inceptionv3

A python library built to empower developers to build applications and systems with self-contained Computer Vision capabilities

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)

Keras model of NSFW detector

Classification models trained on ImageNet. Keras.

InceptionTime: Finding AlexNet for Time Series Classification

food image to recipe with deep convolutional neural networks.

Sign Language Gesture Recognition From Video Sequences Using RNN And CNN

Mobile AI Compute Engine Model Zoo

Core ML demo app with Unsplash API

Simple sign language alphabet recognizer using Python, openCV and tensorflow for training Inception model (CNN classifier).

A neural network to generate captions for an image using CNN and RNN with BEAM Search.

AI Papers

A Multiclass Weed Species Image Dataset for Deep Learning

Detecting Pneumonia in Chest X-ray Images using Convolutional Neural Network and Pretrained Models

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 upload Chest X-rays or CT Scans and get the output of possibility of COVID infection.

Deploying Keras models using TensorFlow Serving and Flask

BEST SCORE ON KAGGLE SO FAR , EVEN BETTER THAN THE KAGGLE TEAM MEMBER WHO DID BEST SO FAR. The project is about diagnosing pneumonia from XRay images of lungs of a person using self laid convolutional neural network and tranfer learning via inceptionV3. The images were of size greater than 1000 pixels per dimension and the total dataset was tagged large and had a space of 1GB+ . My work includes self laid neural network which was repeatedly tuned for one of the best hyperparameters and used variety of utility function of keras like callbacks for learning rate and checkpointing. Could have augmented the image data for even better modelling but was short of RAM on kaggle kernel. Other metrics like precision , recall and f1 score using confusion matrix were taken off special care. The other part included a brief introduction of transfer learning via InceptionV3 and was tuned entirely rather than partially after loading the inceptionv3 weights for the maximum achieved accuracy on kaggle till date. This achieved even a higher precision than before.

This is the code repository for my Medium post "Understanding your Convolution network with Visualizations"

Supervised Classification of bird species 🐦 in high resolution images, especially for, Himalayan birds, having diverse species with fairly low amount of labelled data [ICVGIPW'18]

Transfer Learning with DCNNs (DenseNet, Inception V3, Inception-ResNet V2, VGG16) for skin lesions classification