It's weird, for some reason GCP Vision won't allow me to train my model. I have met the minimum of 10 images per label, no images unlabeled and tried uploading a CSV pointing to 3 of this labels images as VALIDATION images.. Yet I get this error
Some of your labels (e.g. ‘Label1’) do not have enough images assigned to your Validation sets. Import another CSV file and assign those images to those sets.
any ideas would be appreciated
This error generally occurs when you did not labelled all the images because AutoML divides your images, including the mislabelled ones, into the categories and this error is triggered when the unlabelled images go to the VALIDATION set.
According to the documentation, it is recomended 1000 images per label. However, the minimum is 10 images for each label or 50 for complex cases. In addition,
The model works best when there are at most 100x more images for the most common label than for the least common label. We recommend removing very low frequency labels.
Furthermore, AutoML Vision uses the 80% of your content documents for training, 10% for validating, and 10% for testing. Since your images were not divided into these three categories, you should manually assign them to TRAIN, VALIDATION and TEST. You can do that by, uploading your images to a GCS bucket and referencing each labelled image in a .csv file, as follows:
TRAIN, gs://my_bucket/image1.jpeg,cat
As you can see above, it follows the format [SET],[GCS image path], [Label]. Note that you will be dividing your dataset manullay and it should respect the percentages already mentioned. Thus, you will have enough data in each category. You can follow the steps for preparing your training data here and here.
Note: please be aware that your .csv file is case sentive.
Lastly, in order to validate your dataset and inspect labelled/unlabelled images you can export the created dataset and check the exported .csv file. You can do it as described in the documentation. After exporting, download it and verify each SET( TRAIN, VALIDATION and TEST).
