Jinlai Zhang

But the data you provided in Google Cloud seems preprocessed.

The same issue, did you solved it?

Here are more output ``` array(3089), array(3089), array(3057), array(3089), array(3057), array(3121 ), array(2865), array(2865), array(2480), array(13), array(1296), array(591), array(271), array(15), arr ay(14), array(14), array(13), array(13), array(14), array(14), array(17), array(17), array(19),...

update ``` for i in range(len(seg_logits_list)): seg_logits = seg_logits_list[i] seg_pred = seg_logits # print(seg_logits.size()) # seg_pred = seg_logits.argmax(dim=0) # print('the seg 2222pred is',seg_pred) batch_data_samples[i].set_data({ 'pts_seg_logits': PointData(**{'pts_seg_logits': seg_logits}), 'pred_pts_seg': PointData(**{'pts_semantic_mask': seg_pred})...

作者您好，表VII的测试结果如何在kaggle上看到呢？ finger | 2958 | 22638 -- | -- | -- crack | 1260 | 2797 black_core | 1028 | 3877 thick_line | 981 | 1585 也就是说如何找到哪些图片属于这些类别？

这样根本没办法分析算法在每个类别的表现呀

Untargeted attack for Pointnet and DGCNN, the classification after attack is 73.58 and 71.23%, respectively.

Thanks, it works! However, another bug occurs. Since the `gt/` does not include the `.png` file, I changed seg_dataset.py line 47 to `label_file = osp.join(root_dir, "gt/%s" % name)`, and the...

share the code ``` from PIL import Image import os def tif_to_png(file_path, num_classes): # Open the TIF file img = Image.open(file_path) # Convert the image to grayscale img = img.convert("L")...