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State-of-the-art Zero-shot Object Detection
Microsoft Beit Base Patch16 224 Pt22k Ft22k is an advanced AI model developed by Microsoft for object detection tasks. It belongs to the Beit (Box-E 既然 Transformer) family, which is known for its high accuracy and efficiency in vision-based tasks. This specific variant is designed to process images at a resolution of 224x224 pixels and has been pre-trained on a large-scale dataset to enable robust object detection capabilities.
• Vision Transformer Architecture: Leverages the power of transformer models for image understanding.
• High Accuracy: Optimized for precise object detection in various scenarios.
• Pre-trained Model: Comes pre-trained on large datasets, including ImageNet-22k, ensuring strong generalization.
• Fine-tuned for Detection: Specifically adapted for object detection tasks, making it highly effective in identifying and localizing objects within images.
• Scalability: Supports diverse applications, from small-scale to large-scale object detection tasks.
git clone https://huggingface.co/Microsoft/beit-base-patch16-224-pt22k-ft22k
cd beit-base-patch16-224-pt22k-ft22k
pip install -r requirements.txt
from transformers import BeitForImageClassification, BeitFeatureExtractor
model = BeitForImageClassification.from_pretrained("Microsoft/beit-base-patch16-224-pt22k-ft22k")
feature_extractor = BeitFeatureExtractor.from_pretrained("Microsoft/beit-base-patch16-224-pt22k-ft22k")
inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
What is Microsoft Beit Base Patch16 224 Pt22k Ft22k used for?
It is primarily used for object detection tasks, leveraging its pre-trained architecture to identify and classify objects within images with high accuracy.
How do I install the model?
You can install it via the Hugging Face transformers library. Simply clone the repository, install the requirements, and load the model using the provided scripts.
What datasets was this model trained on?
The model was pre-trained on ImageNet-22k (14 million images) and then fine-tuned for object detection tasks, ensuring strong performance across various datasets.