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Images and video restoration in multiple-stages using MIRNETv2 model, additionally object detection on images and video through FASTER-RCNN . And complete web application in flask including responsive front-end
Welcome to the project on downloading the COCO dataset from a JSON file! This application was developed with one goal in mind: to provide an educational and entertaining solution for obtaining data from the famous COCO (Common Objects in Context) dataset.
This application eliminates a set of given elements from a serial video resource. You can directly set some classes and qualifications for filtering options also, there also exixst an sql output for schemes.
Developed an image captioning system using the BLIP model to generate detailed, context-aware captions. Achieved an average BLEU score of 0.72, providing rich descriptions that enhance accessibility and inclusivity.
Demonstrates real-time object detection using the YOLOv8 pre-trained model. The script utilizes the YOLOv8 model to identify objects in a live video stream captured from the user's webcam.