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

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

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

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

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.

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

Webots Image Dataset Collection For Computer Vision And Deep Learning

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

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.

CNN model to classify garbage

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

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

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

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

This project uses a Convolutional Neural Network (CNN) to detect malaria from cell images. The model is built using TensorFlow/Keras and achieves high accuracy in detecting malaria, making it a potential tool for aiding in early diagnosis.

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.

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.

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