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Absk024
Title
Enhanced Image Understanding with CLIP
Enhancement Aim
Integrate CLIP into the Computer Vision project to enable sophisticated image-text understanding, enhancing visual recognition and classification tasks through advanced language-vision models.
Changes
Features
Asynchronous Image Processing: Utilizes Python's
asyncioto load and classify images concurrently, improving performance when handling large batches.Dynamic Prompt Generation: Automatically generates classification prompts based on user-defined base terms, allowing for flexible and contextual image queries.
Confidence Thresholding: Filters classification results based on a user-defined confidence threshold, enhancing accuracy by omitting less certain predictions.
Multi-Modal Retrieval: Enables users to retrieve images based on textual descriptions and vice versa, offering a versatile tool for various multi-modal tasks.
Robust Error Handling: Includes comprehensive error handling and logging to help diagnose issues related to image loading and processing.
Batch Processing: Supports processing multiple images from a specified folder, making it suitable for large datasets.
GPU Acceleration: Automatically utilizes GPU for faster model inference if available, significantly improving processing times.
Screenshots š·
No response
Guidelines
Full Name
Dinmay Kumar Brahma
Participant Role