Large-Scale, Diverse Dataset and Benchmark Suite for Tiny Machine Learning Person Detection

核心概念

The Wake Vision dataset and benchmark suite enable robust and fair tiny machine learning person detection models by providing a large-scale, high-quality dataset and fine-grained evaluation tools.

要約

The paper introduces the Wake Vision dataset and benchmark suite for tiny machine learning (TinyML) person detection.
Wake Vision is a large-scale, diverse dataset with over 6 million images, which is about 100 times larger than the previous standard dataset, Visual Wake Words (VWW). The dataset is derived from the Open Images dataset and is permissively licensed. Wake Vision demonstrates a 2.41% accuracy improvement over VWW on the person detection task.
To enable detailed evaluation of TinyML person detection models, the authors introduce a suite of five fine-grained benchmarks that assess model performance on specific scenarios, such as varying lighting conditions, distances from the camera, and demographic characteristics of subjects. These benchmarks reveal important insights that are often obscured when focusing solely on overall accuracy.
The authors use the benchmark suite to conduct a case study on the effects of typical TinyML compression techniques, such as input image size and model width scaling, as well as quantization. The results show that fine-grained analysis is crucial, as some optimization techniques can have a disproportionate impact on specific subsets of the data.
The Wake Vision dataset, benchmark suite, code, and models are publicly available under the CC-BY 4.0 license, enabling their use for commercial and research purposes.

統計

The Wake Vision dataset contains over 6 million images, which is about 100 times larger than the previous standard dataset, Visual Wake Words (VWW).
The Wake Vision (Quality) training set, which uses bounding box labels, outperforms the larger Wake Vision (Large) training set, which uses image-level labels, by 2.65% in test accuracy.
The Wake Vision test set has an estimated label error rate of 6.8%, compared to 7.8% for the VWW dataset.

引用

"Wake Vision has over 6M images, ~100x more than the prior state-of-the-art person detection dataset, Visual Wake Words (VWW)."
"Wake Vision demonstrates a 2.41% accuracy improvement over VWW."
"The Wake Vision benchmark suite can be used to investigate the effects of different types of input and model scaling and highlight the importance of fine grain analysis when designing a model."

抽出されたキーインサイト

Wake Vision: A Large-scale, Diverse Dataset and Benchmark Suite for TinyML Person Detection

by Colby Banbur... 場所 arxiv.org 05-03-2024

https://arxiv.org/pdf/2405.00892.pdf

深掘り質問

How can the Wake Vision dataset and benchmark suite be extended to support other TinyML tasks beyond person detection?

The Wake Vision dataset and benchmark suite can be extended to support other TinyML tasks by following a similar methodology of data collection, labeling, and benchmark creation tailored to the specific task at hand. Here are some key steps to extend the dataset and benchmarks:

Task-specific Data Collection: Identify a new TinyML task and gather a large-scale dataset of images or data relevant to that task. Ensure the dataset is diverse, representative, and of high quality.

Label Generation: Develop a labeling scheme specific to the new task, ensuring accurate annotations for training models. Consider leveraging existing datasets or labeling tools to streamline this process.

Quality Filtering: Implement quality filtering techniques to remove noisy or irrelevant data from the dataset. This step is crucial to ensure the dataset's integrity and effectiveness for training TinyML models.

Benchmark Creation: Design fine-grained benchmarks that evaluate model performance across various challenging scenarios specific to the new task. Consider factors like lighting conditions, distances, demographic characteristics, and any other relevant variables.

Model Training and Evaluation: Train TinyML models on the extended dataset and evaluate their performance using the newly created benchmarks. Compare the results with existing benchmarks to assess improvements and identify areas for further enhancement.

Public Availability: Make the extended dataset, benchmarks, code, and models publicly available under permissive licenses to encourage research and collaboration in the TinyML community.

By following these steps and adapting the Wake Vision methodology to new TinyML tasks, researchers can create valuable resources for advancing research and development in various domains beyond person detection.

What are the potential biases and limitations of the demographic labels used in the fine-grained benchmarks, and how can they be addressed?

The demographic labels used in the fine-grained benchmarks of the Wake Vision dataset may introduce biases and limitations that need to be carefully considered and addressed. Some potential biases and limitations include:

Representation Bias: The demographic labels may not fully represent the diversity of individuals in the real world, leading to underrepresentation or misrepresentation of certain groups.

Labeling Bias: Human annotators may introduce subjective biases when labeling demographic attributes such as gender and age, impacting the accuracy and fairness of the benchmarks.

Intersectional Bias: The intersection of multiple demographic attributes (e.g., gender and age) may introduce complex biases that are challenging to capture and mitigate.

To address these biases and limitations, the following strategies can be implemented:

Diverse Labeling Team: Ensure a diverse team of annotators from various backgrounds to reduce individual biases and improve the accuracy of demographic labels.

Intersectional Analysis: Conduct in-depth analysis of how different demographic attributes intersect and impact model performance to identify and mitigate biases effectively.

Bias Mitigation Techniques: Implement bias detection and mitigation techniques such as fairness-aware training, data augmentation, and debiasing algorithms to reduce biases in the dataset and models.

Regular Auditing: Continuously audit the dataset and benchmarks for biases, regularly updating and refining the labeling process to improve fairness and inclusivity.

By proactively addressing potential biases and limitations in the demographic labels used in the fine-grained benchmarks, researchers can enhance the reliability and ethical integrity of their TinyML models and applications.

How can the dataset creation and benchmark generation process be further automated and scaled to support the rapid development of TinyML applications?

Automating and scaling the dataset creation and benchmark generation process is essential for supporting the rapid development of TinyML applications. Here are some strategies to achieve this:

Automated Data Collection: Implement web scraping tools, APIs, or data pipelines to automatically collect and curate large-scale datasets from diverse sources relevant to the TinyML task.

Labeling Automation: Utilize machine learning algorithms for semi-supervised or active learning to automate the labeling process, reducing manual effort and improving labeling efficiency.

Quality Assurance Algorithms: Develop algorithms for automated data quality assessment, outlier detection, and error correction to ensure the dataset's integrity and reliability.

Benchmark Template Generation: Create templates or scripts to automatically generate fine-grained benchmarks based on specific criteria, such as lighting conditions, distances, and demographic attributes.

Scalable Infrastructure: Leverage cloud computing resources and distributed computing frameworks to scale up dataset processing, model training, and benchmark evaluation for faster iteration and development.

Continuous Integration and Deployment (CI/CD): Implement CI/CD pipelines for automated testing, validation, and deployment of models trained on the dataset, streamlining the development cycle.

Community Collaboration: Foster collaboration with the TinyML community to crowdsource data collection, labeling, and benchmark creation, accelerating the process and ensuring diverse perspectives.

By incorporating these automation and scaling strategies into the dataset creation and benchmark generation process, researchers can expedite the development of TinyML applications, improve efficiency, and facilitate innovation in the field.