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 basic introduction to learning CNN through applications of VGG models.

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

CNN model to classify garbage

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) 😎

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.

A supermarket chain called Good Seed wanted to see if Data Science could help them comply with the law by ensuring that they did not sell age-restricted products to underage customers. My task was to build and evaluate a model to verify a person's age.

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.

Webots Image Dataset Collection For Computer Vision And Deep Learning

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

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.

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

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