NanoNets Multi Label Classification Python Sample

Python Sample

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Build an Classifier for Natural Scene

Step 1: Clone the Repo

git clone https://github.com/NanoNets/multi-label-classification-sample-python
cd multi-label-classification-sample-python

Step 2: Get your free API Key

Get your free API Key from http://app.nanonets.com/user/api_key

Step 3: Set the API key as an Environment Variable

export NANONETS_API_KEY=YOUR_API_KEY_GOES_HERE

Step 4: Create a New Model

python ./code/create_model.py

_Note: This generates a MODEL_ID that you need for the next step

Step 5: Add Model Id as Environment Variable

export NANONETS_MODEL_ID=YOUR_MODEL_ID

_Note: you will get YOUR_MODEL_ID from the previous step

Step 6: Upload the Training Data

The training data is found in images (image files) and annotations (annotations for the image files)

python ./code/upload_training.py

Step 7: Train Model

Once the Images have been uploaded, begin training the Model

python ./code/train_model.py

Step 8: Get Model State

The model takes ~2 hours to train. You will get an email once the model is trained. In the meanwhile you check the state of the model

python ./code/model_state.py

Step 9: Make Prediction

Once the model is trained. You can make predictions using the model

python ./code/prediction.py PATH_TO_YOUR_IMAGE.jpg

Sample Usage:

python ./code/prediction.py ./multilabel_data/ImageSets/2795.jpg