owlv2 base patch16 ensemble

Introduction

The OWLv2 model is a zero-shot, text-conditioned object detection model that builds upon the CLIP architecture. It enables querying images with text inputs, allowing for open-vocabulary object detection without requiring explicit labeling during training.

Architecture

OWLv2 utilizes a CLIP backbone featuring a ViT-B/16 Transformer for encoding images and a masked self-attention Transformer for encoding text. The model is trained to maximize the similarity between image and text pairs using a contrastive loss. The final architecture includes a lightweight classification and box head attached to each Transformer output token, facilitating open-vocabulary classification.

Training

The model is trained by first constructing the CLIP backbone from scratch. It is then fine-tuned along with the classification and box prediction heads on standard detection datasets using bipartite matching loss. This approach allows the model to perform zero-shot text-conditioned object detection, leveraging text embeddings as class identifiers.

Guide: Running Locally

To run OWLv2 locally, follow these steps:

1. Install Dependencies: Ensure Python, PyTorch, and the Transformers library are installed in your environment.
2. Load Model: Use the Hugging Face Transformers library to load the OWLv2 model and processor.
3. Prepare Input: Load an image and define text queries for object detection.
4. Run Inference: Process the image and text through the model to obtain object detection outputs.
5. Post-process and Display Results: Convert outputs to bounding boxes and confidence scores, and visualize the results.

For enhanced performance, especially with large datasets or complex queries, consider using cloud GPUs from providers like AWS or Google Cloud.

License

The OWLv2 model is licensed under the Apache-2.0 license, allowing for broad use and distribution with the requirement that modifications are documented.