CLIP is a versatile vision-language model trained to associate images with text descriptions using contrastive learning. This notebook demonstrates CLIP's capabilities for zero-shot object detection by using a pretrained model to recognize objects without training on categorized image datasets. The model's understanding of visual concepts and language enables real-world computer vision applications.
To run the scripts in this repository, you will need to have the following dependencies installed:
- Python 3
- PyTorch
- Matplotlib
- Clip
- NumPy
CLIP is a neural network trained on a 400 million image-text pairs dataset to learn visual concepts and their textual representations through contrastive learning. By predicting the correct text for a given image and vice versa, CLIP builds connections between visual and language concepts without the need for labeling or categorization. This self-supervised approach produces a general-purpose model that excels at image-language tasks like classification and object detection.
Object candidate regions are first extracted from the input images using a Faster R-CNN model's pretrained Region Proposal Network. This rapidly identifies potential regions of interest. CLIP then encodes these proposed regions and textual object queries into high-dimensional embeddings suitable for comparison. Multiple phrasings of each query are averaged to obtain a robust representation. Finally, cosine similarity between regional embeddings and averaged query embeddings identifies the most semantically aligned regions for each specified object class.
Below is the original image
The boxes extracted from the original image by Faster R-CNN are presented below
The following are the objects detected by CLIP
