Merge pull request #454 from Mjrovai/366-labs-raspi-support

366 labs raspi support

contents/labs/raspi/image_classification/image_classification.qmd

@@ -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
 
@@ -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
 
@@ -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: