Hello everyone and thank you in advance for your help. I have a problem configuring the visionAI senseCAP A1101 camera. After training, I have obtained the file “best_model.tflite” but according to the seeedstudio tutorial in the section “Can I train an AI model on my PC?” in its point “Step 9. Convert TensorFlow Lite to a UF2 file
UF2 is a file format, developed by Microsoft. Seeed uses this format to convert .tflite to .uf2, allowing tflite files to be stored on the AIoT devices launched by Seeed.” and I cannot get that conversion. Is there a tool to convert tflite to uf2? Is this type of extension necessary to be able to insert it into the camera’s storage unit?
Hi there,
So curious if you searched the term “Uf2” ifso there are several results, One I created for the Xiao 52840 but you can convert any hex or Tflow lite or bin file to a uf2 format, It’s a bootloader file format basically. What is the MCU target? I don’t see it ?
Read up on those threads and ask whatever you need. BTW there are many converters out there as well. WEB pages even.
HTH
GL 
PJ 
Thank you very much for your interest PJ Glasso, I am not an expert in the matter but I have to configure a seeedstudio camera (SenseCap A1101 vision AI Sensor) and well, I have not had any problem with the two models converted to uf2 that they provide on github; but I want to use my already trained model and following the Seeedstudio wiki for this camera I get to point 9, which says to convert the Tflite to uf2. The script called “uf2conv.py” I am not able to find the necessary one. I found on github: “yolov5-swift/uf2conv.py at master · Seeed-Studio/yolov5-swift · GitHub” the script, which mentions (-f VISIONAI), I tried it and a conversion was achieved; but when loading it into the camera the process stopped, saying that no device was found.
In short, I need a script or program that can convert the trained model.
Thanks
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Hi there,
I tried converting my model and deploying it to A1101 using the ‘uf2conv.py’ file you mentioned, and it looks like it’s working correctly and has the recognition box!
I did these steps:
- update the latest tinyuf firmware " tinyuf2-sensecap_vision_ai_2.0.1.bin", see update-bootloader.
- erase the firmware of A1101 by using “erase_model.uf2”, see Perform inference with SenseCAP A1101 on SenseCAP Mate.
- use the “uf2conv,py” you mentioned to convert my model
- flash it to A1101
- test the model via demo link.
Although it looks strange to work with models, you can find a model called ‘user defined 1’ on the demo page.
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