TGSSalt_Identification_Kaggle_Challenge https://www.kaggle.com/c/tgs-salt-identification-challenge
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Updated
Oct 11, 2018 - Jupyter Notebook
TGSSalt_Identification_Kaggle_Challenge https://www.kaggle.com/c/tgs-salt-identification-challenge
This repository contains tasks focusing on prompt engineering for vision models. Each task explores different aspects of image segmentation, object detection, and image generation using advanced machine learning models. Below are detailed descriptions of the tasks and their respective notebooks.
A retinal segmentation model without learning algorithm
Kaggle Competition: Airbus Ship Detection Challenge
This repository contains the Jupyter Notebook for the UNet-VGG16 CNN Model trained on the Lunar Landscape Images Dataset.
This project uses MRF (Markov Random Field) to remove noise from the image and segment it.
This repo contains a UNet based deep learning model for identifying roads from aerial images
This is a python implementation of Hierarchical Image Matting Model for Segmentation.
Deep Extreme Cut http://www.vision.ee.ethz.ch/~cvlsegmentation/dextr . a tool to do automatically object segmentation from extreme points.
Mask R-CNN, FPN, LinkNet, PSPNet and UNet with multiple backbone architectures support readily available
Official implementation of ResUNet++, CRF, and TTA for segmentation of medical images (IEEE JBIHI)
Official PyTorch implementation of Revisiting Image Pyramid Structure for High Resolution Salient Object Detection (ACCV 2022)
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