Build Custom Image Search Engine in Python | DeepImageSearch Library | Gradio app | Applied ML Vide

Описание

In this video, we discuss the library #DeepImageSearch, which uses deep-learning techniques to perform image retrieval. It works by encoding images into a compact feature representation, which is then used to search for similar images in a large database.

The #feature representation is generated by a deep neural network trained on a large #dataset of images. The network is trained to extract relevant features that capture the semantic content of an image. Once the network is trained, new images can be fed through the network to produce their feature representation.

The feature representation can then be used to perform an image search by comparing the similarity between the feature representations of the query image and the images in the database. The search results can be ranked based on their #similarity to the #query #image


Codebook: https://github.com/dhanushnayak/DeepImageSearch
Deep Image Search - https://github.com/TechyNilesh/DeepImageSearch

You can use your know dataset for this video hands-on.