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poor classification results with edgetpu model #50
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one more hint
comment out. This will always delete the result. And you can't see the result. |
attached is an example of the edgetpu model All images in the specified directory are checked. |
I now also have a test with the model: I previously complied the model with the edgetpu complier in version 13. The result is definitely better, all with the edgetup model how can that be? The hit rate is best with model_unquant.tflite result in the attached file |
can you please replace After running please check Thanks |
i don't have pandas installed. Which version should I install in the env? |
please install |
Since I use Windows 10, I unfortunately cannot use the current runtime, but have to use the version That's why I get the error when loading the model_quantized_edgetpu.tflite model here the result classification_cpu_vs_edgetpu.py |
If we look at the results column both quantized tflite output and edgetpu tflite outputs are similar. can you please tell what is the problem here? |
When creating the result file, with classification_cpu_vs_edgetpu.py the paths were wrong. I have to adjust them again and from now the models that are to be checked are actually loaded in the path:
As described above, I had tested the three models and the edetpu model did not give the correct results |
I found the mistake cmd = f'''python3 classify_image.py i used a virtual environment. That would have to be added to your script.
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for example if your env is |
The script is now running. with the supplied model, the error occurs - model_quantized_edgetpu.tflite this is how it looks now:
Error:
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here first the results with my models. Unfortunately, the values are no longer correctly transferred to the columns. I'll try the model now: |
result with your model: that can't work because I can't use the new version 14 under Win10. As already described above. Messages
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I know, v14 does not work on your machine. we are only interested in comparison between |
so here the results of the models: example_edgetpu_v13 quantized.tflite In the results file you can see that with the |
if you look at example_edgetpu_v13.tflite using https://netron.app/ there are only 2 output tensors [1 2]. That's why you are getting only 2 results. |
here the original edgetpu file |
I am not sure how you get the edgetpu file with two output tensors.. It should not happen if input |
could you complieren the model in version 13? i then test how the classification works |
Please check attachment and just keep |
the result of your model: model_quantized_v13_edgetpu.tflite is very good. See file. how did you compile the model into version 13? |
! edgetpu_compiler -s -m 13 model_quantized_v13.tflite |
Hi, do you have questions here, if not please close this ticket. |
Description
I create my models with Teachable Machine.
For this test, I downloaded all three model variants.
So far I have been using the unquant.tflite model for classification, which achieves excellent results.
Test with the script:
images_predict_unquant_Teach_Lite.py
- All images in a directory are checkedModel: model_unquant.tflite
To speed things up, I got the
coral usb accelerator bought to classify with an edge tpu model
Unfortunately I get bad results with the edgetpu model and only two classes are returned as a result (none and cat), regardless of which class I test
Test with script:
classify_image_edgetpu.py
- only individual images are checkedModel:
example_edgetpu_v13.tflite
Reference issues:
google-coral/libedgetpu#29 (comment)
#49
#48
The zipfile contains the two scripts, all models and a test dataset per class of 292
edgetpu_issue_Windows10 (3).zip
images
I have not yet used the model_quantized.tflite model for the tests. However, it is included as a zip file
Install edgetpu env
Install tflite env
Click to expand!
Issue Type
Performance
Operating System
Windows 10
Coral Device
USB Accelerator
Other Devices
No response
Programming Language
Python 3.7
Relevant Log Output
No response
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