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DETR-R50-ONNX

Object Detection
ONNX
computer-vision
renesas
x5h
detr
transformer
detection
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DETR-R50-ONNX / README.md
Artem Plastinkin
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metadata 
license: apache-2.0
base_model:
  - facebook/detr-resnet-50
pipeline_tag: object-detection
tags:
  - object-detection
  - computer-vision
  - renesas
  - x5h
  - onnx
  - detr
  - transformer
  - detection

DETR-R50 (ONNX) – Renesas X5H

Introduction

This repository hosts DETR (DEtection TRansformer), targeting the Renesas R-Car X5H platform for object detection inference on the NPX6 NPU.

  • Model Architecture: DETR — transformer-based, end-to-end object detector using set prediction (bipartite matching), CNN backbone + transformer encoder/decoder
  • Source Model: facebook/detr-resnet-50 — checkpoint sim_mod_detr
  • Task: Object Detection
  • Dataset: COCO (inferred from checkpoint name)
  • Input Resolution: TBD

Deployment Flow

The FP32 ONNX model is auto-cast to INT8 by the Renesas MWMX toolchain at compile time — no separate quantization step is required.

sim_mod_detr_..._optimized.onnx (FP32)
        │
        └─▶  MWMX Runtime  ──▶  INT8 auto-cast  ──▶  NPX6 NPU

Provided Artifacts

Artifact Status Notes
FP32 (ONNX) ✅ fp32/sim_mod_detr.onnx — FP32 ONNX export

Performance

Measured on Renesas R-Car X5H via the MWMX runtime (APM50 ship-performance CI pipeline).

Benchmark configuration: Single NPU · Batch size: 1 · Input resolution: TBD

Runtime Precision Device Latency (ms) Type
MWMX Runtime INT8 (auto) X5H · 1× NPU · 1 Core · 850 MHz 147.768855 Measured
MWMX Runtime INT8 (auto) X5H · 1× NPU · 12 Cores · 850 MHz 92.763967 Measured

Model Input

Input Tensor

  • Shape: TBD — not available from source data (expected (N, 3, H, W), RGB)
  • Format: TBD
  • Data Type: TBD
  • Pixel Range: TBD

Preprocessing

TBD — not available from source data.

Model Outputs

TBD — not available from source data. DETR produces a fixed-size set of predictions (typically 100 object queries), each with a class-probability distribution (including a "no object" class) and a normalized bounding box, directly via set prediction — no anchor decoding or NMS is required by design.

Postprocessing

  1. Per-query class-probability argmax (excluding "no object")
  2. Confidence threshold filtering
  3. Box de-normalization to image coordinates

Accuracy

TBD — not yet measured/published for this repo.

Runtime Details

MWMX Runtime

  • Engine: Renesas MWMX (Middleware MX) native inference runtime
  • Input format: FP32 ONNX (compiled by the MWMX toolchain)
  • NPU execution precision: INT8 (auto-cast by MWMX toolchain)
  • Execution target: NPX6-48K NPU on R-Car X5H

Prerequisites

To run inference on Renesas R-Car X5H, you need:

  1. Renesas R-Car X5H board with NPX6 NPU
  2. Renesas MWMX Runtime
  3. Hugging Face CLI to download the model

Download 

hf download Renesas/DETR-R50-ONNX --repo-type=model --include "fp32/*"

Benchmark Methodology

  • HIL runs: Hardware-in-the-loop — measured on physical R-Car X5H silicon via the MWMX runtime (metawaremx_runtime CI pipeline, "APM50" ship-performance target)
  • Precision: FP32 ONNX input; INT8 execution (auto-cast by MWMX)
  • Slices: results reported for both 1 AI core and 12 AI cores per NPU instance