I am using python program in jetson-inference to process the classification of 3 letters, i.e. A, B & C. DataSet are from “https://s3.amazonaws.com/nist-srd/SD19/by_class.zip”. I followed the ratio, 7:2:1, to place the images of A, B & C into train, val & test floders respectively.
Model was trained, exported and executed by the below commands.
- python3 train.py --model-dir=models/abc --batch-size=4 --workers=1 --epochs=1 data/abc
- python3 onnx_export.py --model-dir=models/abc
- imagenet --model=models/abc/resnet18.onnx --labels=data/abc/labels.txt --input_blob=input_0 --output_blob=output_0 /dev/video0
The Top-5 and Top-1 % are 100% and 96% respectively after the testing.
During running the model, it seems that “A” become the default class and it can find class “B”. However, class “C” cannot be classified.
May I ask how I can debug? or find out the reasons of inaccuracy?
Thank you so much!