3/24/2024 0 Comments Apple CNN Animated Gif![]() ![]() I said we're using the RGB components of the image. In a future post, we'll introduce additional features to try to improve our results. I don't think this will work best with SVM, but in this first post we're starting as simple as possible, so we'll be using the RGB components of the image as our features. color histogram): the CNN worked perfectly fine with raw images. I didn't extracted any feature from them (e.g. Those few times I used CNN, I always used the whole image as input, as-is. I have to admit that I rarely use NN, so I may be wrong here, but from the examples I read online it looks to me that features engineering is not a fundamental task with NN. It will classify the current image based on the samples recorded during training.Īs any beginning machine learning project about image classification worth of respect, our task will be to distinguish an orange from an apple. This algorithm can't locate interesting objects in the image, neither detect if an object is present in the frame. In this context, image recognition means deciding which class (from the trained ones) the current image belongs to. Sure, we will still apply some restrictions to fit the problem on a microcontroller, but this is a huge step forward compared to the simple color identification. This is much more similar to the tasks you do on your PC with CNN or any other form of NN you are comfortable with. The objective of this post, instead, is to investigate if we can use the MicroML framework to do simple image recognition on the images from an ESP32 camera. ![]() Object inference, in that case, works only if you have exactly one object for a given color. Of course, such a process is not object recognition at all: yellow may be a banana, or a lemon, or an apple. In a previous post about color identification with machine learning, we used an Arduino to detect the object we were pointing at with a color sensor (TCS3200) by its color: if we detected yellow, for example, we knew we had a banana in front of us. Since in this series about machine learning on microcontrollers we're exploring the potential of support vector machines (SVMs) at solving different classification tasks, we'll take a look into image classification as well. Sadly, you can't run CNN on your ESP32, as they're just too large for a microcontroller. Convolutional neural networks really shines in this task and can achieve almost perfect accuracy on many scenarios. Image recognition is a very hot topic these days in the AI/ML landscape. In this post, we'll look into a very basic image recognition task: distinguish apples from oranges with machine learning. Want to do image recognition directly on your ESP32, without a PC? ![]()
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