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OWL-ViT: Object Detection
OWL-ViT (Open-World Localization Vision Transformer), developed by Google Research, is an open-vocabulary object detection model that integrates CLIP's vision-language pretraining with detection frameworks to detect objects described by arbitrary text without fine-tuning. It extends CLIP's image and text encoders into a detection architecture, aligning image regions with text descriptions via contrastive learning to generate bounding boxes and match scores. Built on Vision Transformers (ViT) for global feature extraction and lightweight detection heads, it supports zero-shot transfer to unseen categories (e.g., "purple unicorn toy" or "logo-covered backpack"), demonstrating strong generalization on open-world datasets like LVIS.
Source model
- Input shape: [[1,3,768,768]], [[1,16],[1,16]],[[1,24,24,768],[1,512],[1,16]]
- Number of parameters: 84.92M, 60.46M, --
- Model size: 339.91M, 242.06M, 1.51M
- Output shape: [[1,24,24,768],[1,576,4]], [[1,512]], [[1,576,1]]
The source model can be found here
Performance Reference
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Inference & Model Conversion
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License
Source Model: APACHE-2.0
Deployable Model: APLUX-MODEL-FARM-LICENSE