Lobe is an easy-to-use tool that has everything you need to bring your machine learning ideas to life. Show it examples of what you want it to do, and it automatically trains a custom machine learning model that can be exported for edge devices and apps. It doesn’t require any experience to get started. You can train on your own computer for free!
Here's a quick overview on how to use Lobe:
2. Take or import photos and label them into appropriate categories. We'll need these labels later on in the software part of the project.
There are two ways to import photos:
- Take photos of items directly from your computer webcam, or
- Import existing photos from your computer (via single photo or folder/dataset upload).
- Keep in mind that the photo folder name will be used as the category label name, so make sure it matches any existing labels!
Aside: I ended up using both methods, since the more photos you have, the more accurate your model is.
3. Use the "Play" feature to test the model accuracy. Change distances, lighting, hand positions, etc. to identify where the model is and is not accurate. Add more photos as necessary.
- Before importing photos, make a list of all the categories you'll need and how you want to label them (e.g. "garbage," "recycle," "compost," etc.)
- Note: Use the same labels as shown in "Lobe Model Labels" photo above to reduce the amount of code you need to change.
- Include a category for "not trash" that has photos of whatever else might be in the photo (e.g. your hands and arms, the background, etc.)
- If possible, take photos from the Pi Camera and import into Lobe. This will greatly improve the accuracy of your model!
- Need more photos? Check out open-source datasets on Kaggle, including this garbage classification image set!
- Need more help? Connect with the Lobe Community on Reddit!