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Beit Base Patch16 224
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Beit Base Patch16 224

Developed by nielsr
BEiT is a vision model based on image transformers, employing a BERT-like self-supervised pre-training method. It is first pre-trained and fine-tuned on ImageNet-22k, then further fine-tuned on ImageNet-1k.
Image ClassificationOpen Source License:Apache-2.0#Self-supervised pre-training#Image Transformer#ImageNet fine-tuning
Downloads 28
Release Time : 3/2/2022

Model Overview

The BEiT model is pre-trained on the ImageNet-22k dataset through self-supervised learning, effectively capturing image features and suitable for various image classification tasks.

Model Features

Self-supervised pre-training
Employs a BERT-like self-supervised learning method, enabling effective pre-training without the need for large amounts of labeled data.
Two-stage fine-tuning
First fine-tuned on the ImageNet-22k dataset, then further fine-tuned on ImageNet-1k to enhance model performance.
Image Transformer architecture
Transformer-based architecture capable of effectively capturing both global and local features in images.

Model Capabilities

Image feature extraction
Image classification
Visual representation learning

Use Cases

Computer vision
General image classification
Classifies natural images to identify the main objects or scenes within them.
Achieves good performance on standard datasets like ImageNet
Visual feature extraction
Serves as a foundational feature extractor for other vision tasks.
Can be used for downstream tasks such as object detection and image segmentation
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