5. Test your ModelVersion with Picsellia Evaluation Interface

5. Test your ModelVersion with Picsellia Evaluation Interface

At this point, the training of our ModelVersion should be completed. It is now time to use the Picsellia Evaluation Interface to determine how your newly trained ModelVersion is performing on a dedicated test set.

To accomplish this, we need to compute Evaluation and record them to the Picsellia Evaluation Interface at the end of our training script.

# ...

prediction = model.predict(ds_test) # Performing test on your testing dataset
picsellia_ds_test = experiment.get_dataset('test') # fetching picsellia test dataset
labels = list(ds_test.class_indices.keys()) # finding the class_names 
labels_picsellia = {k: picsellia_ds_test.get_label(k) for k in labels} # building the picsellia label mapping

for i, pred in enumerate(prediction):
    fname = ds_test.filenames[i].split('/')[-1]
    asset = picsellia_ds_test.find_asset(filename=fname)
    conf_score = float(np.max(pred))
    class_name = labels[np.argmax(pred)]
    picsellia_label = labels_picsellia[class_name]
    experiment.add_evaluation(asset, classifications=[(picsellia_label, conf_score)])

Let's take a look at each step:

  1. To perform testing, get the DatasetVersion you wish to use. In this case, the DatasetVersion will be referred to as test.
test_dataset = experiment.get_dataset('test')
  1. Get the Asset you want to attach the test result to:
asset = test_dataset.find_asset(filename="filename.png")
  1. Initialize an object with your Picsellia Label in order to reduce the amount of API calls to perform 🌱
labels_picsellia = { k: picsellia_ds_test.get_label(k) for k in labels }
  1. Retrieve the results of your ModelVersion (which can be a CLASSIFICATION, OBJECT_DETECTION, or SEGMENTATION) and match it with your Picsellia Label. Using the example of CLASSIFICATION
conf_score = float(np.max(pred)) 
class_name = label_names_list[np.argmax(pred)]
picsellia_label = labels_picsellia[class_name]
  1. Add the test results to Picsellia Evaluation Interface
experiment.add_evaluation(asset, classifications=[(picsellia_label, conf_score)])
  1. Compute evaluation metrics
job = experiment.compute_evaluations_metrics(InferenceType.CLASSIFICATION)