mobilnet-v2 - 技术专题

Caffe Implementation of Google's MobileNets (v1 and v2)

The official PyTorch implementation for paper "Hierarchical Domain-Adapted Feature Learning for Video Saliency Prediction"

Covid19 Face Mask Detector

Android pet detector with tensorflow models

Implementing and comparing ResNet50 and MobileNetV2 transfer learning models using the MNIST:HAM10000 image dataset. Resulting classification accuracy of ~90%.

The TasterAI is a neural network model for food categorization. It consists of a database of 15 types of food.

Classification for flowers Images using Deep learning [ Convolutional Neural Network] and using Transfer learning [ Mobilnet ] .

This project is a work of fiction written from the perspective of a 2020 researcher traveling back in time to mid 2013 to share some 2020 xNNbased application ideas; references to credit the actual inventors of the various ideas is provided at the end

Обнаружие маски на лице в реальном времени

Flower classification with transfer learning: MobileNetV2 feature extraction, then fine-tuning, going from 83.8% to 92.1% test accuracy on TF Flowers (5 classes).

This application detects a speed sign board on the roads live. It warns the driver when a 30 zone board is detected.

Binary image classifier built with TensorFlow/Keras and MobileNetV2 transfer learning, achieving 85-90% test accuracy. Features two-phase fine-tuning, data augmentation, and full evaluation metrics (accuracy curves, confusion matrix). Trained on a custom dataset combining Tiny Bird Species and public insect images.

DeepRipen is a YOLOv8 + MobileNet based deep learning system for automatic arecanut bunch detection and ripeness classification in real-world agricultural environments.

Real-time hand gesture recognition system using MobileNetV2, MediaPipe and OpenCV.

for binary image classification: detecting real vs forged handwritten signatures.

A real-time driver monitoring system using OpenCV and deep learning to detect drowsiness and alertness.

This project benchmarks vision (MobileNet, ResNet20) and NLP (DistilBERT) models across server vs. client inference, using backend servers and ONNX-converted local models. It evaluates latency, UX, accuracy, and performance, dedicated to fulfilling the Master’s thesis in AI & ML.

This project uses deep learning models to classify plant leaf diseases using the PlantVillage dataset, evaluating four architectures: Simple CNN, VGG16 (Transfer Learning), Deep CNN, MobileNetV2 (Transfer Learning + Fine-tuning)

The Project is about detecting Face Mask using MobileNetv2 and a Caffee Model for detecting a face.