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Update docs for rknn downloads

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This commit is contained in:
Nicolas Mowen 2025-04-23 09:19:32 -06:00
parent 2e16fb8a94
commit c9cd552dd2
1 changed files with 19 additions and 14 deletions
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docs/docs/configuration/object_detectors.md
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@ -844,14 +844,14 @@ detectors: # required
The inference time was determined on a rk3588 with 3 NPU cores.
| Model | Size in mb | Inference time in ms |
| ------------------- | ---------- | -------------------- |
| deci-fp16-yolonas_s | 24 | 25 |
| deci-fp16-yolonas_m | 62 | 35 |
| deci-fp16-yolonas_l | 81 | 45 |
| yolov9_tiny | 8 | 35 |
| yolox_nano | 3 | 16 |
| yolox_tiny | 6 | 20 |
| Model | Size in mb | Inference time in ms |
| --------------------- | ---------- | -------------------- |
| deci-fp16-yolonas_s | 24 | 25 |
| deci-fp16-yolonas_m | 62 | 35 |
| deci-fp16-yolonas_l | 81 | 45 |
| frigate-fp16-yolov9-t | 6 | 35 |
| rock-i8-yolox_nano | 3 | 14 |
| rock-i8_yolox_tiny | 6 | 18 |
- All models are automatically downloaded and stored in the folder `config/model_cache/rknn_cache`. After upgrading Frigate, you should remove older models to free up space.
- You can also provide your own `.rknn` model. You should not save your own models in the `rknn_cache` folder, store them directly in the `model_cache` folder or another subfolder. To convert a model to `.rknn` format see the `rknn-toolkit2` (requires a x86 machine). Note, that there is only post-processing for the supported models.
@ -887,10 +887,13 @@ The pre-trained YOLO-NAS weights from DeciAI are subject to their license and ca
model: # required
# name of model (will be automatically downloaded) or path to your own .rknn model file
# possible values are:
# - yolov9-t
# - yolov9-s
# - frigate-fp16-yolov9-t
# - frigate-fp16-yolov9-s
# - frigate-fp16-yolov9-m
# - frigate-fp16-yolov9-c
# - frigate-fp16-yolov9-e
# your yolo_model.rknn
path: /config/model_cache/rknn_cache/yolov9-t.rknn
path: frigate-fp16-yolov9-t
model_type: yolo-generic
width: 320
height: 320
@ -905,10 +908,12 @@ model: # required
model: # required
# name of model (will be automatically downloaded) or path to your own .rknn model file
# possible values are:
# - yolox_nano
# - yolox_tiny
# - rock-i8-yolox_nano
# - rock-i8-yolox_tiny
# - rock-fp16-yolox_nano
# - rock-fp16-yolox_tiny
# your yolox_model.rknn
path: yolox_tiny
path: rock-i8-yolox_nano
model_type: yolox
width: 416
height: 416