Next, create a new project:ĭo not forget to 'Save parameters.' This will generate the features to be used in training. We will use the Edge Impulse for training, a leading development platform for machine learning on edge devices.Įnter your account credentials (or create a free account) at Edge Impulse. Training the model with Edge Impulse Studio Download the dataset from the Kaggle website to your computer.Vegetables - cucumber, carrot, capsicum, onion, potato, lemon, tomato, radish, beetroot, cabbage, lettuce, spinach, soybean, cauliflower, bell pepper, chili pepper, turnip, corn, sweetcorn, sweet potato, paprika, jalepeño, ginger, garlic, peas, eggplant.Įach category is split into the train (100 images), test (10 images), and validation (10 images).Fruits - banana, apple, pear, grapes, orange, kiwi, watermelon, pomegranate, pineapple, mango.This dataset contains images of the following food items: So, let's find a specific dataset that includes images from those categories. We will differentiate apples from bananas and potatoes (you can try other categories). With TinyML, a set of technics associated with machine learning inference on embedded devices, we should limit the classification to three or four categories due to limitations (mainly memory in this situation). But first of all, we need a goal! What do we want to classify? For training, we should find some data (in fact, tons of data!). We should start training a model and proceed with inference on the ESP32-CAM. Now that we have an embedded camera running, it is time to try image classification. On the Serial monitor, you should see the wifi networks (SSIDs and RSSIs) in the range of your device. Go to Arduino IDE Examples and look for WiFI => WiFIScan You can do it by running one of the code examples on the board.

So, let's test its radio, scanning the wifi networks around it. One of the sound characteristics of the ESP32S is its WiFi capability. So, change the Blink sketch accordantly: #define LED_BUILT_IN 33 void setup() Note that the LED is located under the board. The ESP32-CAM has a built-in LED that is connected to GPIO33. Last but not least, select the Port where the ESP32-Cam is connected.

Download esp32 cam pinout for free