basic recommendation system with mySQL database and API

A project on building deep learning classifier to classify playing cards

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

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.

A Large-Scale Dataset for Fish Segmentation and Classification Using Deep Learning Algorithms

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

cat-dog classifer

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.

Python-based solution for automatic image caption generation using a ResNet-50 CNN and RNN, featuring comprehensive data preprocessing, model training, and evaluation with BLEU score and Cosine Similarity metrics.

This project addresses the danger of distracted driving by developing a system that analyzes in-vehicle camera footage. Deep learning models, including self-trained Convolutional Neural Networks (CNNs) and pre-trained architectures like ResNet-50 and VGG-16, are used to identify drivers who are texting, using phones, reaching for objects or talking

Vehicle Identify