Your first Computer vision model¶
Here is how you can quickly train an Object detection or Image classification model:
Install the required packages¶
First, you need the appropriate packages to be able to run the model. Luckily there is one code environment (see Code environments) containing all the appropriate packages already built for you, that you just need to install:
Go to the “Administration >> Settings >> Misc” tab. In the section “Computer vision code environment”, you can create either an Object detection or an Image classification code environment by selecting your Python interpreter and clicking on create the environment. It will install all the required packages for the corresponding task.
Create the analysis¶
Make sure to have the correct Computer vision analysis inputs before continuing.
Then create the analysis: select the Dataset then click on “Action panel >> Lab >> Object detection” (or Image classification depending on your need). Choose your target column and the image folder. Then click “Create” and you’re done !
Review the design of your model (optional)¶
If you want to train a model with the default parameters you can simply proceed by clicking the “Train” button. You will be asked whether or not you want to select GPUs for training.
If you prefer reviewing the design of the model first, here are some tips on what you can configure:
The Target tab displays a sample of your data with the class labels or bounding boxes previewed on the images. If the boxes or labels do not correspond with your image as you expect, you may have an issue with the format of your targets. See Computer vision analysis inputs for the expected format.
The Training tab allows you to change the default training parameters according to your data. See Model architectures & training parameters for the complete list of parameters available.
The Train / Test tab allows you to change the ratio of test samples compared to the training samples. See Settings: Train / Test set for more information.
The Metrics tab allows you to choose which metric will be optimized during training. See Evaluation Metrics.
The Data augmentation tab allows you to introduce some diversity in your training dataset, and visualize dynamically some examples of those augmentations on your images. See Data augmentation for more information.
You shouldn’t need to change anything in the Runtime Environment tab for now.
Once you’re ready, Click on “Train”, select your GPU settings (see GPU support) then click “Train” again. This will prepare your model then start the training loop.
Monitor the performance of your model during training¶
The chart displays the performance of your model at the end of each epoch, against your chosen metric. Once training completes, you can assess the performance of your model (see Performance assessment), then deploy, retrain and score it, like any other Visual Machine Learning model in DSS.