Training and evaluating state-of-the-art deep learning CNN architectures for plant disease classification task.
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Updated
Oct 13, 2021 - Python
Training and evaluating state-of-the-art deep learning CNN architectures for plant disease classification task.
Convolutional Neural Network (CNN) for text classification implemented with PyTorch and TorchText
PyTorch Practice Notebooks
Implemented fully-connected DNN of arbitrary depth with Batch Norm and Dropout, three-layer ConvNet with Spatial Batch Norm in NumPy. The update rules used for training are SGD, SGD+Momentum, RMSProp and Adam. Implemented three block ResNet in PyTorch, with 10 epochs of training achieves 73.60% accuracy on test set.
DogBreedSpotter is a Python-based image classification project designed to identify and classify dog breeds in images. This project utilizes deep learning models, including convolutional neural networks (CNNs) such as VGG, AlexNet, and ResNet, to accurately detect whether an image contains a dog and, if so, determine the breed.
Final Project of Udacity's PyTorch Scholarship Challenge Nanodegree Program
Repository to archive and document work done for satisfying Capstone Project requirements at Praxis Business School.
This is a deep learning model that classifies shape images.
Photo sharing and photo storage services like to have location data for each photo that is uploaded. With the location data, these services can build advanced features, such as automatic suggestion of relevant tags or automatic photo organization, which help provide a compelling user experience.
Codes crafted during the PyTorch Scholarship Challenge, from Facebook and Udacity
My personal AI projects. All of them are .ipnyb files which are pytorch-dependant.
A Binary Image Classifier in PyTorch that classifies images into Ship or Truck
Pytorch and FastAI CNN classifer for plant disease
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