Cvt 21 384
CvT-21 is an image classification model based on the Convolutional Vision Transformer architecture, pretrained on the ImageNet-1k dataset at a resolution of 384x384.
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Release Time : 4/4/2022
Model Overview
This model combines the strengths of convolutional neural networks and vision transformers for image classification tasks, capable of classifying images into 1,000 ImageNet categories.
Model Features
Combination of Convolution and Transformer
Introduces convolutional operations into the vision transformer architecture, combining CNN's local feature extraction capability with Transformer's global modeling ability.
High-resolution Processing
Supports 384x384 high-resolution image input, capturing finer image features.
Efficient Computation
Reduces computational complexity through convolutional operations, making it more efficient compared to pure Transformer architectures.
Model Capabilities
Image Classification
Visual Feature Extraction
Use Cases
Computer Vision
Object Recognition
Identify the category of objects in an image.
Accurately classifies 1,000 common objects.
Scene Understanding
Analyze the content of an image scene.
Can recognize various scenes such as natural environments and indoor settings.
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