Run zero-shot prediction models on your data

Enhance your skills in prompt engineering for vision models. Learn to effectively prompt, fine-tune, and track experiments for models like SAM, OWL-ViT, and Stable Diffusion 2.0 to achieve precise image generation, segmentation, and object detection.

OWL-ViT module for Autodistill.

Vector with Advanced AI compatibilities

Annotation assistant tool that uses CLIP to find described objects in dataset and label them

This is a multimodal model design for the Vision Question Answering (VQA) task. It integrates the Llama2 13B, OWL-ViT, and YOLOv8 models.

Interactive OpenCV playground — chain image processing operations with live preview.

LLMs/LVMs/LMMs Prompt Engineering & Quantization

FastAPI service running YOLO instance segmentation on aerial imagery to extract ordered rooftop corner coordinates

Back-end model of Efficient Video Analytics, 2023 summer research intern

Zero-shot object detection system for visually impaired users using CLIP, OWL-ViT, and real-time audio feedback.

Implementing a solution for fall detection in videos, with specific emphasis on detecting falls accurately and minimizing false positives.