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Update detectors.md to detail MODEL_CACHE_DIR env var

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Resolves confusion in https://github.com/blakeblackshear/frigate/discussions/9025
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SlothCroissant 2023-12-19 16:00:41 -06:00 committed by GitHub
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docs/docs/configuration/detectors.md
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@ -239,7 +239,7 @@ yolov7x-320
The TensorRT detector can be selected by specifying `tensorrt` as the model type. The GPU will need to be passed through to the docker container using the same methods described in the [Hardware Acceleration](hardware_acceleration.md#nvidia-gpu) section. If you pass through multiple GPUs, you can select which GPU is used for a detector with the `device` configuration parameter. The `device` parameter is an integer value of the GPU index, as shown by `nvidia-smi` within the container.
The TensorRT detector uses `.trt` model files that are located in `/trt-models/` by default. These model file path and dimensions used will depend on which model you have generated.
The TensorRT detector uses `.trt` model files that are located in a versioned subdirectory of `${MODEL_CACHE_DIR}`. You can set `MODEL_CACHE_DIR` as an environment variable to change it from the default `/config/model_cache/tensorrt`. These model file path and dimensions used will depend on which model you have generated.
```yaml
detectors:
@ -248,6 +248,8 @@ detectors:
device: 0 #This is the default, select the first GPU
model:
# Be sure to set MODEL_CACHE_DIR environment variable to customize the model path from the default
# In this example, MODEL_CACHE_DIR = '/trt-models'
path: /trt-models/yolov7-tiny-416.trt
input_tensor: nchw
input_pixel_format: rgb