Truck Volume Estimator-Inference Pricing

1. Access to Model Package, SageMaker and S3 storage bucket.
2. Open Trailer Container Images. (Refer to Sample Input linked below)
3. Execution Role for the SageMaker session.
4. Python Packages as listed in the Instructions Notebook linked below.

Supported Content Type: 'application/json' (Image serialized to json as shown below in Python)

from PIL import Image import json import numpy as np img = Image.open('images/sample1.jpg').convert(mode = 'RGB') img = img.resize((300,300)) img = np.array(img).tolist() img_json = json.dumps({'instances': [{'input_image': img}]}) // If required can be written to file (Also can be found in Sample link below) with open('img.json', 'w') as f: f.write(img_json)

Content Type: 'application/json'

Sample Output & Interpretation:

{"predictions": [[0.04, 0.55, 0.41]]}

• Element 1 of the list represents probability of: Full Capacity (i.e ~ Above 75%)

• Element 2 of the list represents probability of: Half Capacity (i.e ~ Above 25% and Below 50%)

• Element 3 of the list represents probability of: Quarter Capacity (i.e ~ Below 25%)

If you are using real time inferencing, please create the endpoint first.

// Find detailed instructions in the Instructions Notebook lined below predictor = sage.RealTimePredictor(endpoint='endpoint name', content_type='application/json', sagemaker_session= sagemaker_session, ) prediction = predictor.predict(img_json) aws sagemaker-runtime invoke-endpoint --endpoint-name "endpoint-name" --body fileb://img.json --content-type application/json --accept application/json out.json

1. Download the Notebook from the link below onto a SageMaker Notebook Instance OR Install necessary packages on the desired compute resource.
2. Bring in the input images for classification onto the SageMaker Notebook Instance OR on the desired compute resource and follow the instructions in the Notebook.

Sample Input  Sample Notebook