NanoNets Cracked Screen Detection Python Sample

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Cracked Screen Detection

We will use the Nanonets Image Classification API to determine from a picture of a phone whether it is damaged or not.

Note: Make sure you have python and pip installed on your system if you don't visit Python, pip

Step 1: Clone the Repo, Install dependencies

git clone https://github.com/NanoNets/nanonets-cracked-screen-detection.git
cd nanonets-cracked-screen-detection
sudo pip install requests tqdm

Step 2: Get your free API Key

Get your free API Key from http://app.nanonets.com/#/keys

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 the data directory

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 ./data/mobile_damaged/00000028.jpg