Running a pre-converted neural network

To run a image classification neural network on a random image using an evaluation kit:

1. Install a pre-built image and connect to the board as described here

2. On the target first go to the model directory:

   $ cd /usr/share/synap/models/image_classification/imagenet/model/mobilenet_v2_1.0_224_quant/
   

3. Then test the model inference time using random inputs:

   $ synap_cli random
   Flush/invalidate: yes
   Loop period (ms): 0
   Network inputs: 1
   Network outputs: 1
   Input buffer: input size: 150528 : random
   Output buffer: output size: 1001
   
   Predict #0: 12.61 ms
   
   Inference timings (ms):  load: 28.37  init: 66.99  min: 12.60  median: 12.60  max: 12.60  stddev: 0.00  mean: 12.60
   

4. Then test the model accuracy with a sample image:

   $ synap_cli_ic ../../sample/goldfish_224x224.jpg
   
   Loading network: model.nb
   Input image: ../../sample/goldfish_224x224.jpg
   Classification time: 3.15 ms (pre:0.56, inf:2.53, post:0.05)
   Class  Confidence  Description
       1     18.9874  goldfish, Carassius auratus
     112      9.2959  conch
     927      8.7025  trifle
      29      8.2081  axolotl, mud puppy, Ambystoma mexicanum
     122      7.7136  American lobster, Northern lobster, Maine lobster, Homarus americanus
   

To learn about other ai demos refer to Machine Learning with SyNAP.