Renesas/MobileNetV4-Medium-r256-ONNX
image-classification model by Renesas
From the publisher
Model card & documentation
Source previewRead the publisher’s intended use, setup instructions, evaluations and limitations. The original model card is the source of truth.
This repository hosts MobileNetV4-Medium (r256), targeting the Renesas R-Car X5H platform for image-classification inference on the NPX6 NPU. The FP32 ONNX model is auto-cast to INT8 by the Renesas MWMX toolchain at compile time — no separate quantization step is required. Measured on Renesas R-Car X5H…
Read the full model card ↗ · Preview checked 2026-09-23T08:37:27.753Z
Inside the original model card — Document outline
- MobileNetV4-Medium (r256) (ONNX) – Renesas X5H
- Introduction
- Deployment Flow
- Provided Artifacts
- Performance
- Accuracy
- Runtime Details
- MWMX Runtime
- Prerequisites
- Download
- Benchmark Methodology
Headings are captured from the source. Links open the publisher’s document, not a locally hosted copy.
Documentation belongs to its respective authors. Reported project/model license: apache-2.0. A listing is not a grant of reuse or training rights. Confirm the document’s own terms at the source.
Model overview
Renesas/MobileNetV4-Medium-r256-ONNX is a image-classification model repository published by Renesas on Hugging Face. A library was not reported.
This page summarizes Hub metadata. For intended use, training data, evaluation results and limitations, consult the original model card.
Model facts
- Task
- image-classification
- Library
- Not reported
- Recent downloads (30 days)
- 0
- Cumulative likes
- 0
- Hugging Face trending score
- 0
- Reported safetensors parameters
- Not reported
- Architecture
- Not reported
- License
- apache-2.0
- Access
- Not gated by Hugging Face
- Created
- 2026-09-23T06:13:00.000Z
- Last modified
- 2026-09-23T06:13:04.000Z
Compatibility and lineage
Language coverage was not reported.
Reported base models: timm/mobilenetv4_conv_medium.e500_r256_in1k
Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.
Use and evaluate
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Source and freshness
Source: Hugging Face Hub. Metadata observed 2026-09-23T06:37:25.545Z. Daily imports are snapshots, not real-time monitoring.
Popularity and source listings do not establish security, suitability, licensing rights or benchmark performance.
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