Traditional Machine learning and Deep learning approaches to classify Search for extraterrestrial intelligence Signal
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Updated
Nov 30, 2022 - Jupyter Notebook
Traditional Machine learning and Deep learning approaches to classify Search for extraterrestrial intelligence Signal
Swin Transformer implementations for TensorFlow/Keras
[Ecological Informatics] TensorFlow implementation for the paper "Bag of tricks for long-tail visual recognition of animal species in camera-trap images"
Kaggle Competition Bronze Medal 🥉 (205th out of 3537 teams)
This repository contains an image classification model for the subject AI Convergence and Application held on Handong Global University (HGU)
This repo hosts the water body extraction from satellite images using Trans Deeplab model.
Swin Transformers, short for "Shifted Windows," were introduced in the paper titled "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" by Liu et a. (2021). Unlike traditional transformers, Swin Transformers divide the image into non-overlapping shifted windows, enabling efficient and scalable computation.
Real-time ReID Tracking w/. Lite but Strong Feature Extractor & GAN
This project uses PyTorch to classify bone fractures. As well as fine-tuning some famous CNN architectures (like VGG 19, MobileNetV3, RegNet,...), we designed our own architecture. Additionally, we used Transformer architectures (such as Vision Transformer and Swin Transformer). This dataset is Bone Fracture Multi-Region X-ray, available on Kaggle.
building AVA from ex-machina; a lightweight multi-modal system from scratch, just for learning & experimentation
swin transformer pytorch starter
Comprehensive Performance Analysis of Three Pretrained Transformer Models (ViT, Swin, and MaxViT) on ImageNet and Fine-tuned on the NIH Chest X-rays Dataset for Classifying 14 Chest Radiograph Pathologies
This project compares the performance of Swin-Transformer v2 implemented in JAX and PyTorch.
This is a warehouse for Agent-Attention-Models based on pytorch framework, can be used to train your image datasets.
This is the key code of the paper "CCST: Crowd Counting with Swin Transformer"
Official Repository for the paper "Feature Extraction for Generative Medical Imaging Evaluation: New Evidence Against an Evolving Trend".
Comparing the performance of pixel-wise binary classification for road detection, from panchromatic satellite images using a custom model called RoadSegNN (with ResNet and Swin-T backbones) and SegNet.
This is a warehouse for STL-Pytorch-model, can be used to train your image-datasets for vision tasks.
TensorFlow implementation of EGIC (to be announced)
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