# DLCV final

## Experiment

Template:
Experiment: factor A v.s. factor B ( factor C...etc if existed)

### s1-s2-s3 each 200 epoch

| mIoU (train) | Head | Common | Tail | All | All (test) |
| ------------------------------------- | ------ | ------ | ------ | ------ | ---------- |
| train best - ce - none | 66.554 | 56.430 | 37.069 | 53.382 | 18.4486 |
| train best - focal - none | 67.953 | 64.342 | 56.097 | 62.813 | 19.5710 |
| train best - focal+ts - none (failed) | 23.733 | 5.601 | 2.348 | 10.511 | N/A |

| mIoU (val) | Head | Common | Tail | All | All (test) |
| --------------------------- | ------ | ------ | ----- | ------ | ---------- |
| val best - ce - none | 38.413 | 7.759 | 3.071 | 16.328 | 17.6254 |
| val best - ce - focal+ts | 37.370 | 7.327 | 3.048 | 15.829 | 17.7206 |
| val best - focal - none | 39.909 | 10.169 | 4.227 | 18.022 | 19.3887 |
| val best - focal - focal+ts | 39.280 | 9.621 | 3.904 | 17.521 | 18.7903 |
| none - focal - none | 37.376 | 8.319 | 3.881 | 16.443 | 18.2070 |

> ts = tail sample
> ce = cross entropy

model spec

| Items | Settings |
| ---------- | -------- |
| voxel size | 0.04 |
| batch size | 2 |
| optimizer | sgd(lr=0.05, momentum=0.9, weight_decay=1e-4) |
| scheduler | MultiStepLR(milestones=[120,150], gamma=0.3) |
| | |
...

model

d

Res16UNet34D(
  (conv0p1s1): MinkowskiConvolution(in=3, out=32, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
  (bn0): MinkowskiBatchNorm(32, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
  (conv1p1s2): MinkowskiConvolution(in=32, out=32, kernel_size=[2, 2, 2], stride=[2, 2, 2], dilation=[1, 1, 1])
  (bn1): MinkowskiBatchNorm(32, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
  (block1): Sequential(
    (0): BasicBlock(
      (conv1): MinkowskiConvolution(in=32, out=32, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm1): MinkowskiBatchNorm(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): MinkowskiConvolution(in=32, out=32, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm2): MinkowskiBatchNorm(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): MinkowskiReLU()
    )
    (1): BasicBlock(
      (conv1): MinkowskiConvolution(in=32, out=32, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm1): MinkowskiBatchNorm(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): MinkowskiConvolution(in=32, out=32, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm2): MinkowskiBatchNorm(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): MinkowskiReLU()
    )
  )
  (conv2p2s2): MinkowskiConvolution(in=32, out=32, kernel_size=[2, 2, 2], stride=[2, 2, 2], dilation=[1, 1, 1])
  (bn2): MinkowskiBatchNorm(32, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
  (block2): Sequential(
    (0): BasicBlock(
      (conv1): MinkowskiConvolution(in=32, out=64, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): MinkowskiConvolution(in=64, out=64, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm2): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): MinkowskiReLU()
      (downsample): Sequential(
        (0): MinkowskiConvolution(in=32, out=64, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
        (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
      )
    )
    (1): BasicBlock(
      (conv1): MinkowskiConvolution(in=64, out=64, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): MinkowskiConvolution(in=64, out=64, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm2): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): MinkowskiReLU()
    )
    (2): BasicBlock(
      (conv1): MinkowskiConvolution(in=64, out=64, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): MinkowskiConvolution(in=64, out=64, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm2): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): MinkowskiReLU()
    )
  )
  (conv3p4s2): MinkowskiConvolution(in=64, out=64, kernel_size=[2, 2, 2], stride=[2, 2, 2], dilation=[1, 1, 1])
  (bn3): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
  (block3): Sequential(
    (0): BasicBlock(
      (conv1): MinkowskiConvolution(in=64, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm2): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): MinkowskiReLU()
      (downsample): Sequential(
        (0): MinkowskiConvolution(in=64, out=128, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
        (1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
      )
    )
    (1): BasicBlock(
      (conv1): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm2): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): MinkowskiReLU()
    )
    (2): BasicBlock(
      (conv1): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm2): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): MinkowskiReLU()
    )
    (3): BasicBlock(
      (conv1): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): MinkowskiConvolution(in=128, out=128, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm2): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): MinkowskiReLU()
    )
  )
  (conv4p8s2): MinkowskiConvolution(in=128, out=128, kernel_size=[2, 2, 2], stride=[2, 2, 2], dilation=[1, 1, 1])
  (bn4): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
  (block4): Sequential(
    (0): BasicBlock(
      (conv1): MinkowskiConvolution(in=128, out=256, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): MinkowskiConvolution(in=256, out=256, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm2): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): MinkowskiReLU()
      (downsample): Sequential(
        (0): MinkowskiConvolution(in=128, out=256, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
        (1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
      )
    )
    (1): BasicBlock(
      (conv1): MinkowskiConvolution(in=256, out=256, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): MinkowskiConvolution(in=256, out=256, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm2): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): MinkowskiReLU()
    )
    (2): BasicBlock(
      (conv1): MinkowskiConvolution(in=256, out=256, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): MinkowskiConvolution(in=256, out=256, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm2): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): MinkowskiReLU()
    )
    (3): BasicBlock(
      (conv1): MinkowskiConvolution(in=256, out=256, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): MinkowskiConvolution(in=256, out=256, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm2): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): MinkowskiReLU()
    )
    (4): BasicBlock(
      (conv1): MinkowskiConvolution(in=256, out=256, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): MinkowskiConvolution(in=256, out=256, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm2): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): MinkowskiReLU()
    )
    (5): BasicBlock(
      (conv1): MinkowskiConvolution(in=256, out=256, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): MinkowskiConvolution(in=256, out=256, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm2): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): MinkowskiReLU()
    )
  )
  (convtr4p16s2): MinkowskiConvolutionTranspose(in=256, out=256, kernel_size=[2, 2, 2], stride=[2, 2, 2], dilation=[1, 1, 1])
  (bntr4): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
  (block5): Sequential(
    (0): BasicBlock(
      (conv1): MinkowskiConvolution(in=384, out=256, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): MinkowskiConvolution(in=256, out=256, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm2): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): MinkowskiReLU()
      (downsample): Sequential(
        (0): MinkowskiConvolution(in=384, out=256, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
        (1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
      )
    )
    (1): BasicBlock(
      (conv1): MinkowskiConvolution(in=256, out=256, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): MinkowskiConvolution(in=256, out=256, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm2): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): MinkowskiReLU()
    )
  )
  (convtr5p8s2): MinkowskiConvolutionTranspose(in=256, out=256, kernel_size=[2, 2, 2], stride=[2, 2, 2], dilation=[1, 1, 1])
  (bntr5): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
  (block6): Sequential(
    (0): BasicBlock(
      (conv1): MinkowskiConvolution(in=320, out=256, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): MinkowskiConvolution(in=256, out=256, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm2): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): MinkowskiReLU()
      (downsample): Sequential(
        (0): MinkowskiConvolution(in=320, out=256, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
        (1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
      )
    )
    (1): BasicBlock(
      (conv1): MinkowskiConvolution(in=256, out=256, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): MinkowskiConvolution(in=256, out=256, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm2): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): MinkowskiReLU()
    )
  )
  (convtr6p4s2): MinkowskiConvolutionTranspose(in=256, out=256, kernel_size=[2, 2, 2], stride=[2, 2, 2], dilation=[1, 1, 1])
  (bntr6): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
  (block7): Sequential(
    (0): BasicBlock(
      (conv1): MinkowskiConvolution(in=288, out=256, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): MinkowskiConvolution(in=256, out=256, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm2): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): MinkowskiReLU()
      (downsample): Sequential(
        (0): MinkowskiConvolution(in=288, out=256, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
        (1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
      )
    )
    (1): BasicBlock(
      (conv1): MinkowskiConvolution(in=256, out=256, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): MinkowskiConvolution(in=256, out=256, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm2): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): MinkowskiReLU()
    )
  )
  (convtr7p2s2): MinkowskiConvolutionTranspose(in=256, out=512, kernel_size=[2, 2, 2], stride=[2, 2, 2], dilation=[1, 1, 1])
  (bntr7): MinkowskiBatchNorm(512, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
  (block8): Sequential(
    (0): BasicBlock(
      (conv1): MinkowskiConvolution(in=544, out=512, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm1): MinkowskiBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): MinkowskiConvolution(in=512, out=512, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm2): MinkowskiBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): MinkowskiReLU()
      (downsample): Sequential(
        (0): MinkowskiConvolution(in=544, out=512, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
        (1): MinkowskiBatchNorm(512, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
      )
    )
    (1): NoReluBlock(
      (conv1): MinkowskiConvolution(in=512, out=512, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm1): MinkowskiBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): MinkowskiConvolution(in=512, out=512, kernel_size=[3, 3, 3], stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm2): MinkowskiBatchNorm(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): MinkowskiReLU()
    )
  )
  (final): MinkowskiConvolution(in=512, out=200, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
  (relu): MinkowskiReLU()
)