A powerful and easy-to-use web scrapper for collecting data from the web. Supports scraping of images, text, videos, meta data, and more. Ideal for machine learning and deep learning engineers. Download and extract data with just one line of code

The binary classification problem focused on first IEEE Image forensics challenge-phase 1, to predict the given image is pristine or manipulated/edited/fake. Comparing CNN & Transfer Learning models for the problem and boosting the performance by feature extraction

An image downloader for https://pikwizard.com using Selenium. Copyright-free image downloader.

The goal of this project is to build a neural network that takes an MNIST handwritten digit (0-9) image and a random number (digit 0-9) as inputs and returns the predicted class label (0-9) for the input image and its addition (sum) with the input random number as summed output (range 0-18) label as outputs.

Successfully established a deep learning model which can precisely classify a human being as a child or an adult.

Successfully established a deep learning model which can accurately predict whether a woman's face is beautiful or average.

Webots Image Dataset Collection For Computer Vision And Deep Learning

A practice on improving CNN model accuracy by Image Data Generator or transfer learning when train model with a small dataset for binary classification.

(ImageDataGenerator) is Keras deep learning library provides the ability to use data augmentation automatically when training a model.

A python module to generate synthetic images from 3D models, for use in image detection/segmentation tasks.

Image Data Augmentation with Keras and Image data generator with keras model.

In this X-ray classification assignment, we built a deep learning model to classify chest X-ray images into "nofinding" and "effusion" classes. We tackled challenges like data augmentation, imbalanced classes, and used weighted cross-entropy to improve model performance. The goal was to identify abnormalities with high accuracy.

In this dataset we are provided with images that belong to 4 classes : diseased leaf , diseased plant , fresh leaf and fresh plant. The objective of this study is to create a CNN model to help us predict whether these image of the leaf/plant belong to the diseased category or the healthy category.

manual image labeller (with human level accracy 😉). diy

CNN model to classify garbage

A basic introduction to learning CNN through applications of VGG models.

Transform TV control with Gesture Recognition! Enable intuitive interaction with smart TVs using gestures built using Conv3D, CNN & RNN

The standard approach to image reconstruction using deep learning is to use clean image priors for training purposes. In this project, we attempt to achieve denoising without using a clean image prior and yet, achieving a performance comparable to, or sometimes, even better than that obtained using the conventional approach.

Train a computer to play Rock-Paper-Scissors by teaching it to recognize hand gestures using images! (Great for machine learning beginners) 😎