GitHub Repository

Example scripts and notebooks for bootstrapping YOLO datasets with open-vocabulary object detection using free-text prompts and the Detect Anything API. Includes Python, Colab, and JS examples for easy dataset generation.

6 starsJupyter Notebook

Colab pipeline for auto-labeling datasets with prompt and training YOLO

Name: Colab pipeline for auto-labeling datasets with prompt and training YOLO
Availability: InStock
Author: eyasu6464

by eyasu6464·Mar 11, 2026·2 points·1 comment

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AI Analysis

●MidShip It

Notebook wrapper around an API, but Roboflow and Label Studio already do this.

Strengths

•One-click Colab pipeline removes local environment setup friction for training.
•Exports standard YOLO format, ensuring compatibility with existing deployment pipelines.
•Reduces manual annotation time for niche concepts using open-vocabulary prompts.

Weaknesses

•Requires proprietary API key, creating lock-in versus open models like GroundingDINO.
•Accuracy depends on external API, limiting reproducibility and offline use cases.

Post Description

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 dataset Generate bounding boxes using prompts (for example "cat's head" or "dent in car bumper") Filter positives and rebalance the dataset Export labels in YOLO format Train and evaluate a YOLO model

In the example notebook I use a cats vs dogs dataset with only image-level labels. Using the prompt "cat's and dog's head", the pipeline auto-generates head bounding boxes and trains a small YOLO model.

The repository mainly contains the Colab notebook plus example scripts for running the detection and exporting YOLO labels.

Curious to know what people think of this approach.