Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

366 labs raspi support #454

Merged
merged 3 commits into from
Sep 17, 2024
Merged

366 labs raspi support #454

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
0deb503
Add files via upload
Mjrovai Sep 16, 2024
3043102
correcting Typos
Mjrovai Sep 16, 2024
98b51bb
Correcting Typos
Mjrovai Sep 16, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
6 changes: 3 additions & 3 deletions contents/labs/raspi/image_classification/image_classification.qmd
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -943,7 +943,7 @@ The final dense layer of our model will have 0 neurons with a 10% dropout for ov

![](images/png/result-train.png)

The result is excellent, with a reasonable 35ms of latency (for a Rasp-4), which should result in around 30 fps (frames per second) during inference. A Raspi-Zero should be slower, and the Rasp-5, faster.
The result is excellent, with a reasonable 35ms of latency (for a Raspi-4), which should result in around 30 fps (frames per second) during inference. A Raspi-Zero should be slower, and the Raspi-5, faster.

### Trading off: Accuracy versus speed

Expand Down Expand Up @@ -1213,7 +1213,7 @@ Let's download a smaller model, such as the one trained for the [Nicla Vision La

![](images/png/infer-int8-96.png)

The model lost some accuracy, but it is still OK once our model does not look for many details. Regarding latency, we are around **ten times faster** on the Rasp-Zero.
The model lost some accuracy, but it is still OK once our model does not look for many details. Regarding latency, we are around **ten times faster** on the Raspi-Zero.

## Live Image Classification

Expand Down Expand Up @@ -1505,7 +1505,7 @@ The code creates a web application for real-time image classification using a Ra

## Conclusion:

Image classification has emerged as a powerful and versatile application of machine learning, with significant implications for various fields, from healthcare to environmental monitoring. This chapter has demonstrated how to implement a robust image classification system on edge devices like the Raspi-Zero and Rasp-5, showcasing the potential for real-time, on-device intelligence.
Image classification has emerged as a powerful and versatile application of machine learning, with significant implications for various fields, from healthcare to environmental monitoring. This chapter has demonstrated how to implement a robust image classification system on edge devices like the Raspi-Zero and Raspi-5, showcasing the potential for real-time, on-device intelligence.

We've explored the entire pipeline of an image classification project, from data collection and model training using Edge Impulse Studio to deploying and running inferences on a Raspi. The process highlighted several key points:

Expand Down
Loading