Qualcomm AI Hub Models give developers a fast way to test state‑of‑the‑art neural networks on real hardware, but getting started can be confusing. The main hurdles are installing the right packages, figuring out which models are available, preparing image tensors in the format the model expects, and moving from local PyTorch inference to a full cloud‑device workflow for compilation, profiling and on‑device execution.
First, install the hub package in your environment (for example, using pip install qai_hub_models). Once installed, list the available models to confirm that MobileNet‑V2, YOLOv7 or others are present. Load a pretrained model and read its input specification; this tells you the expected name, shape and data type of the input tensor.
A common issue is the tensor layout. Qualcomm models usually expect NCHW (batch, channels, height, width) while many utilities provide NHWC. Write a small helper that converts any incoming tensor to NCHW by moving the channel dimension to index 1 and ensuring the batch dimension exists. Apply this conversion before feeding data to the model.
Run inference on the model’s built‑in sample input to verify that the conversion works and to see the top‑5 predictions. Then download a real image, apply standard preprocessing (resize, center crop, tensor conversion), convert to NCHW and run another inference. Visualize the result to confirm that the prediction matches the content of the picture.
To go beyond classification, install the YOLOv7 extra (pip install qai_hub_models[yolov7]) and execute the official demo command. The demo will produce an output image with detections; locate the newest file in the output folder and display it.
If you have an API token for Qualcomm AI Hub, you can take the same PyTorch model to the cloud. Trace the model, submit a compile job for TFLite, run a profile job to see latency and memory usage, then submit an inference job on a real device (e.g., Samsung Galaxy S24). Download the output, compute top‑5 predictions and save the compiled .tflite file for later use.
These steps give you a complete, reproducible path from a blank notebook to hardware‑aware deployment, solving the typical pain points of package management, input formatting, local testing, demo execution and cloud‑device optimization.
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