PyTorch deep learning projects made easy.

Tez is a super-simple and lightweight Trainer for PyTorch. It also comes with many utils that you can use to tackle over 90% of deep learning projects in PyTorch.

PyTorch 深度学习实战项目集 本项目汇集了多个基于 PyTorch 的深度学习实战案例，涵盖图像分类、文本分类、图像生成、强化学习等任务。每个项目均包含详细注释和完整代码流程，适合学习和实践 PyTorch 的开发者使用。持续更新中，欢迎 Star 和贡献！

A Cookiecutter template for PyTorch Deep Learning projects.

A flexible template, as a quick setup for deep learning projects in pytorch-lightning

A PyTorch collection of deep learning beginners projects

This column has compiled 100 Examples of PyTorch Deep Learning Projects. It contains a variety of deep learning projects, including their principles and source code. Each project instance comes with a complete code + data set.

A collection of deep learning projects implemented in Python, focusing on neural networks, computer vision, and NLP using frameworks like TensorFlow and PyTorch. Includes practical examples, datasets, and tutorials for building and evaluating advanced machine learning models.

I’m Amirhosein Honardoust, a passionate developer and data scientist with experience in Python, PyTorch, deep learning, and machine learning projects across vision, NLP, and forecasting. I enjoy turning data into insights and building portfolio-ready AI applications that combine practicality with creativity.

Image Classifier Going forward, AI algorithms will be incorporated into more and more everyday applications. For example, you might want to include an image classifier in a smartphone app. To do this, you'd use a deep learning model trained on hundreds of thousands of images as part of the overall application architecture. A large part of software development in the future will be using these types of models as common parts of applications. In this project, you'll train an image classifier to recognize different species of flowers. You can imagine using something like this in a phone app that tells you the name of the flower your camera is looking at. In practice, you'd train this classifier, then export it for use in your application. We'll be using this dataset of 102 flower categories. When you've completed this project, you'll have an application that can be trained on any set of labelled images. Here your network will be learning about flowers and end up as a command line application. But, what you do with your new skills depends on your imagination and effort in building a dataset. This is the final Project of the Udacity AI with Python Nanodegree Prerequisites The Code is written in Python 3.6.5 . If you don't have Python installed you can find it here. If you are using a lower version of Python you can upgrade using the pip package, ensuring you have the latest version of pip. To install pip run in the command Line python -m ensurepip -- default-pip to upgrade it python -m pip install -- upgrade pip setuptools wheel to upgrade Python pip install python -- upgrade Additional Packages that are required are: Numpy, Pandas, MatplotLib, Pytorch, PIL and json. You can donwload them using pip pip install numpy pandas matplotlib pil or conda conda install numpy pandas matplotlib pil In order to intall Pytorch head over to the Pytorch site select your specs and follow the instructions given. Viewing the Jyputer Notebook In order to better view and work on the jupyter Notebook I encourage you to use nbviewer . You can simply copy and paste the link to this website and you will be able to edit it without any problem. Alternatively you can clone the repository using git clone https://github.com/fotisk07/Image-Classifier/ then in the command Line type, after you have downloaded jupyter notebook type jupyter notebook locate the notebook and run it. Command Line Application Train a new network on a data set with train.py Basic Usage : python train.py data_directory Prints out current epoch, training loss, validation loss, and validation accuracy as the netowrk trains Options: Set direcotry to save checkpoints: python train.py data_dor --save_dir save_directory Choose arcitecture (alexnet, densenet121 or vgg16 available): pytnon train.py data_dir --arch "vgg16" Set hyperparameters: python train.py data_dir --learning_rate 0.001 --hidden_layer1 120 --epochs 20 Use GPU for training: python train.py data_dir --gpu gpu Predict flower name from an image with predict.py along with the probability of that name. That is you'll pass in a single image /path/to/image and return the flower name and class probability Basic usage: python predict.py /path/to/image checkpoint Options: Return top K most likely classes: python predict.py input checkpoint ---top_k 3 Use a mapping of categories to real names: python predict.py input checkpoint --category_names cat_To_name.json Use GPU for inference: python predict.py input checkpoint --gpu Json file In order for the network to print out the name of the flower a .json file is required. If you aren't familiar with json you can find information here. By using a .json file the data can be sorted into folders with numbers and those numbers will correspond to specific names specified in the .json file. Data and the json file The data used specifically for this assignemnt are a flower database are not provided in the repository as it's larger than what github allows. Nevertheless, feel free to create your own databases and train the model on them to use with your own projects. The structure of your data should be the following: The data need to comprised of 3 folders, test, train and validate. Generally the proportions should be 70% training 10% validate and 20% test. Inside the train, test and validate folders there should be folders bearing a specific number which corresponds to a specific category, clarified in the json file. For example if we have the image a.jpj and it is a rose it could be in a path like this /test/5/a.jpg and json file would be like this {...5:"rose",...}. Make sure to include a lot of photos of your catagories (more than 10) with different angles and different lighting conditions in order for the network to generalize better. GPU As the network makes use of a sophisticated deep convolutional neural network the training process is impossible to be done by a common laptop. In order to train your models to your local machine you have three options Cuda -- If you have an NVIDIA GPU then you can install CUDA from here. With Cuda you will be able to train your model however the process will still be time consuming Cloud Services -- There are many paid cloud services that let you train your models like AWS or Google Cloud Coogle Colab -- Google Colab gives you free access to a tesla K80 GPU for 12 hours at a time. Once 12 hours have ellapsed you can just reload and continue! The only limitation is that you have to upload the data to Google Drive and if the dataset is massive you may run out of space. However, once a model is trained then a normal CPU can be used for the predict.py file and you will have an answer within some seconds. Hyperparameters As you can see you have a wide selection of hyperparameters available and you can get even more by making small modifications to the code. Thus it may seem overly complicated to choose the right ones especially if the training needs at least 15 minutes to be completed. So here are some hints: By increasing the number of epochs the accuracy of the network on the training set gets better and better however be careful because if you pick a large number of epochs the network won't generalize well, that is to say it will have high accuracy on the training image and low accuracy on the test images. Eg: training for 12 epochs training accuracy: 85% Test accuracy: 82%. Training for 30 epochs training accuracy 95% test accuracy 50%. A big learning rate guarantees that the network will converge fast to a small error but it will constantly overshot A small learning rate guarantees that the network will reach greater accuracies but the learning process will take longer Densenet121 works best for images but the training process takes significantly longer than alexnet or vgg16 *My settings were lr=0.001, dropoup=0.5, epochs= 15 and my test accuracy was 86% with densenet121 as my feature extraction model. Pre-Trained Network The checkpoint.pth file contains the information of a network trained to recognise 102 different species of flowers. I has been trained with specific hyperparameters thus if you don't set them right the network will fail. In order to have a prediction for an image located in the path /path/to/image using my pretrained model you can simply type python predict.py /path/to/image checkpoint.pth Contributing Please read CONTRIBUTING.md for the process for submitting pull requests. Authors Shanmukha Mudigonda - Initial work Udacity - Final Project of the AI with Python Nanodegree

Tutorials on how to engineer Machine Learning projects using Deep Neural Networks with PyTorch and Python

Training wheels, side rails, and helicopter parent for your Deep Learning projects in PyTorch

A comprehensive repository of PyTorch tutorials, projects, and examples to master deep learning concepts and model implementation. Covers neural networks, computer vision, and NLP with hands-on code and detailed explanations for learners.

Deep Learning Projects using Python & Pytorch

Deep Learning Projects with PyTorch [video], published by Packt

A repo about deep learning and PyTorch covering basics, projects and ideas

No description available

A boilerplate for seamlessly integrating PyTorch's Distributed Data Parallel (DDP) with SLURM job scheduling and Weights and Biases. Kickstart your scalable deep learning projects on HPC clusters.

Production-ready template for PyTorch Deep Learning Projects

Personal repository used to save and document different projects I've worked on. Most of the projects are deep learning projects in Tensorflow or PyTorch.

A progressive, highly extensible and developer-friendly framework for building deep learning projects based on PyTorch.

The aipose library provides a simple API for performing human pose estimation in computer vision projects using pre-trained deep learning models built on PyTorch and TensorFlow.

Pytorch start pack for deep learning projects and research

Projects involve working on machine learning and deep learning concepts using Keras, PyTorch, TensorFlow libraries using Python Code.

Collection of deep learning projects using CNNs, RNNs, NLP models, and Transformers with TensorFlow and PyTorch.

A collection of projects built using PyTorch & NLTK implementing Deep Learning Models for NLP-related tasks

A collection of notebooks and projects done as a part of Udacity's Deep Learning Nanodegree using Pytorch.

A curated set of Jupyter notebooks showcasing various PyTorch projects and tutorials, covering deep learning fundamentals, computer vision, GANs, transfer learning, transformers and more. Continuously updated with new experiments, models and practical implementations for learning and research.

This repo contains three deep learning projects: multi-task learning models, tweet emotion recognition, and object localization using PyTorch. Each notebook explores key concepts with practical implementations and detailed explanations.

A Repository for various Deep Learning Projects with TensorFlow, Keras, Pytorch and other Deep Learning Libraries