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image-decode
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2.2 KiB
2.2 KiB
Operator: image-decode
Author:
Overview
Interface
__init__(self, init_arg_1: xxx)
Args:
-
init_arg_1:
- xxx(description about init_arg_1)
- supported types: xxx
__call__(self, call_arg_1: xxx)
Args:
- call_arg_1:
- xxx(description about call_arg_1)
- supported types: xxx
Returns: The Operator returns a tuple Tuple[('results_1', xxx)] containing following fields:
- results_1:
- xxx(description of results_1)
- data type: xxx
- shape: (xxx,)
Requirements
How it works
Reference
# More Resources
- [Scalar Quantization and Product Quantization - Zilliz blog](https://zilliz.com/learn/scalar-quantization-and-product-quantization): A hands-on dive into scalar quantization (integer quantization) and product quantization with Python.
- Understanding Computer Vision - Zilliz blog: Computer Vision is a field of Artificial Intelligence that enables machines to capture and interpret visual information from the world just like humans do.
- The guide to clip-vit-base-patch32 | OpenAI: clip-vit-base-patch32: a CLIP multimodal model variant by OpenAI for image and text embedding.
- Demystifying Color Histograms: A Guide to Image Processing and Analysis - Zilliz blog: Mastering color histograms is indispensable for anyone involved in image processing and analysis. By understanding the nuances of color distributions and leveraging advanced techniques, practitioners can unlock the full potential of color histograms in various imaging projects and research endeavors.
- Understanding ImageNet: A Key Resource for Computer Vision and AI Research: The large-scale image database with over 14 million annotated images. Learn how this dataset supports advancements in computer vision.
- Image Embeddings for Enhanced Image Search - Zilliz blog: Image Embeddings are the core of modern computer vision algorithms. Understand their implementation and use cases and explore different image embedding models.
2.2 KiB
2.2 KiB
Operator: image-decode
Author:
Overview
Interface
__init__(self, init_arg_1: xxx)
Args:
-
init_arg_1:
- xxx(description about init_arg_1)
- supported types: xxx
__call__(self, call_arg_1: xxx)
Args:
- call_arg_1:
- xxx(description about call_arg_1)
- supported types: xxx
Returns: The Operator returns a tuple Tuple[('results_1', xxx)] containing following fields:
- results_1:
- xxx(description of results_1)
- data type: xxx
- shape: (xxx,)
Requirements
How it works
Reference
# More Resources
- [Scalar Quantization and Product Quantization - Zilliz blog](https://zilliz.com/learn/scalar-quantization-and-product-quantization): A hands-on dive into scalar quantization (integer quantization) and product quantization with Python.
- Understanding Computer Vision - Zilliz blog: Computer Vision is a field of Artificial Intelligence that enables machines to capture and interpret visual information from the world just like humans do.
- The guide to clip-vit-base-patch32 | OpenAI: clip-vit-base-patch32: a CLIP multimodal model variant by OpenAI for image and text embedding.
- Demystifying Color Histograms: A Guide to Image Processing and Analysis - Zilliz blog: Mastering color histograms is indispensable for anyone involved in image processing and analysis. By understanding the nuances of color distributions and leveraging advanced techniques, practitioners can unlock the full potential of color histograms in various imaging projects and research endeavors.
- Understanding ImageNet: A Key Resource for Computer Vision and AI Research: The large-scale image database with over 14 million annotated images. Learn how this dataset supports advancements in computer vision.
- Image Embeddings for Enhanced Image Search - Zilliz blog: Image Embeddings are the core of modern computer vision algorithms. Understand their implementation and use cases and explore different image embedding models.