| --- |
| license: apache-2.0 |
| tags: |
| - onnx |
| - object-detection |
| - rf-detr |
| - pet-detection |
| library_name: onnx |
| pipeline_tag: object-detection |
| --- |
| |
| # RF-DETR Small (ONNX) — pet detection for Gallery |
|
|
| ONNX export of [RF-DETR](https://github.com/roboflow/rf-detr) Small by Roboflow, used by |
| [Gallery](https://github.com/open-noodle/gallery) for pet detection. |
|
|
| Unmodified COCO-pretrained weights. No fine-tuning. |
|
|
| ## Files |
|
|
| | Path | Description | |
| | ---------------------- | -------------------------------- | |
| | `detection/model.onnx` | RF-DETR Small, opset 17, batch 1 | |
|
|
| ## Inference contract |
|
|
| Getting any of this wrong degrades output silently rather than erroring. |
|
|
| **Input** — `input`, shape `[1, 3, 512, 512]`, float32: |
|
|
| 1. Decode to **RGB** (not BGR) |
| 2. Resize to 512×512 — plain square resize, **not** letterboxed |
| 3. Scale to `[0, 1]` |
| 4. Normalise with ImageNet statistics: mean `[0.485, 0.456, 0.406]`, std `[0.229, 0.224, 0.225]` |
| 5. Transpose HWC → CHW, add batch dimension |
|
|
| **Output** — two tensors: |
|
|
| | Name | Shape | Meaning | |
| | -------- | -------------- | ---------------------------------------------- | |
| | `dets` | `[1, 300, 4]` | Boxes as cx, cy, w, h — normalised to `[0, 1]` | |
| | `labels` | `[1, 300, 91]` | Class logits, **pre-sigmoid** | |
|
|
| Apply sigmoid to `labels`, then threshold. Classes use the **91-class COCO id space** |
| (90 categories plus background), not the contiguous 80-class space YOLO uses — so |
| `bird=16, cat=17, dog=18, horse=19, sheep=20, cow=21`. |
|
|
| The 300 queries are already deduplicated. **No NMS step is required.** |
|
|
| ## License |
|
|
| Apache-2.0, inherited from RF-DETR. See the |
| [upstream repository](https://github.com/roboflow/rf-detr) for full terms. |
|
|
