OWL-ViT Base Patch32
| Property | Value |
|---|---|
| Parameter Count | 153M |
| License | Apache 2.0 |
| Paper | Research Paper |
| Author | |
| Release Date | May 2022 |
What is owlvit-base-patch32?
OWL-ViT (Vision Transformer for Open-World Localization) is an advanced zero-shot text-conditioned object detection model that leverages the power of CLIP as its multi-modal backbone. The model combines a ViT-like Transformer for visual processing with a causal language model for text understanding, enabling users to query images with multiple text prompts simultaneously.
Implementation Details
The model architecture consists of a CLIP backbone with a ViT-B/32 Transformer for image encoding and a masked self-attention Transformer for text encoding. The model removes CLIP's final token pooling layer and adds lightweight classification and box heads to each transformer output token, enabling precise object detection capabilities.
- Multi-modal architecture combining vision and text processing
- Trained on large-scale image-caption datasets including YFCC100M
- Fine-tuned on COCO and OpenImages datasets
- Supports zero-shot text-conditioned object detection
Core Capabilities
- Zero-shot object detection without prior training on specific objects
- Multiple text query processing per image
- Bipartite matching loss for accurate object detection
- Open-vocabulary classification using text embeddings
Frequently Asked Questions
Q: What makes this model unique?
OWL-ViT's ability to perform zero-shot object detection with text queries makes it particularly versatile. Unlike traditional object detection models, it doesn't require pre-training on specific object classes and can identify new objects based on text descriptions.
Q: What are the recommended use cases?
The model is primarily intended for research purposes, particularly in studying zero-shot detection capabilities, model robustness, and generalization. It's especially useful in scenarios where traditional object detection models might fail due to unavailable training labels.
