Download a public baseline model

Choose a pretrained baseline

Use mobilenet_v1_0.25_224_quant.tflite from the TensorFlow MobileNet V1 model catalog . It’s a public pretrained image classifier with a width multiplier of 0.25 and a fixed input shape of [1, 224, 224, 3].

The model has quantized eight-bit weights and activations, with unsigned eight-bit input and output tensors. It isn’t a neural upscaling model, but provides a real convolutional workload for learning the MLIA analysis workflow. You download the existing quantized export rather than retraining or calibrating a model.

Download and verify the model

From mlia-demo, download the versioned public archive and extract the LiteRT model. Name your local copy baseline.tflite:

    

        
        
mkdir -p models runs
curl -fL https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_0.25_224_quant.tgz -o models/baseline.tgz
tar -xzf models/baseline.tgz -C models ./mobilenet_v1_0.25_224_quant.tflite
mv models/mobilenet_v1_0.25_224_quant.tflite models/baseline.tflite
echo "ce8f61fe3e4e29560c4ea8038e4b91eb354be330fc428348b9645bd1e7de03bf  models/baseline.tflite" | sha256sum --check

    

The expected final output is:

    

        
        models/baseline.tflite: OK

        
    

Pass models/baseline.tflite directly to MLIA in the next section. MLIA runs the installed converter and saves the intermediate artifacts with its reports. Keep the downloaded model in your working directory; no model binaries need to be added to the Learning Paths repository.

What you’ve accomplished and what’s next

You’ve downloaded and verified the quantized MobileNet baseline. Next, you’ll check whether its operators are supported.