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Revamp RF-DETR Export Docs (#19341)

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* Revamp RFDETR docs

* Clarify model size

* Specify model size
This commit is contained in:
Nicolas Mowen
2025-07-31 08:11:46 -06:00
committed by GitHub
parent d18f2282c8
commit c3410cd13e
2 changed files with 24 additions and 25 deletions
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docs/docs/frigate/hardware.md
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@@ -166,12 +166,12 @@ There are improved capabilities in newer GPU architectures that TensorRT can ben
Inference speeds will vary greatly depending on the GPU and the model used.
`tiny` variants are faster than the equivalent non-tiny model, some known examples are below:
| Name | YOLOv9 Inference Time | YOLO-NAS Inference Time | RF-DETR Inference Time |
| --------------- | --------------------- | ------------------------- | ------------------------- |
| RTX 3050 | 320: 15 ms | 320: ~ 10 ms 640: ~ 16 ms | 336: ~ 16 ms 560: ~ 40 ms |
| RTX 3070 | 320: 11 ms | 320: ~ 8 ms 640: ~ 14 ms | 336: ~ 14 ms 560: ~ 36 ms |
| RTX A4000 | | 320: ~ 15 ms | |
| Tesla P40 | | 320: ~ 105 ms | |
| Name | YOLOv9 Inference Time | YOLO-NAS Inference Time | RF-DETR Inference Time |
| --------------- | --------------------- | ------------------------- | ---------------------- |
| RTX 3050 | t-320: 15 ms | 320: ~ 10 ms 640: ~ 16 ms | Nano-320: ~ 12 ms |
| RTX 3070 | t-320: 11 ms | 320: ~ 8 ms 640: ~ 14 ms | Nano-320: ~ 9 ms |
| RTX A4000 | | 320: ~ 15 ms | |
| Tesla P40 | | 320: ~ 105 ms | |
### ROCm - AMD GPU