A react application with a deep learning model to generate caption for images
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Updated
May 11, 2024 - Jupyter Notebook
A react application with a deep learning model to generate caption for images
This repository contains code for comparing and evaluating various CNN classification models on a waste image dataset.
Xception model predict dog breeds from dog picture , flask web site
This GitHub repository contains instructions for downloading and utilizing the AI4Food-NutritionDB food image database, as well as different food recognition systems based on Xception and EfficientNetV2 architectures.
A Python-based computer vision and AI system for skin disease recognition and diagnosis. Led end-to-end project pipeline, including data gathering, preprocessing, and training models. Utilized Keras, TensorFlow, OpenCV, and other libraries for image processing and CNN models, showcasing expertise in deep learning and machine learning techniques.
Stanford dogs dataset breed classification with Xception (CNN)
Workshop CDK Template to provision infra for the Deep Visual Search workshop
Transfer Learning models in PyTorch
Notebooks of pre trained models using the HAM10000 dataset
Address the crowd counting problem on the Mall dataset (sparse) by exploring regression-based (Xception) and density-based (CSRNet) approaches.
Deep fake detection using cnn, Xception, Denesenet121, GAN on four different datasets.
COVID-19 Detection Chest X-rays and CT scans: COVID-19 Detection based on Chest X-rays and CT Scans using four Transfer Learning algorithms: VGG16, ResNet50, InceptionV3, Xception. The models were trained for 500 epochs on around 1000 Chest X-rays and around 750 CT Scan images on Google Colab GPU. A Flask App was later developed wherein user can…
Pothole Detection Using Transfer Learning Models: A Comparative Study
Explore the Standard OCR Project: a deep learning-based character recognition system leveraging advanced computer vision techniques. Detect characters in images using ResNet, Xception, Inception, and MobileNet models. Train, evaluate, and contribute to this cutting-edge technology.
Leverage TensorFlow, Keras, and Xception to train a predictive model with the provided dataset. Once the model is trained, it can be utilized tflite to make predictions. For deployment, upload the model to AWS ECR and employ AWS Lambda for model execution.
Fine-tuning xception model to classify 6 types of jellyfish
Classification of flowers using Convolutional Neural Network
Practice on cifar100(ResNet, DenseNet, VGG, GoogleNet, InceptionV3, InceptionV4, Inception-ResNetv2, Xception, Resnet In Resnet, ResNext,ShuffleNet, ShuffleNetv2, MobileNet, MobileNetv2, SqueezeNet, NasNet, Residual Attention Network, SENet, WideResNet)
Medical Image classification using Convolutional Neural Networks
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