eyasu6464

https://www.useful-ai-tools.com/tools/detect-anything/

I built a tool that detects objects in images by describing them in text.Instead of training a detector for a fixed set of classes, you can type what you want to find and the system returns bounding boxes for matching objects.Examples of prompts: "dented car bumper" "person wearing red backpack" "cat scratching couch" "broken window"The model is open-vocabulary, so it can generalize beyond...

https://github.com/useful-ai-tools/detect-anything

I built a Colab notebook that demonstrates a full pipeline for bootstrapping and training a YOLO object detection model without manual annotation.The workflow uses open-vocabulary object detection to generate bounding boxes from free-text prompts, which are then exported as YOLO labels and used to train a detector.Typical workflow in the notebook:Start with an unlabeled or weakly labeled image...

https://www.useful-ai-tools.com/tools/satellite-analysis-demo/

I built a browser-based tool for detecting objects in satellite imagery using vision-language models (VLMs). You draw a polygon on the map and enter a text prompt such as "swimming pools", "oil tanks", or "buses". The system scans the selected area tile-by-tile and returns detections projected back onto the map as GeoJSON.Pipeline: select area and zoom level, split the region into mercantile...

https://www.useful-ai-tools.com/tools/satellite-analysis-demo/

I built a browser-based tool that uses Vision-Language Models (VLMs) to detect objects in satellite imagery via natural language prompts. Draw a polygon on the map, type what you want to find (e.g., "swimming pools," "oil tanks," "solar panels"), and the system scans tile-by-tile, projecting bounding boxes back onto the globe as GeoJSON. The pipeline: pick zoom level + prompt → slice map into...