Different prediction results with ImageNet Inference than Digits on Custom GoogleNet Model about jetson-inference HOT 7 CLOSED

I went through everything I could think of to try to fix this, but nothing has worked so far.

Here are the things I've tried

1.) I trained my data using AlexNet with pretrained weights from the original alexnet bvlc_alexnet.caffemodel. This made the training extremely fast, and a hair under 100% for the accuracy that Digits reported. All the test images reported correct under digits with >89% confidence. But, one of the test images still failed on the TX1 imagenet-console. On further testing I realized that it was having issues with the playingcards that were slanted about 45 degrees. None of the 0 degree or 90 degree cards failed.

2.) from the jetson-inference source code it looked like the images were being resized to 227x227 so I tried resizing them to 227x227 to see what Digits reported with those sizes. Digits still reported them correctly.

3.) I tried changing the mean value subtraction to all 0.0f's, but that didn't seem to help at all. I didn't expect it to because the data is trained using mean subtraction.

4.) I tried disabling FP16, and then deleting the tensor caffe file before retrying. But, that didn't help. I didn't expect it to as everything I've read suggest that FP16 has no noticeable degradation in accuracy.

The only thing I can think of doing next is changing the mean value subtraction, but I don't know how to get those values yet. I'm a bit skeptical of that though, and it seems like a resizing issue.

Any suggestions on what to try?

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The images I tested were 256x256. I purposely squashed them to this size to make sure they matched the size that Digits trained on.

I went ahead and uploaded the Model being used along with the Test Images.

https://github.com/S4WRXTTCS/PlayingCards

The Test Image it has problems on is Squashed4.jpg

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After investigating it further it seems it was related to the mean values.

I realized that use_archive.py was also giving me the same incorrect answer for that one example. Where it was completely off the results from digits.

One limitation of use_archive.py is it subtracts a mean pixel rather than the whole mean file. So that meant the mean subtraction was likely the culprit.

So I retrained the images using Mean Pixel subtraction instead of image in an attempt to at least get use_archive.py to report the same result as Digits.

The new Model worked for Digits+use_archive.py, and also gave me similar results with the jetson inference code.

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Thanks for your help.

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@S4WRXTTCS hi ,i also train a GoogleNet image classification network on a custom dataset using the default Googlenet network within Digits. but i don't know how to move it to jetson-inference and use the model that i got it by training. could you tell me the detail. thank you very much. you can send email to me [email protected]

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