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I upgraded to v2.2.1, but the recognition efficiency of OCR is very low #7709
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opened 2026-02-05 13:15:17 +03:00 by OVERLORD
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Reference: immich-app/immich#7709
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Originally created by @nodis on GitHub (Nov 2, 2025).
I have searched the existing issues, both open and closed, to make sure this is not a duplicate report.
The bug
I upgraded to v2.2.1, but the recognition efficiency of OCR is very low

The specific situation is as follows:
My NAS CPU is i5 10400
The model I am using is PP-OCRv5_Server
When running text recognition tasks, the CPU usage is over 90%, which means that the OCR model is loading normally, but the recognition efficiency is very low, only able to recognize dozens of images per hour
The OS that Immich Server is running on
Debian fnos
Version of Immich Server
v2.2.1
Version of Immich Mobile App
v2.2.1
Platform with the issue
Device make and model
No response
Your docker-compose.yml content
Your .env content
Reproduction steps
...
Relevant log output
Additional information
No response
@gianlucaT1989 commented on GitHub (Nov 2, 2025):
The _server ocr model depends heavily on CPU performance — it's very demanding in terms of CPU and RAM usage.
In my case i have Minisforum MS-790 Pro with an AMD Ryzen 9 7940HS and integrated Radeon 780M GPU.
Additionally, I’ve noticed that the OCR model appears to be reloaded multiple times during a global batch process, as if it’s not being kept in memory across requests. This significantly impacts performance (SSD>RAM// SSD>RAM Load reload load reload)
I also tried enabling GPU acceleration, but the current immich-machine-learning container is hardcoded to use only CPUExecutionProvider?, and doesn’t support alternatives like OpenCLExecutionProvider or DmlExecutionProvider, which would allow GPU usage on AMD or Intel iGPUs. Even though the code lists multiple SUPPORTED_PROVIDERS, only CPU is actually used unless the image is rebuilt.
Suggestions for improvement:
Note: OpenCLExecutionProvider, DmlExecutionProvider, and CUDAExecutionProvider only work if the immich-machine-learning component is running directly on a machine that has a compatible GPU. In virtualized environments (e.g., Proxmox), GPU passthrough is required to expose the GPU to the virtual machine — but support for this setup is limited and can be complex to configure.
In such cases, a more practical solution might be to run the immich-machine-learning component directly on the host system (with GPU access), while keeping the rest of Immich inside the virtual machine. You would then need to update the connection URL in the virtual machine to point to the host’s IP or hostname.
I'm currently evaluating this option, but I will only proceed if — as in my case with AMD — the OpenCLExecutionProvider is actually available and functional.