A toolkit for Animal Individual Identification that covers use cases such as training, feature extraction, similarity calculation, image retrieval, and classification.

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Datasets for identification of individual animals	Trained model for individual re‑identification	Tools for training re‑identification models

Introduction

The wildlife-tools library offers a simple interface for various tasks in the Wildlife Re-Identification domain. It covers use cases such as training, feature extraction, similarity calculation, image retrieval, and classification. It complements the wildlife-datasets library, which acts as dataset repository.

More information can be found in Documentation

What's New

Here’s a summary of recent updates and changes.

• Expanded Functionality: Local feature matching is done using gluefactory
  • Feature extraction methods: SuperPoint, ALIKED, DISK, SIFT features
  • Matching method: LightGlue, More efficient LoFTR
• New Feature: Introduced WildFusion (https://arxiv.org/abs/2408.12934), calibrated score fusion for high-accuracy animal reidentification. Added calibration methods.
• Bug Fixes: Resolved issues with knn and ranking inference methods and many more.

Installation

To install wildlife-tools, you can build it from scratch or use pre-build Pypi package.

Using Pypi

pip install wildlife-tools

Building from scratch

Clone the repository using git and install it.

git clone git@github.com:WildlifeDatasets/wildlife-tools.git

cd wildlife-tools
pip install -e .

Modules in the in the wildlife-tools

• The data module provides tools for creating instances of the ImageDataset.
• The train module offers tools for fine-tuning feature extractors on the ImageDataset.
• The features module provides tools for extracting features from the ImageDataset using various extractors.
• The similarity module provides tools for constructing a similarity matrix from query and database features.
• The inference module offers tools for creating predictions using the similarity matrix.

Relations between modules:

  graph TD;
      A[Data]-->|ImageDataset|B[Features]
      A-->|ImageDataset|C;
      C[Train]-->|finetuned extractor|B;
      B-->|query and database features|D[Similarity]
      D-->|similarity matrix|E[Inference]

Loading

Example

1. Create ImageDataset

Using metadata from wildlife-datasets, create ImageDataset object for the MacaqueFaces dataset.

from wildlife_datasets.datasets import MacaqueFaces
from wildlife_tools.data import ImageDataset
import torchvision.transforms as T

metadata = MacaqueFaces('datasets/MacaqueFaces')
transform = T.Compose([T.Resize([224, 224]), T.ToTensor(), T.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225))])
dataset = ImageDataset(metadata.df, metadata.root, transform=transform)

Optionally, split metadata into subsets. In this example, query is first 100 images and rest are in database.

dataset_database = ImageDataset(metadata.df.iloc[100:,:], metadata.root, transform=transform)
dataset_query = ImageDataset(metadata.df.iloc[:100,:], metadata.root, transform=transform)

2. Extract features

Extract features using MegaDescriptor Tiny, downloaded from HuggingFace hub.

import timm
from wildlife_tools.features import DeepFeatures

name = 'hf-hub:BVRA/MegaDescriptor-T-224'
extractor = DeepFeatures(timm.create_model(name, num_classes=0, pretrained=True))
query, database = extractor(dataset_query), extractor(dataset_database)

3. Calculate similarity

Calculate cosine similarity between query and database deep features.

from wildlife_tools.similarity import CosineSimilarity

similarity_function = CosineSimilarity()
similarity = similarity_function(query, database)

4. Evaluate

Use the cosine similarity in nearest neigbour classifier and get predictions.

import numpy as np
from wildlife_tools.inference import KnnClassifier

classifier = KnnClassifier(k=1, database_labels=dataset_database.labels_string)
predictions = classifier(similarity['cosine'])
accuracy = np.mean(dataset_database.labels_string == predictions)

Citation

If you like our package, please cite us.

@InProceedings{Cermak_2024_WACV,
    author    = {\v{C}erm\'ak, Vojt\v{e}ch and Picek, Luk\'a\v{s} and Adam, Luk\'a\v{s} and Papafitsoros, Kostas},
    title     = {{WildlifeDatasets: An Open-Source Toolkit for Animal Re-Identification}},
    booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
    month     = {January},
    year      = {2024},
    pages     = {5953-5963}
}

@article{cermak2024wildfusion,
  title={WildFusion: Individual animal identification with calibrated similarity fusion},
  author={Cermak, Vojt{\v{e}}ch and Picek, Lukas and Adam, Luk{\'a}{\v{s}} and Neumann, Luk{\'a}{\v{s}} and Matas, Ji{\v{r}}{\'\i}},
  journal={arXiv preprint arXiv:2408.12934},
  year={2024}
}