Comments (4)
I confirm it is easy to achieve the 92.9 OA using their code (results reported using the best model).
Exp show that you can achieve even better results (like 93.1) using the same number epochs as KPConv (400 epochs) instead of 250 epochs.
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I got 92.4%
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Train 26, loss: 3.676545, train acc: 0.075407, train avg acc: 0.030953
Test 26, loss: 3.640235, test acc: 0.042139, test avg acc: 0.026000
Train 27, loss: 3.671733, train acc: 0.072358, train avg acc: 0.030758
Test 27, loss: 3.637559, test acc: 0.044976, test avg acc: 0.027750
Train 28, loss: 3.675745, train acc: 0.073272, train avg acc: 0.031713
Test 28, loss: 3.636188, test acc: 0.068071, test avg acc: 0.042000
Train 29, loss: 3.691305, train acc: 0.071545, train avg acc: 0.030454
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Why is the accuracy rate so low? #81
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Related Issues (20)
- There was no model.2048.t7 file in pretrained folder.
- Can I get the final paper?
- pytorch part segmentation HOT 1
- Can we run this code in windows?
- Visualization issues
- getting 'Indexerror' in train_multi_gpu.py HOT 1
- Understanding equation #8 in research paper (Section 3.1)
- 请问你的分类模型到底是哪样的,有的有连接,有的没有!!!
- LiDAR Point Cloud Classification results not good with real data HOT 1
- Why does the pytorch version not have transform? HOT 2
- How to add more DGCNN layers in your implementation?
- Aborted (core dumped) if I process to many points at once HOT 2
- Is the input size of the first layer 6 because the input parameters are the point itself and its normals?? HOT 1
- Why is the accuracy rate so low? HOT 1
- Visualization
- ValueError: need at least one array to concatenate HOT 2
- What is the KNN distance formula implemented by pytorch HOT 1
- The network architecture in code is different from the paper?
- 数据可视化严重问题
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