1. Model Quantization (INT8)

Goal: Reduce model size and speed up inference by converting floating-point (FP32) to integer (INT8) operations.

PyTorch (for OSNet/MobileNet)

import torch
from torchreid.models import build_model

# Load pre-trained model
model = build_model("osnet_x0_25", pretrained=True)

# Dynamic Quantization (CPU only)
quantized_model = torch.quantization.quantize_dynamic(
							    model, {torch.nn.Linear}, dtype=torch.qint8
									)
torch.save(quantized_model.state_dict(), "osnet_quantized.pt")

TensorFlow Lite (for MobileNetV3)

import tensorflow as tf

# Convert to TFLite with quantization
converter = tf.lite.TFLiteConverter.from_saved_model("saved_model_dir")
converter.optimizations = [tf.lite.Optimize.DEFAULT]  # INT8 quantization
tflite_model = converter.convert()

# Save the quantized model
with open("model_quant.tflite", "wb") as f:
    f.write(tflite_model)

Impact:

• 4x smaller model, 2-3x faster inference.
• Accuracy drop: ~1-2% (often acceptable for Re-ID).

2. Hardware Acceleration

Goal: Leverage mobile GPUs/TPUs via platform-specific backends.

Impact:

• 5-10x speedup vs. CPU on modern phones.
• Requires testing per device (some GPUs throttle under load).

3. Input Resolution Reduction

Goal: Reduce compute by downsizing Re-ID input crops.

# In your DeepSORT embedder class:
def preprocess(self, crop):
    import cv2
    # Resize from e.g., 128x64 to 64x32
    resized = cv2.resize(crop, (64, 32), interpolation=cv2.INTER_AREA)
    return resized  # Smaller input → faster inference

Impact: