DOLG-pytorch
Image-Forgery-Detection-CNN
| DOLG-pytorch | Image-Forgery-Detection-CNN | |
|---|---|---|
| 1 | 2 | |
| 123 | 136 | |
| - | - | |
| 0.0 | 0.0 | |
| over 2 years ago | 10 months ago | |
| Python | Python | |
| MIT License | - |
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DOLG-pytorch
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[R] DOLG: Single-Stage Image Retrieval with Deep Orthogonal Fusion of Local and Global Features
Unofficial Code : https://github.com/dongkyuk/DOLG-pytorch
Image-Forgery-Detection-CNN
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Question to the Mods: What is the exact criteria for removing new sighting submissions?
So far the bot archives only on the Telegram chann image/video posts that are posted to this subreddit exclusively hosted by the reddit uploaded media servers. It only tries to classify still images, using a small pre-trained dataset ueint from this GitHub repo: https://github.com/kPsarakis/Image-Forgery-Detection-CNN
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Update on the UAP.observer project that aims to instantly archive & classify forgery vs authentic footage.
Today I continued working on the script from yesterday utilizing the help of this GitHub repository (shout out to these guys!): https://github.com/kPsarakis/Image-Forgery-Detection-CNN I managed to get some results already.
What are some alternatives?
EasyOCR - Ready-to-use OCR with 80+ supported languages and all popular writing scripts including Latin, Chinese, Arabic, Devanagari, Cyrillic and etc.
awesome-colab-notebooks - Collection of google colaboratory notebooks for fast and easy experiments
fast-reid - SOTA Re-identification Methods and Toolbox
Hierarchical-Localization - Visual localization made easy with hloc
SincNet - SincNet is a neural architecture for efficiently processing raw audio samples.
cnnimageretrieval-pytorch - CNN Image Retrieval in PyTorch: Training and evaluating CNNs for Image Retrieval in PyTorch
Video_Forgery_Detection_Using_Machine_Learning
TorchGA - Train PyTorch Models using the Genetic Algorithm with PyGAD
classification - Classification of the MNIST dataset using various Deep Learning techniques
pytorch-metric-learning - The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.