Stitchsong

Stitchsong's Stars

• xmu-xiaoma666/External-Attention-pytorch

  🍀 Pytorch implementation of various Attention Mechanisms, MLP, Re-parameter, Convolution, which is helpful to further understand papers.⭐⭐⭐

  Language:Python11.6k103831.9k

• cmhungsteve/Awesome-Transformer-Attention

  An ultimately comprehensive paper list of Vision Transformer/Attention, including papers, codes, and related websites

  4.7k13030490

• jeinlee1991/chinese-llm-benchmark

  中文大模型能力评测榜单：目前已囊括134个大模型，覆盖chatgpt、gpt-4o、谷歌gemini、百度文心一言、阿里通义千问、百川、讯飞星火、商汤senseChat、minimax等商用模型， 以及deepseek-v2.5、qwen2.5、llama3.1、glm4、书生internLM2.5、openbuddy、AquilaChat等开源大模型。不仅提供能力评分排行榜，也提供所有模型的原始输出结果！

  3.2k3853141

• frgfm/torch-cam

  Class activation maps for your PyTorch models (CAM, Grad-CAM, Grad-CAM++, Smooth Grad-CAM++, Score-CAM, SS-CAM, IS-CAM, XGrad-CAM, Layer-CAM)

  Language:Python2.1k1260211

• TACJu/TransFG

  This is the official PyTorch implementation of the paper "TransFG: A Transformer Architecture for Fine-grained Recognition" (Ju He, Jie-Neng Chen, Shuai Liu, Adam Kortylewski, Cheng Yang, Yutong Bai, Changhu Wang, Alan Yuille).

  Language:Python39353690

• pmqs/Fix-OneDrive-Zip

  Fix OneDrive Zip files >4Gig

  Language:Perl27571324

• grip-unina/DMimageDetection

  On the detection of synthetic images generated by diffusion models

  Language:Python20971521

• SunnyHaze/IML-ViT

  Official repository of paper “IML-ViT: Benchmarking Image manipulation localization by Vision Transformer”

  Language:Jupyter Notebook208194224

• NiFangBaAGe/Explicit-Visual-Prompt

  [CVPR 2023] Explicit Visual Prompting for Low-Level Structure Segmentations

  Language:Python19652016

• chou141253/FGVC-PIM

  Pytorch implementation for "A Novel Plug-in Module for Fine-Grained Visual Classification". fine-grained visual classification task.

  Language:Python19233539

• raoyongming/CAL

  [ICCV 2021] Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identification

  Language:Python14852724

• grip-unina/TruFor

  TruFor

  Language:Python14710209

• chail/patch-forensics

  Investigating patches for fake image classification

  Language:Python1344919

• grip-unina/noiseprint

  Noiseprint, a CNN-based camera model fingerprint

  Language:Python13431026

• chou141253/FGVC-HERBS

  Pytorch implementation of "Fine-grained Visual Classification with High-temperature Refinement and Background Suppression"

  Language:Python9242113

• jonasricker/diffusion-model-deepfake-detection

  [VISAPP2024] Towards the Detection of Diffusion Model Deepfakes

  Language:Python913910

• chuangchuangtan/LGrad

  Code for the paper: Learning on Gradients: Generalized Artifacts Representation for GAN-Generated Images Detection

  Language:Python893717

• JayQine/AMR

  Language:Python634166

• Dichao-Liu/CMAL

  Language:Python4411111

• shanface33/AutoSplice_Dataset

  AutoSplice: A Text-prompt Manipulated Image Dataset for Media Forensics, WMF@CVPR2023

  43150

• Knightzjz/NCL-IML

  Offical implement of NCL-IML (Pre-training-free Image Manipulation Localization through Non-Mutually Contrastive Learning), ICCV2023

  Language:Jupyter Notebook42267

• mobulan/IELT

  Source code of the paper Fine-Grained Visual Classification via Internal Ensemble Learning Transformer

  Language:Python42285

• SSAW14/BeyondtheSpectrum

  Implementation for the IJCAI2021 work "Beyond the Spectrum: Detecting Deepfakes via Re-synthesis"

  Language:Python37215

• grip-unina/SyntheticImagesAnalysis

  Synthetic Images Analysis

  Language:Python32343

• SunnyHaze/CASIA2.0-Corrected-Groundtruth

  Resolves severe noise in the widely spread CASIA2.0 dataset ground-truth for Image Manipulation Detection

  31140

• Sergo2020/DIF_pytorch_official

  Official Implementation of Deep Image Fingerprint: Accurate And Low Budget Synthetic Image Detector

  Language:Python20142

• seukgcode/IncDE

  [AAAI 2024] Towards Continual Knowledge Graph Embedding via Incremental Distillation

  Language:Python15211

• seukgcode/IterDE

  [AAAI 2023] IterDE: An Iterative Knowledge Distillation Framework for Knowledge Graph Embeddings

  Language:Python10211

• DSLJDI/NIST16-data-set-deduplication

  Language:Python5100

• Olivia-account/Poverty-Level-Prediction-Project-with-Random-Forest-and-Decision-Trees

  本项目旨在通过先进的数据分析和机器学习技术，对哥斯达黎加的家庭信息数据进行深度分析，以科学、精准地预测家庭的贫困程度。项目的核心目标是识别出最脆弱的家庭，以便政府和社会组织能更有针对性地提供援助，从而有效改善这些家庭的生活状况，并优化社会援助资源的分配。 通过随机森林和决策树等机器学习模型的应用，本项目开发了一个贫困预测系统。系统首先对原始数据集进行了详细的预处理和特征工程，包括缺失值处理、统计汇总与可视化以及新特征的创建和选择。然后，利用交叉验证和模型评估策略，精选出影响贫困程度的关键特征，并通过准确率等指标评估了模型的性能。最终，决策树模型以其高准确率被选为最优模型，用于实际的贫困预测任务。

  Language:Jupyter Notebook3101