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Image Classifier Using SDK - Uploading and Training the Model

Image Classifier Using SDK - Uploading and Training the Model

Learn to build an Image Classifier using the Azure Custom Vision SDK for Python.

We'll cover the following...

Let’s continue the implementation part. Just to recap, in the previous lesson, we’ve created the training and prediction client objects. We’ve also created a project and added the two image tags that we’re interested in.

Now, we’re going to upload the images with their tags to our Custom Vision project and then we’ll train our custom model.

Uploading the images with their tags

Now let’s upload the images and their corresponding tags to the Custom Vision project.

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base_image_location = "CourseAssets/ImageClassification/Images"
print("Adding images...")
image_list = []
for image_num in range(1, 11):
    file_name = "hemlock_{}.jpg".format(image_num)
    with open(base_image_location + "/Hemlock/" + file_name, "rb") as hemlock_image:
        image_list.append(
                ImageFileCreateEntry(
                    name = file_name, 
                    contents = hemlock_image.read(), 
                    tag_ids= [hemlock_tag.id]
                )
            )
for image_num in range(1, 11):
    file_name = "japanese_cherry_{}.jpg".format(image_num)
    with open(base_image_location + "/Japanese_Cherry/" + file_name, "rb") as cherry_image:
        image_list.append(
                ImageFileCreateEntry(
                    name = file_name, 
                    contents = cherry_image.read(), 
                    tag_ids = [cherry_tag.id]
                )
            )
upload_result = trainer.create_images_from_files(
                        project.id, 
                        ImageFileCreateBatch(images = image_list)
                    )
if not upload_result.is_batch_successful:
    print("Image batch upload failed.")
    for image in upload_result.images:
        print("Image status: ", image.status)
else:
    print("Imaged Added Successfully!!")

  • In line 1, we define the location of the “Images” folder present in our working directory.

  • In line 5, we define a list named image_list that will ...