Popular GitHub Repositories for Cnn For Visual Recognition

This deep learning application can detect Facial Keypoints (15 unique points). They mark important areas of the face - the eyes, corners of the mouth, the nose, etc.

TensorFlow Lite object detection example for Raspberry Pi Zero

Python module for face recognition with OpenCV and Deep Learning.

The project aims at building a machine learning model that will be able to classify the various hand gestures used for fingerspelling in sign language. In this user independent model, classification machine learning algorithms are trained using a set of image data and testing is done. Various machine learning algorithms are applied on the datasets, including Convolutional Neural Network (CNN).

Semantic neural network to realize pixel-wise classification of 2D nano-material using Matlab

Convolutional Neural Network (CNN) was trained on 48x48 pixel grayscale images to predict 5 different emotions from images. Ten different models with different settings were trained to find the best model and The best model was able to predict 5 emotions from images with 88% training accuracy and 70% testing accuracy.

Deep learning classification and generation of pocket monster

Facial detection using CNN and openCV

Fine-tuned EfficientNetV2B0, for binary Image_Classification, with Gradio.

CNN - object alignment, object detection, classification & recognition GAN - object generation

An app for classifying/tagging a given image of a famous landmark using CNN

SpeedServAI is a machine learning project that tracks tennis players' speed, position, and ball speed in real-time video. Using YOLO for object detection and CNNs for feature extraction, it provides accurate, real-time insights into match dynamics, enhancing performance analysis .

Build a custom CNN model using pre-trained models to develop a state of the art image recognition system to control quality and improve production in the supply chain process using deep learning

Implementa un sistema para clasificar imágenes en diferentes categorías utilizando técnicas avanzadas de aprendizaje profundo. Combina clasificadores lineales, redes neuronales convolucionales y modelos preentrenados como EfficientNet, con procesamiento y visualización realizados mediante PyTorch.

Malaria detection using Deep Learning. CNN, VGG-19 and ResNet50.

TensorFlow Lite object detection example for Raspberry Pi 2+

VGG 19 is a Very Deep Convolutional Networks for Large-Scale Image Recognition In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.We use the network with pretrained parameters to classify images on ImageNet.