Proceedings of the 28th ACM International Conference on Multimedia The official repository with Pytorch Our method can realize arbitrary face swapping on images and videos with one single trained model. Currently, only the test code is available. Training scripts are coming soon Our paper can be downloaded from [Arxiv] [ACM DOI] Attention This project is for technical and academic use only. Please do not apply it to illegal and unethical scenarios. If you find this project useful, please star it. […]

PyTorch implementation of a Real-ESRGAN model trained on custom dataset. This model shows better results on faces compared to the original version. It is also easier to integrate this model into your projects. You can try it in google colab Installation Clone repo git clone https://https://github.com/sberbank-ai/Real-ESRGAN cd Real-ESRGAN Install requirements pip install -r requirements.txt Download pretrained weights and put them into weights/ folder Usage Basic example:

@InProceedings{Gao_2021_CVPR, author = {Gao, Gege and Huang, Huaibo and Fu, Chaoyou and Li, Zhaoyang and He, Ran}, title = {Information Bottleneck Disentanglement for Identity Swapping}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2021}, pages = {3404-3413} }    

GitHub – mratsim/Amazon-Forest-Computer-Vision at pythonawesome.com Amazon Forest Computer Vision: Satellite Image tagging code using PyTorch / Keras with lots of PyTorch tricks – GitHub – mratsim/Amazon-Forest-Computer-Vision at pythonawesome.com GitHubmratsim    

python export detections_repeatability.py python compute_repeatability.py (NOTE: You have to edit *.yaml files to run corresponding tasks, especially for the following items model name: superpoint # magicpoint … data: name: coco #synthetic image_train_path: [‘./data/mp_coco_v2/images/train2017’,] #several data sets can be list here label_train_path: [‘./data/mp_coco_v2/labels/train2017/’,] image_test_path: ‘./data/mp_coco_v2/images/test2017/’ label_test_path: ‘./data/mp_coco_v2/labels/test2017/’    

Unofficial PyTorch implementation of SimCLR by Google Brain [abs, pdf] Installation You can install the package via pip: pip install simclr_pytorch Usage You can use the SimCLR model like so: from simclr_pytorch import SimCLR train_loader = … bigcnn = ResNet50(…) simclr = SimCLR( image_size=(32, 32), bigcnn=bigcnn ) trained_bigcnn = simclr.fit(train_loader)

arXiv | BibTeX | Project Page This repo contains code for single GPU training of LayoutTransformer from LayoutTransformer: Layout Generation and Completion with Self-attention. This code was rewritten from scratch using a cleaner GPT codebase. Some of the details such as training hyperparameters might differ from the arxiv version of the paper. How To Use This Code Start a new conda environment conda env create -f environment.yml conda activate layout or update an existing environment conda env    

GeneralOCR is open source Optical Character Recognition based on PyTorch. It makes a fidelity and useful tool to implement SOTA models on OCR domain. You can use them to infer and train the model with your customized dataset. The solution architecture of this project is re-implemented from facebook Detectron and openmm-cv. Refer to the guideline of gen_ocr installation Configuration Model text detection Supported Algorithms: Text Detection Table 1: Text detection algorithms, papers and parameters configuration in SDK. Model text recognition […]

DiscoBox: Weakly Supervised Instance Segmentation and Semantic Correspondence from Box Supervision Paper | Project page | Demo (Youtube) | Demo (Bilibili) DiscoBox: Weakly Supervised Instance Segmentation and Semantic Correspondence from Box Supervision.Shiyi Lan, Zhiding Yu, Chris Choy, Subhashree Radhakrishnan, Guilin Liu, Yuke Zhu, Larry Davis, Anima AnandkumarInternational Conference on Computer Vision (ICCV) 2021 This repository contains the official Pytorch implementation of training & evaluation code and pretrained models for DiscoBox.DiscoBox is a    

Overview This is a PyTorch implementation of MobileViT specified in “MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer“, arXiv 2021. Usage import torch from mobilevit import mobilevit_xxs net = mobilevit_xxs() img = torch.randn(1, 3, 256, 256) out = net(img) Credits Code adapted from MobileNetV2 and ViT. GitHub https://github.com/chinhsuanwu/mobilevit-pytorch