From 05eb77272d36ace4ef79d64bba49e2f2e864acb1 Mon Sep 17 00:00:00 2001 From: Jael Gu Date: Wed, 18 Sep 2024 12:30:07 +0800 Subject: [PATCH] Add more resources Signed-off-by: Jael Gu --- README.md | 98 ++----------------------------------------------------- 1 file changed, 3 insertions(+), 95 deletions(-) diff --git a/README.md b/README.md index d60de51..d431bdf 100644 --- a/README.md +++ b/README.md @@ -1,98 +1,6 @@ -# Image Embedding with data2vec -*author: David Wang* +# More Resources -
- - - -## Description - -This operator extracts features for image with [data2vec](https://arxiv.org/abs/2202.03555). The core idea is to predict latent representations of the full input data based on a masked view of the input in a self-distillation setup using a standard Transformer architecture. - -
- - -## Code Example - -Load an image from path './towhee.jpg' to generate an image embedding. - -*Write a pipeline with explicit inputs/outputs name specifications:* - -```python -from towhee import pipe, ops, DataCollection - -p = ( - pipe.input('path') - .map('path', 'img', ops.image_decode()) - .map('img', 'vec', ops.image_embedding.data2vec(model_name='facebook/data2vec-vision-base-ft1k')) - .output('img', 'vec') -) - -DataCollection(p('towhee.jpeg')).show() -``` -result2 - - -
- - - -## Factory Constructor - -Create the operator via the following factory method - -***data2vec(model_name='facebook/data2vec-vision-base')*** - -**Parameters:** - - -​ ***model_name***: *str* - -The model name in string. -The default value is "facebook/data2vec-vision-base-ft1k". - -Supported model name: -- facebook/data2vec-vision-base-ft1k -- facebook/data2vec-vision-large-ft1k - -
- - - -## Interface - -An image embedding operator takes a [towhee image](link/to/towhee/image/api/doc) as input. -It uses the pre-trained model specified by model name to generate an image embedding in ndarray. - - -**Parameters:** - -​ ***img:*** *towhee.types.Image (a sub-class of numpy.ndarray)* - -​ The decoded image data in towhee.types.Image (numpy.ndarray). - - - -**Returns:** *numpy.ndarray* - -​ The image embedding extracted by model. - - - - - - # More Resources - - - [What is a Transformer Model? An Engineer's Guide](https://zilliz.com/glossary/transformer-models): A transformer model is a neural network architecture. It's proficient in converting a particular type of input into a distinct output. Its core strength lies in its ability to handle inputs and outputs of different sequence length. It does this through encoding the input into a matrix with predefined dimensions and then combining that with another attention matrix to decode. This transformation unfolds through a sequence of collaborative layers, which deconstruct words into their corresponding numerical representations. - -At its heart, a transformer model is a bridge between disparate linguistic structures, employing sophisticated neural network configurations to decode and manipulate human language input. An example of a transformer model is GPT-3, which ingests human language and generates text output. -- [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. -- [Transforming Text: The Rise of Sentence Transformers in NLP - Zilliz blog](https://zilliz.com/learn/transforming-text-the-rise-of-sentence-transformers-in-nlp): Everything you need to know about the Transformers model, exploring its architecture, implementation, and limitations -- [What Are Vector Embeddings?](https://zilliz.com/glossary/vector-embeddings): Learn the definition of vector embeddings, how to create vector embeddings, and more. -- [What is Detection Transformers (DETR)? - Zilliz blog](https://zilliz.com/learn/detection-transformers-detr-end-to-end-object-detection-with-transformers): DETR (DEtection TRansformer) is a deep learning model for end-to-end object detection using transformers. -- [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. - \ No newline at end of file + + \ No newline at end of file