The repository contains the Jupyter Notebook that perform semantic segmentation using the famous U-Net. The encoder of the U-Net is replaced with the pretrained encoder.
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Updated
May 1, 2021 - Jupyter Notebook
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The repository contains the Jupyter Notebook that perform semantic segmentation using the famous U-Net. The encoder of the U-Net is replaced with the pretrained encoder.
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Repo of all deep learning models
A simple and intuitive library for diffusion models using Flax and Jax. Includes detailed notebooks on DDPM, DDIM, and EDM with simplified mathematical explanations. Made as part of my journey for learning and experimenting with generative AI.
Collection of notebooks for image segmentation tasks.
A beginner-friendly guide to building and training diffusion models from scratch. Includes step-by-step tutorials, interactive notebooks, and a complete PyTorch implementation with DDIM, Heun, and DPM-Solver samplers. Learn to generate images with MNIST/CIFAR-10 datasets.
Educational PyTorch notebook guide on DDPM, Latent Diffusion, VAE, U-Net, and LoRA.
A tutorial that guides users through the process of fine-tuning a stable diffusion model using HuggingFace's diffusers library. The tutorial includes advice on suitable hardware requirements, data preparation using the BLIP Flowers Dataset and a Python notebook, and detailed instructions for fine-tuning the model.
Deep learning architectures for semantic segmentation run on Jupyter Notebook: U-Net, MANet, MAResU-Net, MACU-Net.
U-Net semantic segmentation study on the Oxford-IIIT Pet Dataset with MobileNetV2 encoder, qualitative mask predictions, and reproducible notebook artifacts.
U-Net pour la segmentation personne/fond en PyTorch : du reseau ecrit a la main au transfer learning ResNet18. Notebooks annotes ligne par ligne.
This repository accompanies the publication "U-Net based particle localization in granular experiments: Accuracy limits and optimization," published in Granular Matter (2026). It contains the U-Net implementation, pretrained weights, labeled datasets, Jupyter notebooks, and instructions for reproducing the results.
In this repository you can find the jupyter notebooks used to take part at the competitions created for the Artifical Neural Networks and Deep Learning exam at Politecnico di Milano.
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