NanoNets Car Damage Inspection

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Car Damage Classification with Nanonets

A simple classifier used to classify images of damaged cars from non-damaged cars using the Nanonets API. You can find the example to train a model in python, by updating the api-key and model id in corresponding file.

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Build a Car Damage Classification Model

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-car-damage-classification.git
cd nanonets-car-damage-classification
sudo pip install nanonets

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: Upload Images For Training

The training data is found in images

python ./code/training.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: 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 7: 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 ./images/damaged-40.jpg

Note the python sample uses the converted json instead of the xml payload for convenience purposes, hence it has no dependencies.