Optimize DeepFace's speed on low-performance hardware
DeepFace, as a deep learning tool library, may face speed bottlenecks when running on CPU devices. Here are a few proven optimization solutions:
- Pre-calculated facial embedding: For database scenarios that require repetitive queries, preemptively pass the
DeepFace.represent()Calculate all facial feature vectors and save them as a pickle file, load the embedded data directly for subsequent queries - Model Selection: Choose a lightweight model such as GhostFaceNet instead of VGG-Face, which can be specified at initialization.
model_name='GhostFaceNet' - batch control: Used when analyzing multiple images
batch_sizeParameters control the number of treatments in a single pass (recommendations 4-8) - Image Preprocessing: By
enforce_detection=FalseSkip strict face detection, or useresampleReduced input resolution
For more thorough optimization, consider 1) using the ONNX Runtime to accelerate inference, 2) using a quantized version of the model, and 3) compiling and installing a specially optimized version of TensorFlow Lite on a device such as a Raspberry Pi.
This answer comes from the articleDeepFace: a lightweight Python library that implements facial age, gender, emotion, race recognitionThe
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