Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
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Tf2deepfloorplan | 107 | a year ago | 2 | gpl-3.0 | Python | |||||
TF2 Deep FloorPlan Recognition using a Multi-task Network with Room-boundary-Guided Attention. Enable tensorboard, quantization, flask, tflite, docker, github actions and google colab. | ||||||||||
Operation Wise Attention Network | 74 | 5 years ago | 5 | mit | Python | |||||
Attention-based Adaptive Selection of Operations for Image Restoration in the Presence of Unknown Combined Distortions (CVPR 2019) | ||||||||||
Ganvinci | 33 | a year ago | 3 | Python | ||||||
Photorealistic human image editing with GANs - Reimplementation of the paper "FEAT: Face Editing with Attention" with additional changes and improvements. | ||||||||||
Diabetic Retinopathy Detection | 26 | 4 years ago | 1 | mit | Jupyter Notebook | |||||
DIAGNOSIS OF DIABETIC RETINOPATHY FROM FUNDUS IMAGES USING SVM, KNN, and attention-based CNN models with GradCam score for interpretability, | ||||||||||
Concise Ipython Notebooks For Deep Learning | 17 | 5 years ago | Jupyter Notebook | |||||||
Ipython Notebooks for solving problems like classification, segmentation, generation using latest Deep learning algorithms on different publicly available text and image data-sets. | ||||||||||
Pmaa | 9 | 8 months ago | mit | JavaScript | ||||||
Official PyTorch implementation of "PMAA: A Progressive Multi-scale Attention Autoencoder Model for High-Performance Cloud Removal from Multi-temporal Satellite Imagery" (ECAI 2023). | ||||||||||
Automated Scoring Of Handwritten Test Papers | 8 | 5 months ago | mit | Python | ||||||
Extract handwritten information like name, student ID and then recognize them with CRNN-CTC-Attention. Using lexicon search on class list to help teacher on updating score faster | ||||||||||
Coughvid 19 Crnn Attention | 5 | 5 months ago | mit | Jupyter Notebook | ||||||
Another project for classifying Covid and non-covid patients through cough sound. Using CRNN-Attention model with the sound data converted into image data |