Setting up model and learner for multilabel classification model deployment

[kodzaks (Natalija Lace)]

Sorry, I am still a bit confused. I found your reply to a similar thread:

Deploy Multi-label classifier into production

I’m working on deploying every model The Render walkthrough is a great way to deploy most every type of vision model I’ve found, as they all rely on the same bit of boilerplate code. Let’s take a look at what would be different from our analyze function between the two. First, single classification based on the example from the course: @app.route('/analyze', methods=["POST"]) data = await request.form() bytes = await (data['file'].read()) pred = learn.predict(bytes)[0] r…

Also what about classes= in here:

model_file_url = ‘www’
model_file_name = ‘model’
classes = [‘Class1’, ‘Class2’, ‘Class3’]
path = Path( file ).parent

Do we still need to list all multilabels categories here?

Just to confirm, I am talking about multilabel , like in this example, only in my case there could be 4 to 6 multilabel tags in a single image.

How to use Interpretation API in case of multilabeled dataset

First of all, thanks for your help, I really appreciate it ! Here’s the information about the dataset. Number of images: training set: 580 images validation set: 70 images [4] Both the training and validation images are contained in a single folder. I’ve used a text file to identify the images of the validation set. Labelling - I’ve tried to use the same labelling system as the one in the planet dataset: a csv file, with 2 columns: column 1: contains the images’ names column 2: conta…