02-Text-Prompted Object Detection with Grounding DINO (Google Colab)

In this video we build the first half of the annotation pipeline from scratch in Google Colab: Grounding DINO for open-vocabulary object detection.

Grounding DINO can detect any object you describe in plain English, with no task-specific training. We load the model via Hugging Face Transformers, test it on a natural COCO image to confirm it works, then push it into territory it was not designed for: an H&E kidney section and a Lucchi electron microscopy stack.

Along the way we work through every practical detail you need for real use: how to format text prompts correctly, what the box threshold and NMS threshold actually control, how to filter out whole-image false positives, and how to interpret confidence scores. We show the results honestly — including where detection fails and why.

The notebook is ready to run with a free Colab T4 GPU. No prior experience with object detection required.

Notebook: https://github.com/bnsreenu/LLM-Assisted-Scientific-Image-Annotation-Tool/blob/main/01_grounding_dino_bboxes.ipynb

#GroundingDINO #ObjectDetection #ZeroShot #GoogleColab #Python #DeepLearning #ImageAnnotation #Microscopy #Pathology #AIforScience

Видео 02-Text-Prompted Object Detection with Grounding DINO (Google Colab) канала DigitalSreeni