Vector search finds wide applications in image recognition and image retrieval. It aims to obtain the similarity ranking for a given query vector by performing a similarity or distance calculation of feature vectors with all the vectors to be queried in an established vector library. In the image recognition system, [Faiss](https://github.com/facebookresearch/faiss) is adopted for corresponding support, please check [the official website of Faiss](https://github.com/facebookresearch/faiss for more information ) for more details. The main advantages of `Faiss` can be generalized as the following:
Vector search finds wide applications in image recognition and image retrieval. It aims to obtain the similarity ranking for a given query vector by performing a similarity or distance calculation of feature vectors with all the vectors to be queried in an established vector library. In the image recognition system, [Faiss](https://github.com/facebookresearch/faiss) is adopted for corresponding support, please check [the official website of Faiss](https://github.com/facebookresearch/faiss) for more details. The main advantages of `Faiss` can be generalized as the following:
- Great adaptability: support Windows, Linux, and MacOS systems
- Easy installation: support `python` interface and direct installation with `pip`
@@ -12,7 +12,7 @@ For an image to be queried, the image recognition process in PaddleClas is divid
The feature gallery is built in advance using the labeled image datasets. The complete image recognition system is shown in the figure below.
[
To experience the whole image recognition system, or learn how to build a feature gallery, please refer to [Quick Start of Image Recognition](../quick_start/quick_start_recognition_en.md), which explains the overall application process. The following parts expound on the training part of the above three steps.
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@@ -311,7 +311,7 @@ Image retrieval refers to a query image given a specific instance (e.g. a specif
- Metric Learning
Metric learning studies how to learn a distance function on a particular task so that the distance function can help nearest-neighbour based algorithms (kNN, k-means, etc.) to achieve better performance. Deep Metric Learning is a method of metric learning that aims to learn a mapping from the original features to a low-dimensional dense vector space (embedding space) such that similar objects on the embedding space are closer together using commonly used distance functions (Euclidean distance, cosine distance, etc.) ) on the embedding space, while the distances between objects of different classes are not close to each other. Deep metric learning has achieved very successful applications in the field of computer vision, such as face recognition, commodity recognition, image retrieval, pedestrian re-identification, etc. See [HERE](../algorithm_introduction/metric_learning_em.md) for detailed information.
Metric learning studies how to learn a distance function on a particular task so that the distance function can help nearest-neighbour based algorithms (kNN, k-means, etc.) to achieve better performance. Deep Metric Learning is a method of metric learning that aims to learn a mapping from the original features to a low-dimensional dense vector space (embedding space) such that similar objects on the embedding space are closer together using commonly used distance functions (Euclidean distance, cosine distance, etc.) ) on the embedding space, while the distances between objects of different classes are not close to each other. Deep metric learning has achieved very successful applications in the field of computer vision, such as face recognition, commodity recognition, image retrieval, pedestrian re-identification, etc. See [HERE](../algorithm_introduction/metric_learning_en.md) for detailed information.
<aname="Introduction to Image Retrieval Datasets"></a>
