autolane/yolox-s-alpr

YOLOX-S for US License Plate Detection

YOLOX-S fine-tuned for single-class license plate detection on ~20k US license plate images.

Results

Metric	Value
AP@50	98.9%
AP@50:95	90.1%
AP@75	97.6%
AR@50:95	92.5%
AP small	75.7%
AP medium	87.9%
AP large	91.9%
Inference	~4.2ms/image (FP16, fused)

Model Details

Parameter	Value
Architecture	YOLOX-S (depth=0.33, width=0.50)
Classes	1 (license_plate)
Input size	640x640
Pretrained from	COCO (yolox_s.pth)
Training epochs	80
Precision	FP16

Training

See Autolane/alpr-finetuning for the full training pipeline.

W&B project: goautolane/alpr-yolox

Hyperparameters

• Optimizer: SGD (momentum=0.9)
• Learning rate: 0.01/64 per image
• Weight decay: 5e-4
• Warmup: 5 epochs
• No-augmentation: last 10 epochs
• Augmentation: Mosaic, Mixup, HSV jitter, horizontal flip, rotation (+/-10deg)

Quantized Models (QAT)

Quantization-Aware Training (QAT) fine-tunes the model with simulated int8 noise so weights become robust to quantization. Post-training quantization (PTQ) loses -7.8 AP50:95; QAT recovers most of this gap.

Metric	FP32	int8 PTQ	Delta
AP@50:95	0.901	0.823	-7.8
AP@50	0.989	0.989	0.0
AP@75	0.976	0.966	-1.0

NPU Deployment (NXP i.MX93)

The Vela-compiled TFLite model targets the Arm Ethos-U65-256 NPU on NXP i.MX93:

• NPU utilization: 96.2% (12 TRANSPOSE ops fall back to CPU)
• Inference time: ~120ms per image (Ethos-U65-256 @ 1GHz)
• Model size: 7.7MB (Vela) vs 35MB (ONNX FP32)

ONNX Runtime Inference

import onnxruntime as ort
import numpy as np

session = ort.InferenceSession("yolox_plates_s.onnx")
# Input: [1, 3, 640, 640] float32, range [0, 255]
input_name = session.get_inputs()[0].name
output = session.run(None, {input_name: image_tensor})

Usage

import torch
from yolox.exp import get_exp

# Load experiment config
exp = get_exp("yolox_plates_s.py", None)
model = exp.get_model()

# Load weights
ckpt = torch.load("best_ckpt.pth", map_location="cpu")
model.load_state_dict(ckpt["model"])
model.eval()

Files

File	Size	Description
best_ckpt.pth	69MB	FP32 PyTorch checkpoint (best validation AP)
yolox_plates_s.py	—	Experiment config (architecture + training hyperparameters)
yolox_plates_s.onnx	35MB	FP32 ONNX export (for ONNX Runtime inference)
yolox_plates_s_int8.tflite	8.9MB	int8 TFLite via QAT (for TFLite Runtime / CPU)
yolox_plates_s_vela.tflite	7.7MB	Vela-compiled int8 TFLite (for Ethos-U65-256 NPU)

Dataset

Trained on ~20k US license plate images (80/20 train/val split, stratified by US State). See Autolane/us-license-plates.