You can deploy this model and start earning money today!

AnnotateIt/
rtdetr-r50vd-coco-onnx

Name: rtdetr-r50vd-coco-onnx
Availability: InStock

Views

No views yet

RT-DETR R50vd COCO — verified ONNX mirror for AnnotateIt

This repository is an AnnotateIt-maintained, byte-identical mirror of the FP32 ONNX graph from onnx-community/rtdetr_r50vd. It is based on the official Apache-2.0 PekingU/rtdetr_r50vd checkpoint.

This is not an official PekingU or RT-DETR ONNX release. AnnotateIt did not alter or quantize the graph; the mirror adds an explicit license, immutable provenance, checksums, and independent numerical/runtime validation.

Artifact

File	Value
ONNX graph	`model.onnx`
Precision	FP32
Opset	16
Declared input	`pixel_values`: float32 `[batch, channels, height, width]`
Validated input	float32 `[1, 3, 640, 640]`
Outputs	`logits`: float32 `[batch, 300, 80]`; `pred_boxes`: float32 `[batch, 300, 4]`
Size	175,421,119 bytes
SHA-256	`b1a6aa26c56b7838b02c2b5fa66d312deee1295095ea5e85f5679a6f41eee855`

Preprocessing and outputs

Convert the image to RGB.
Resize to 640×640 with bilinear interpolation (stretch, without letterboxing).
Rescale channels to [0, 1] by dividing by 255; do not apply mean/std normalization.
Convert HWC to NCHW and add a batch dimension.
Apply sigmoid to logits and take the best COCO class score per query.
Interpret pred_boxes as normalized cx, cy, width, height and map them to the original image size.

The model predicts the 80 COCO classes and does not require NMS in the reference postprocessor.

Validation

ONNX checker and ONNX Runtime CPU inference passed.
The unchanged graph was compared with official PyTorch checkpoint revision df939e661d8c52e80608d1ec566561aabd25a4e7 on a real COCO image.
PyTorch and ONNX produced the same five detections at threshold 0.3, with matching classes, scores, and boxes.
The graph ran successfully through AnnotateIt's onnxruntime-web 1.24.3 WASM runtime.

See validation-report.json for the measured differences and PROVENANCE.md for the pinned sources. Timings are smoke-test observations, not benchmarks.

License

RT-DETR and the official checkpoint are licensed under Apache License 2.0. The full text is included in LICENSE. Retain the license and attribution when redistributing the model.

Official project: lyuwenyu/RT-DETR

AnnotateIt/rtdetr-r50vd-coco-onnx