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pyvisim 1.0.0
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pyvisim 1.0.0

Getting started

  • Introduction

API reference

  • Classic embedders
    • VLADEmbedder
    • FisherVectorEmbedder
    • Pipeline
  • Features
    • Handcrafted features
      • SIFT
      • RootSIFT
      • Lambda
    • Deep learning based features
      • DeepConvFeature
  • Image Similarity Retrieval
    • Image store
      • In Memory Image Embedding Store
    • Reranking
      • K-Reciprocal Reranking
  • Dataset
    • OxfordFlowerDataset
  • Neural networks
    • ContrastiveSiameseNetwork
    • BCESiameseNetwork
    • TripletNeuralNetwork
    • ClipEmbedder
    • Backbones
  • Distance metrics
  • Dense
    • Structural
      • SSIM
      • MSSSIM
    • Pixelwise
      • PSNR
  • Typing
  • Evaluation functions

Tutorials

  • 1 Introduction
    • 1.1 pyvisim Introduction
  • 2 Classical methods
    • 2.1 Classical Embedding algorithms: VLAD and Fisher Vectors
    • 2.2 VLAD and Fisher Vector Retrieval with deep CNN features
    • 2.3 Pipeline with Deep Features
    • 2.4 Custom Feature Extractor with RootSIFT
    • 2.5 Custom Feature Extractor with ORB
  • 3 Metric learning methods
    • 3.1 Siamese Neural Network
    • 3.2 Triplet Neural Network
  • 4 Image similarity search
    • 4.1 Image Search
    • 4.2 Computing Mean Average Precision (mAP) and Top-k Accuracy for our Retrieval System
    • 4.3 Oxford Flowers Clustering Notebook

Release notes

  • Release notes
    • v1.0.0
    • v0.10.2
    • v0.10.1
    • v0.10.0
    • v0.9.5
    • v0.9.4
    • Changelog
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Release notes¶

Important

Releases up to and including 0.9.3 are documented in the changelog.

  • v1.0.0
  • v0.10.2
  • v0.10.1
  • v0.10.0
  • v0.9.5
  • v0.9.4
  • Changelog
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4.3 Oxford Flowers Clustering Notebook
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