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First of all, thanks for the code! I was looking for a PyTorch port of DeepLabv3+ that can use the official TensorFlow checkpoints. Your repo is the only one I could find! Great work!
There seems however to be some discrepancy compared to the TensorFlow version.
I tried the xception65_coco_voc_trainval checkpoint on the Pascal VOC test set with flipping and multi-scale ([0.5:0.25:1.75]) inference, and obtained mIoU = 85.93630 (by uploading to the evaluation server), which far inferior to the reported 87.80%.
In addition to the difference in BatchNorm's eps that you discovered previously, there should be some other details that affect the performance. Unfortunately I was unable to find them. I hope you or somebody can.
The text was updated successfully, but these errors were encountered:
Hi,
First of all, thanks for the code! I was looking for a PyTorch port of DeepLabv3+ that can use the official TensorFlow checkpoints. Your repo is the only one I could find! Great work!
There seems however to be some discrepancy compared to the TensorFlow version.
I tried the
xception65_coco_voc_trainval
checkpoint on the Pascal VOC test set with flipping and multi-scale ([0.5:0.25:1.75]
) inference, and obtained mIoU = 85.93630 (by uploading to the evaluation server), which far inferior to the reported 87.80%.In addition to the
difference in BatchNorm's eps
that you discovered previously, there should be some other details that affect the performance. Unfortunately I was unable to find them. I hope you or somebody can.The text was updated successfully, but these errors were encountered: