Hello @fanronghua0123456,
Thank you, I think your configuration is correct.
In your first message, it seems that it is the quantization matrix that has an issue.
Can you tell me if your training and validation matrixes also have issues or are they correct?
Could you try to use your training path for the quantization?
dataset:
name: handers
class_names: [hander1, hander2, hander3, hander4, hander5, hander6, hander7] # Or, classes_file_path, one of it is mandatory for deployment and prediction.
training_path: ./datasets/hander_photos # Mandatory
validation_path: # Optional
validation_split: 0.2 # Optional, default value is 0.2
test_path: # Optional
quantization_path: ./datasets/hander_photos # Mandatory
quantization_split: # Optional
check_image_files: False # Optional, set it to True if you want to check that all the image files can be read successfully
seed: 127 # Optional, there is a default seed
When you don't provide a path for validation, it uses random data. It is always recommended to use real data to quantize. You can also use quantization_split to use less data if you want.
For test you need to use different data, but for quantization, you can use the training data.
Have a good day,
Julian
In order to give better visibility on the answered topics, please click on 'Accept as Solution' on the reply which solved your issue or answered your question.
