# Visual Wake Words Dataset

arXiv: https://arxiv.org/abs/1906.05721 · HTML: https://mlsanity.com/paper/1906.05721/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2019.

## Built on by (most cited first, 30 of 46)

- [On-Device Training Under 256KB Memory](https://mlsanity.com/paper/2206.15472/) (2022)
- [MLPerf Tiny Benchmark](https://mlsanity.com/paper/2106.07597/) (2021)
- [MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep Learning](https://mlsanity.com/paper/2110.15352/) (2021)
- [Machine Learning for Microcontroller-Class Hardware: A Review](https://mlsanity.com/paper/2205.14550/) (2022)
- [Edge Impulse: An MLOps Platform for Tiny Machine Learning](https://mlsanity.com/paper/2212.03332/) (2022)
- [Widening Access to Applied Machine Learning with TinyML](https://mlsanity.com/paper/2106.04008/) (2021)
- [Differentiable Network Pruning for Microcontrollers](https://mlsanity.com/paper/2110.08350/) (2021)
- [P2M: A Processing-in-Pixel-in-Memory Paradigm for Resource-Constrained TinyML Applications](https://mlsanity.com/paper/2203.04737/) (2022)
- [On-device Federated Learning with Flower](https://mlsanity.com/paper/2104.03042/) (2021)
- [Intelligence at the Extreme Edge: A Survey on Reformable TinyML](https://mlsanity.com/paper/2204.00827/) (2022)
- [Quantization-Guided Training for Compact TinyML Models](https://mlsanity.com/paper/2103.06231/) (2021)
- [AnalogNAS: A Neural Network Design Framework for Accurate Inference with Analog In-Memory Computing](https://mlsanity.com/paper/2305.10459/) (2023)
- [On TinyML and Cybersecurity: Electric Vehicle Charging Infrastructure Use Case](https://mlsanity.com/paper/2404.16894/) (2024)
- [AnalogNets: ML-HW Co-Design of Noise-robust TinyML Models and Always-On Analog Compute-in-Memory Accelerator](https://mlsanity.com/paper/2111.06503/) (2021)
- [Enhancing Neural Architecture Search with Multiple Hardware Constraints for Deep Learning Model Deployment on Tiny IoT Devices](https://mlsanity.com/paper/2310.07217/) (2023)
- [TinyFedTL: Federated Transfer Learning on Tiny Devices](https://mlsanity.com/paper/2110.01107/) (2021)
- [Accelerating Deep Learning Model Inference on Arm CPUs with Ultra-Low Bit Quantization and Runtime](https://mlsanity.com/paper/2207.08820/) (2022)
- [Towards On-device Learning on the Edge: Ways to Select Neurons to Update under a Budget Constraint](https://mlsanity.com/paper/2312.05282/) (2023)
- [Machine Learning Sensors](https://mlsanity.com/paper/2206.03266/) (2022)
- [T-RECX: Tiny-Resource Efficient Convolutional neural networks with early-eXit](https://mlsanity.com/paper/2207.06613/) (2022)
- [LLS: Local Learning Rule for Deep Neural Networks Inspired by Neural Activity Synchronization](https://mlsanity.com/paper/2405.15868/) (2024)
- [TinySV: Speaker Verification in TinyML with On-device Learning](https://mlsanity.com/paper/2406.01655/) (2024)
- [On-Device Training of Fully Quantized Deep Neural Networks on Cortex-M Microcontrollers](https://mlsanity.com/paper/2407.10734/) (2024)
- [Mixed-precision Neural Networks on RISC-V Cores: ISA extensions for Multi-Pumped Soft SIMD Operations](https://mlsanity.com/paper/2407.14274/) (2024)
- [TENT: Efficient Quantization of Neural Networks on the tiny Edge with Tapered FixEd PoiNT](https://mlsanity.com/paper/2104.02233/) (2021)
- [Toward Efficient Hyperspectral Image Processing inside Camera Pixels](https://mlsanity.com/paper/2203.05696/) (2022)
- [Depth Pruning with Auxiliary Networks for TinyML](https://mlsanity.com/paper/2204.10546/) (2022)
- [DTMM: Deploying TinyML Models on Extremely Weak IoT Devices with Pruning](https://mlsanity.com/paper/2401.09068/) (2024)
- [Pex: Memory-efficient Microcontroller Deep Learning through Partial Execution](https://mlsanity.com/paper/2211.17246/) (2022)
- [MicroFlow: An Efficient Rust-Based Inference Engine for TinyML](https://mlsanity.com/paper/2409.19432/) (2024)

## Abstract (arXiv)

The emergence of Internet of Things (IoT) applications requires intelligence on the edge. Microcontrollers provide a low-cost compute platform to deploy intelligent IoT applications using machine learning at scale, but have extremely limited on-chip memory and compute capability. To deploy computer vision on such devices, we need tiny vision models that fit within a few hundred kilobytes of memory footprint in terms of peak usage and model size on device storage. To facilitate the development of microcontroller friendly models, we present a new dataset, Visual Wake Words, that represents a common microcontroller vision use-case of identifying whether a person is present in the image or not, and provides a realistic benchmark for tiny vision models. Within a limited memory footprint of 250 KB, several state-of-the-art mobile models achieve accuracy of 85-90% on the Visual Wake Words dataset. We anticipate the proposed dataset will advance the research on tiny vision models that can push the pareto-optimal boundary in terms of accuracy versus memory usage for microcontroller applications.