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Evaluate original squeezeDetPlus model on KITTI Benchmark #23
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Thanks for your question. I'll look into it. @cksl |
@BichenWuUCB I found one possible bug in the demo.py , I modify my demo.py above, which seven lines of code are inserted( commented 【new codes】). The new codes are in the blow.
The result turns better, however, there are still gaps with your paper (Table 2)
I think the change reason is that: some input image is 1224x370, which is resized to 1242x375. So the results should be restored to original scale. There must be other problems , hope you can look into that carefully . Thank you, Bichen!
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both in training and demo the resize function distorts the image, which cannot be good for learning... probably would be better to rescale keeping the aspect ratio and add padding? this is already what happens when the image is smaller than the given size.. |
In my opinion(from the experience), KITTI dataset has sequential images(This means that sequential images have large correlation) and for test in paper they use randomly sampled training and validation data. This is why the result is different |
@ByeonghakYim Yes, KITTI train dataset has sequential images. |
@dojoscan ,Yes, One algorithm should run only once on the evaluation server. |
@cksl Did you figured out this problem? I think the keep_score is too low, leading to too many false positive. @BichenWuUCB I did not find the report on kitti test datasets in your paper. Did you run SqueezeDet on KITTI test datasets? What is the score you got from them? |
@BichenWuUCB - |
@BichenWuUCB seems result on validation and official Test benchmark differs greatly, I randomly split the training set and run the model(squeezedet), after some tune I got close to paper result, which is |
@MrWanter Can you please help me with what hyperparamter changes (tuning) you made to get the same result as the paper? |
From my experience the low loss on training set does not guarantee a correspondent low mAP in test set. Maybe you got higher mAP on test set at a higher training loss (some previous checkpoint I mean). |
Your work impress me with high speed and better performance, before training on my own data, I just run your squeezeDetPlus model (model.ckpt-95000) on the KITTI test set (7518 images). However , the result is not good.
I see the mAP of pedestrian in the Paper (Tablet 2) is:
The result I run is:
To be honest , I trust your result, there must be something wrong in the code. I just modify the demo.py, Here is my demo.py . All other codes are the same with yours.
I have checked my demo.py over and over again. I doubt there are bugs in the original demo.py , Could you figure it out? Hope you can help with that!
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