From e7d80eafedb1f96f3bd2761d779a5fe5e9d637f1 Mon Sep 17 00:00:00 2001 From: Vijay Janapa Reddi Date: Sun, 3 Nov 2024 09:40:29 -0500 Subject: [PATCH] Improving documentation --- contents/labs/labs.qmd | 12 +++++++++++- 1 file changed, 11 insertions(+), 1 deletion(-) diff --git a/contents/labs/labs.qmd b/contents/labs/labs.qmd index 013c0388..970e1ebd 100644 --- a/contents/labs/labs.qmd +++ b/contents/labs/labs.qmd @@ -34,6 +34,12 @@ These labs are designed for: ## Supported Devices +We have included laboratory materials for three key devices that represent different hardware profiles and capabilities. + +* Nicla Vision: Optimized for vision-based applications like image classification and object detection, ideal for compact, low-power use cases. +* XIAO ESP32S3: A versatile, compact board suitable for keyword spotting and motion detection tasks. +* Raspberry Pi: A flexible platform for more computationally intensive tasks, including small language models and various classification and detection applications. + +----------------------------+----------------------------------------------------------------+----------------------------------------------------------------------------+----------------------------------------------+ | Exercise | [Nicla Vision](https://store.arduino.cc/products/nicla-vision) | [XIAO ESP32S3](https://wiki.seeedstudio.com/xiao_esp32s3_getting_started/) | [Raspberry Pi](https://www.raspberrypi.com/) | +:===========================+:===============================================================+:===========================================================================+:=============================================+ @@ -63,7 +69,11 @@ Each lab follows a structured approach: 4. **Exercises**: Hands-on tasks to modify and experiment with model parameters. 5. **Discussion**: Analysis of results, potential improvements, and practical insights. - + +## Recommended Lab Sequence + +If you're new to embedded ML, we suggest starting with setup and keyword spotting before moving on to image classification and object detection. Raspberry Pi users can explore more advanced tasks, like small language models, after familiarizing themselves with the basics. + ## Troubleshooting and Support If you encounter any issues during the labs, consult the troubleshooting comments or check the FAQs within each lab. For further assistance, feel free to reach out to our support team or engage with the community forums.