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autolane/rfdetr-alpr

sourceHugging Faceapache-2.0updated 9mo agoView on Hugging Face

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Model Card

RF-DETR License Plate Detector

A fine-tuned RF-DETR Medium model for license plate detection, optimized for edge deployment.

Model Details

—Base Model: RF-DETR Medium
—Task: License plate detection (single class)
—Input Resolution: 576x576
—Training Framework: PyTorch

Available Formats

File	Format	Size	Use Case
`rfdetr_alpr.onnx`	ONNX	117MB	Cross-platform inference
`rfdetr_alpr_optimized.onnx`	ONNX (optimized)	115MB	Optimized for TensorRT
`rfdetr_alpr_int8.onnx`	ONNX INT8	34MB	Edge inference (ARM/NPU)
`license_plate_detector_fp16.engine`	TensorRT FP16	64MB	GPU inference (balanced)
`license_plate_detector_int8.engine`	TensorRT INT8	82MB	GPU inference (fastest)
`calibration.cache`	Calibration data	103KB	INT8 calibration cache

Deployment Paths

—NVIDIA GPU: Use TensorRT engines (.engine) for fastest inference
—Edge/ARM (i.MX8M Plus, i.MX93): Use INT8 ONNX with ONNX Runtime or convert to TFLite

TensorRT Engine Details

—TensorRT Version: 10.14.1
—Target GPU: NVIDIA GB10 (Compute Capability 12.1)
—Input Shape: 1x3x576x576 (fixed batch size)
—Precision: FP16 / INT8

Usage

ONNX Inference

python

import onnxruntime as ort
import numpy as np

session = ort.InferenceSession("rfdetr_alpr_optimized.onnx")
# Input: (1, 3, 576, 576) normalized to [0, 1]
outputs = session.run(None, {"images": input_tensor})
boxes, scores = outputs[0], outputs[1]

TensorRT Inference

python

import tensorrt as trt
import pycuda.driver as cuda

# Load engine
with open("license_plate_detector_int8.engine", "rb") as f:
    engine = trt.Runtime(trt.Logger()).deserialize_cuda_engine(f.read())

Edge Inference (INT8 ONNX)

For ARM/edge devices without NVIDIA GPU:

python

import onnxruntime as ort

# Use INT8 quantized model for edge deployment
session = ort.InferenceSession(
    "rfdetr_alpr_int8.onnx",
    providers=['CPUExecutionProvider']  # or platform-specific NPU provider
)

# Input: (1, 3, 576, 576) with ImageNet normalization
outputs = session.run(None, {"images": input_tensor})
boxes, scores = outputs[0], outputs[1]

License

Apache 2.0 (same as RF-DETR)