DataDreamer

DataDreamer examples

🚀 Quickstart

To generate your dataset with custom classes, you need to execute only two commands:

pip install datadreamer
datadreamer --class_names person moon robot

🌟 Overview

DataDreamer is an advanced toolkit engineered to facilitate the development of edge AI models, irrespective of initial data availability. Distinctive features of DataDreamer include:

Synthetic Data Generation: Eliminate the dependency on extensive datasets for AI training. DataDreamer empowers users to generate synthetic datasets from the ground up, utilizing advanced AI algorithms capable of producing high-quality, diverse images.
Knowledge Extraction from Foundational Models: DataDreamer leverages the latent knowledge embedded within sophisticated, pre-trained AI models. This capability allows for the transfer of expansive understanding from these "Foundation models" to smaller, custom-built models, enhancing their capabilities significantly.
Efficient and Potent Models: The primary objective of DataDreamer is to enable the creation of compact models that are both size-efficient for integration into any device and robust in performance for specialized tasks.

✨ New: Pre-annotate Real Data with DataDreamer

DataDreamer helps you accelerate your annotation process by pre-annotating real data with minimal effort. Simply provide your dataset, and DataDreamer generates high-quality initial annotations for further refinement.

Available tasks: classification, object detection, and instance segmentation.

Example

Run the following to pre-annotate images in your dataset:

datadreamer --task instance-segmentation --image_annotator owlv2-slimsam --save_dir dataset_path --class_names dumpling --annotate_only

📚 Tutorial: Training a Semantic Segmentation Model using luxonis-train and DataDreamer

🛠️ Features

Prompt Generation: Automate the creation of image prompts using powerful language models.

Provided class names: ["horse", "robot"]

Generated prompt: "A photo of a horse and a robot coexisting peacefully in the midst of a serene pasture."
Image Generation: Generate synthetic datasets with state-of-the-art generative models.
Dataset Annotation: Leverage foundation models to label datasets automatically.
Edge Model Training: Train efficient small-scale neural networks for edge deployment. (not part of this library)

💻 Installation

There are two ways to install the datadreamer library:

Using pip:

To install with pip:

pip install datadreamer

Using Docker (for Linux/Windows):

Pull Docker Image from GHCR:

docker pull ghcr.io/luxonis/datadreamer:latest

Or build Docker Image from source:

# Clone the repository
git clone https://github.com/luxonis/datadreamer.git
cd datadreamer

# Build Docker Image
docker build -t datadreamer .

Run Docker Container (assuming it's GHCR image, otherwise replace ghcr.io/luxonis/datadreamer:latest with datadreamer)

Run on CPU:

docker run --rm -v "$(pwd):/app" ghcr.io/luxonis/datadreamer:latest --save_dir generated_dataset --device cpu

Run on GPU, make sure to have nvidia-docker installed:

docker run --rm --gpus all -v "$(pwd):/app" ghcr.io/luxonis/datadreamer:latest --save_dir generated_dataset --device cuda

These commands mount the current directory ($(pwd)) to the /app directory inside the container, allowing you to access files from your local machine.

⚙️ Hardware Requirements

To ensure optimal performance and compatibility with the libraries used in this project, the following hardware specifications are recommended:

GPU: A CUDA-compatible GPU with a minimum of 16 GB memory. This is essential for libraries like torch, torchvision, transformers, and diffusers, which leverage CUDA for accelerated computing in machine learning and image processing tasks.
RAM: At least 16 GB of system RAM, although more (32 GB or above) is beneficial for handling large datasets and intensive computations.

📋 Usage

The datadreamer/pipelines/generate_dataset_from_scratch.py (datadreamer command) script is a powerful tool for generating and annotating images with specific objects. It uses advanced models to both create images and accurately annotate them with bounding boxes for designated objects.

Run the following command in your terminal to use the script:

datadreamer --save_dir <directory> --class_names <objects> --prompts_number <number> [additional options]

or using a .yaml config file

datadreamer --config <path-to-config>

🎯 Main Parameters

--save_dir (required): Path to the directory for saving generated images and annotations.
--class_names (required): Space-separated list of object names for image generation and annotation. Example: person moon robot.
--prompts_number (optional): Number of prompts to generate for each object. Defaults to 10.
--annotate_only (optional): Only annotate the images without generating new ones, prompt and image generator will be skipped. Defaults to False.

🔧 Additional Parameters

--task: Choose between detection, classification and instance-segmentation. Default is detection.
--dataset_format: Format of the dataset. Defaults to raw. Supported values: raw, yolo, coco, voc, luxonis-dataset, cls-single.
--split_ratios: Split ratios for train, validation, and test sets. Defaults to [0.8, 0.1, 0.1].
--num_objects_range: Range of objects in a prompt. Default is 1 to 3.
--prompt_generator: Choose between simple, lm (Mistral-7B), tiny (tiny LM), and qwen2 (Qwen2.5 LM). Default is qwen2.
--image_generator: Choose image generator, e.g., sdxl, sdxl-turbo, sdxl-lightning or shuttle-3. Default is sdxl-turbo.
--image_annotator: Specify the image annotator, like owlv2 for object detection or aimv2 or clip for image classification or owlv2-slimsam and owlv2-sam2 for instance segmentation. Default is owlv2.
--conf_threshold: Confidence threshold for annotation. Default is 0.15.
--annotation_iou_threshold: Intersection over Union (IoU) threshold for annotation. Default is 0.2.
--prompt_prefix: Prefix to add to every image generation prompt. Default is "".
--prompt_suffix: Suffix to add to every image generation prompt, e.g., for adding details like resolution. Default is ", hd, 8k, highly detailed".
--negative_prompt: Negative prompts to guide the generation away from certain features. Default is "cartoon, blue skin, painting, scrispture, golden, illustration, worst quality, low quality, normal quality:2, unrealistic dream, low resolution, static, sd character, low quality, low resolution, greyscale, monochrome, nose, cropped, lowres, jpeg artifacts, deformed iris, deformed pupils, bad eyes, semi-realistic worst quality, bad lips, deformed mouth, deformed face, deformed fingers, bad anatomy".
--use_tta: Toggle test time augmentation for object detection. Default is False.
--synonym_generator: Enhance class names with synonyms. Default is none. Other options are llm, wordnet.
--use_image_tester: Use image tester for image generation. Default is False.
--image_tester_patience: Patience level for image tester. Default is 1.
--lm_quantization: Quantization to use for Mistral language model. Choose between none and 4bit. Default is none.
--annotator_size: Size of the annotator model to use. Choose between base and large. Default is base.
--disable_lm_filter: Use only a bad word list for profanity filtering (LM check disabled). Default is False.
--disable_profanity_filter: Disable profanity filtering entirely. Default is False.
`--keep_unlabe

Datadreamer

Install / Use

README