Your can change [training script](https://towhee.io/image-embedding/isc/src/branch/main/train_isc.py) in your custom way.
Or your can refer to the [original repo](https://github.com/lyakaap/ISC21-Descriptor-Track-1st) and [paper](https://arxiv.org/abs/2112.04323) to learn more about contrastive learning and image instance retrieval.
Or your can refer to the [original repo](https://github.com/lyakaap/ISC21-Descriptor-Track-1st) and [paper](https://arxiv.org/abs/2112.04323) to learn more about contrastive learning and image instance retrieval.
# More Resources
- [Powering Semantic Search in Computer Vision with Embeddings - Zilliz blog](https://zilliz.com/learn/embedding-generation): Discover how to extract useful information from unstructured data sources in a scalable manner using embeddings.
- [How to Get the Right Vector Embeddings - Zilliz blog](https://zilliz.com/blog/how-to-get-the-right-vector-embeddings): A comprehensive introduction to vector embeddings and how to generate them with popular open-source models.
- [What Are Vector Embeddings?](https://zilliz.com/glossary/vector-embeddings): Learn the definition of vector embeddings, how to create vector embeddings, and more.
- [Image Embeddings for Enhanced Image Search - Zilliz blog](https://zilliz.com/learn/image-embeddings-for-enhanced-image-search): Image Embeddings are the core of modern computer vision algorithms. Understand their implementation and use cases and explore different image embedding models.
- [Enhancing Information Retrieval with Sparse Embeddings | Zilliz Learn - Zilliz blog](https://zilliz.com/learn/enhancing-information-retrieval-learned-sparse-embeddings): Explore the inner workings, advantages, and practical applications of learned sparse embeddings with the Milvus vector database
- [An Introduction to Vector Embeddings: What They Are and How to Use Them - Zilliz blog](https://zilliz.com/learn/everything-you-should-know-about-vector-embeddings): In this blog post, we will understand the concept of vector embeddings and explore its applications, best practices, and tools for working with embeddings.
