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checkpoint/modelnet/modelnet40ply2048-train-ppv2-s-ngpus1-seed1234-20250424-143358-QbzzqAa6vYDExr9P9eCVRD_ckpt_best.pth

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+size 31348318

checkpoint/s3dis/s3dis-train-ppv2-xl-ngpus1-20250326-122107-nfhNFTpXV6atsVFWAXCDsk_ckpt_best.pth

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checkpoint/scanobejctnn/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR.log

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+[04/01 16:49:46] ScanObjectNNHardest INFO: dist_url: tcp://localhost:8888
+dist_backend: nccl
+multiprocessing_distributed: False
+ngpus_per_node: 1
+world_size: 1
+launcher: mp
+local_rank: 0
+use_gpu: True
+seed: 1234
+epoch: 0
+epochs: 250
+ignore_index: None
+val_fn: validate
+deterministic: False
+sync_bn: False
+criterion_args:
+  NAME: SmoothCrossEntropy
+  label_smoothing: 0.3
+use_mask: False
+grad_norm_clip: 10
+layer_decay: 0
+step_per_update: 1
+start_epoch: 1
+sched_on_epoch: True
+wandb:
+  use_wandb: False
+  project: PointNeXt-ScanObjectNN
+  tags: ['scanobjectnn', 'train', 'ppv2-s', 'ngpus1', 'seed1234']
+  name: scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR
+use_amp: False
+use_voting: False
+val_freq: 1
+resume: False
+test: False
+finetune: False
+mode: train
+logname: None
+load_path: None
+print_freq: 10
+save_freq: -1
+root_dir: log/scanobjectnn
+pretrained_path: None
+datatransforms:
+  train: ['PointsToTensor', 'PointCloudScaling', 'PointCloudCenterAndNormalize', 'PointCloudRotation']
+  val: ['PointsToTensor', 'PointCloudCenterAndNormalize']
+  vote: ['PointCloudRotation']
+  kwargs:
+    scale: [0.9, 1.1]
+    angle: [0.0, 1.0, 0.0]
+    gravity_dim: 1
+feature_keys: pos
+dataset:
+  common:
+    NAME: ScanObjectNNHardest
+    data_dir: ./data/ScanObjectNN/h5_files/main_split
+  train:
+    split: train
+  val:
+    split: val
+    num_points: 1024
+num_points: 1024
+num_classes: 15
+batch_size: 32
+val_batch_size: 64
+dataloader:
+  num_workers: 6
+lr: 0.002
+optimizer:
+  NAME: adamw
+  weight_decay: 0.05
+sched: cosine
+warmup_epochs: 0
+min_lr: 0.0001
+t_max: 200
+log_dir: log/scanobjectnn/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR
+model:
+  NAME: DpnCls
+  encoder_args:
+    NAME: PPV2Encoder
+    blocks: [1, 1, 1, 1, 1, 1]
+    strides: [1, 2, 2, 2, 2, 1]
+    width: 32
+    in_channels: 4
+    sa_layers: 2
+    sa_use_res: True
+    radius: 0.15
+    flag: 0
+    radius_scaling: 1.5
+    nsample: 32
+    expansion: 4
+    aggr_args:
+      feature_type: dp_fj
+      reduction: max
+    group_args:
+      NAME: ballquery
+      normalize_dp: True
+    conv_args:
+      order: conv-norm-act
+    act_args:
+      act: relu
+    norm_args:
+      norm: bn
+  cls_args:
+    NAME: DpnClsHead
+    num_classes: 15
+    mlps: [512, 256]
+    norm_args:
+      norm: bn1d
+rank: 0
+distributed: False
+mp: False
+task_name: scanobjectnn
+exp_name: ppv2-s
+opts: 
+run_name: scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR
+run_dir: log/scanobjectnn/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR
+exp_dir: log/scanobjectnn/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR
+ckpt_dir: log/scanobjectnn/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR/checkpoint
+log_path: log/scanobjectnn/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR.log
+cfg_path: log/scanobjectnn/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR/cfg.yaml
+[04/01 16:49:46] ScanObjectNNHardest INFO: radius: [[0.15], [0.15], [0.22499999999999998], [0.33749999999999997], [0.50625], [0.7593749999999999]],
+ nsample: [[32], [32], [32], [32], [32], [32]]
+[04/01 16:49:46] ScanObjectNNHardest INFO: NAME: ballquery
+normalize_dp: True
+radius: 0.15
+nsample: 32
+return_idx: True
+[04/01 16:49:46] ScanObjectNNHardest INFO: NAME: ballquery
+normalize_dp: True
+radius: 0.15
+nsample: 32
+return_idx: True
+[04/01 16:49:46] ScanObjectNNHardest INFO: NAME: ballquery
+normalize_dp: True
+radius: 0.525
+nsample: 64
+return_idx: True
+[04/01 16:49:46] ScanObjectNNHardest INFO: NAME: ballquery
+normalize_dp: True
+radius: 0.22499999999999998
+nsample: 32
+return_idx: True
+[04/01 16:49:46] ScanObjectNNHardest INFO: NAME: ballquery
+normalize_dp: True
+radius: 0.7874999999999999
+nsample: 64
+return_idx: True
+[04/01 16:49:46] ScanObjectNNHardest INFO: NAME: ballquery
+normalize_dp: True
+radius: 0.33749999999999997
+nsample: 32
+return_idx: True
+[04/01 16:49:46] ScanObjectNNHardest INFO: NAME: ballquery
+normalize_dp: True
+radius: 0.50625
+nsample: 32
+return_idx: True
+[04/01 16:49:46] ScanObjectNNHardest INFO: NAME: ballquery
+normalize_dp: True
+radius: None
+nsample: None
+return_idx: True
+[04/01 16:49:46] ScanObjectNNHardest INFO: DpnCls(
+  (encoder): PPV2Encoder(
+    (grouper0): QueryAndGroup()
+    (encoder): Sequential(
+      (0): Sequential(
+        (0): SetAbstractionCls(
+          (convs): Sequential(
+            (0): Sequential(
+              (0): Conv1d(12, 32, kernel_size=(1,), stride=(1,))
+            )
+          )
+        )
+      )
+      (1): Sequential(
+        (0): SetAbstractionCls(
+          (skipconv): Sequential(
+            (0): Conv1d(64, 64, kernel_size=(1,), stride=(1,))
+          )
+          (act): ReLU(inplace=True)
+          (grouper): QueryAndGroup()
+          (preconv): Sequential(
+            (0): Conv1d(32, 64, kernel_size=(1,), stride=(1,), bias=False)
+            (1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+            (2): ReLU(inplace=True)
+          )
+          (scorenet_global): Sequential(
+            (0): Conv1d(8, 1, kernel_size=(1,), stride=(1,))
+            (1): BatchNorm1d(1, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+            (2): ReLU(inplace=True)
+            (3): Conv1d(1, 1, kernel_size=(1,), stride=(1,))
+            (4): BatchNorm1d(1, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+          )
+          (pt): PointTransformerLayer(
+            (linear_q): Linear(in_features=8, out_features=8, bias=True)
+            (linear_k): Linear(in_features=8, out_features=8, bias=True)
+            (linear_p): Sequential(
+              (0): Conv2d(3, 3, kernel_size=(1, 1), stride=(1, 1))
+              (1): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+              (2): ReLU(inplace=True)
+              (3): Conv2d(3, 3, kernel_size=(1, 1), stride=(1, 1))
+            )
+            (w): Sequential(
+              (0): BatchNorm2d(11, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+              (1): ReLU(inplace=True)
+              (2): Conv2d(11, 1, kernel_size=(1, 1), stride=(1, 1))
+              (3): BatchNorm2d(1, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+              (4): ReLU(inplace=True)
+            )
+            (v): Sequential(
+              (0): Conv2d(11, 64, kernel_size=(1, 1), stride=(1, 1))
+              (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+              (2): ReLU(inplace=True)
+              (3): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))
+            )
+            (softmax): Softmax(dim=-1)
+            (conv_p): Sequential(
+              (0): Conv2d(3, 3, kernel_size=(1, 1), stride=(1, 1))
+              (1): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+              (2): ReLU(inplace=True)
+              (3): Conv2d(3, 64, kernel_size=(1, 1), stride=(1, 1))
+            )
+          )
+          (conv_finanal): Sequential(
+            (0): Conv1d(64, 64, kernel_size=(1,), stride=(1,), bias=False)
+            (1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+            (2): ReLU(inplace=True)
+          )
+          (selfattention): SelfAttention(
+            (linear_q): Linear(in_features=8, out_features=8, bias=True)
+            (linear_k): Linear(in_features=8, out_features=8, bias=True)
+            (linear_v): Sequential(
+              (0): Conv1d(8, 64, kernel_size=(1,), stride=(1,))
+              (1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+              (2): ReLU(inplace=True)
+              (3): Conv1d(64, 64, kernel_size=(1,), stride=(1,))
+              (4): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+              (5): ReLU(inplace=True)
+            )
+            (softmax): Softmax(dim=-1)
+          )
+          (key_grouper): QueryAndGroup()
+        )
+      )
+      (2): Sequential(
+        (0): SetAbstractionCls(
+          (skipconv): Sequential(
+            (0): Conv1d(128, 128, kernel_size=(1,), stride=(1,))
+          )
+          (act): ReLU(inplace=True)
+          (grouper): QueryAndGroup()
+          (scorenet_global): Sequential(
+            (0): Conv1d(8, 3, kernel_size=(1,), stride=(1,))
+            (1): BatchNorm1d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+            (2): ReLU(inplace=True)
+            (3): Conv1d(3, 3, kernel_size=(1,), stride=(1,))
+            (4): BatchNorm1d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+          )
+          (preconv): Sequential(
+            (0): Conv1d(64, 128, kernel_size=(1,), stride=(1,), bias=False)
+            (1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+            (2): ReLU(inplace=True)
+          )
+          (pt): PointTransformerLayer(
+            (linear_q): Linear(in_features=24, out_features=8, bias=True)
+            (linear_k): Linear(in_features=8, out_features=8, bias=True)
+            (linear_p): Sequential(
+              (0): Conv2d(3, 3, kernel_size=(1, 1), stride=(1, 1))
+              (1): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+              (2): ReLU(inplace=True)
+              (3): Conv2d(3, 3, kernel_size=(1, 1), stride=(1, 1))
+            )
+            (w): Sequential(
+              (0): BatchNorm2d(11, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+              (1): ReLU(inplace=True)
+              (2): Conv2d(11, 1, kernel_size=(1, 1), stride=(1, 1))
+              (3): BatchNorm2d(1, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+              (4): ReLU(inplace=True)
+            )
+            (v): Sequential(
+              (0): Conv2d(11, 128, kernel_size=(1, 1), stride=(1, 1))
+              (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+              (2): ReLU(inplace=True)
+              (3): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
+            )
+            (softmax): Softmax(dim=-1)
+            (conv_p): Sequential(
+              (0): Conv2d(3, 3, kernel_size=(1, 1), stride=(1, 1))
+              (1): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+              (2): ReLU(inplace=True)
+              (3): Conv2d(3, 128, kernel_size=(1, 1), stride=(1, 1))
+            )
+          )
+          (conv_finanal): Sequential(
+            (0): Conv1d(128, 128, kernel_size=(1,), stride=(1,), bias=False)
+            (1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+            (2): ReLU(inplace=True)
+          )
+          (selfattention): SelfAttention(
+            (linear_q): Linear(in_features=24, out_features=24, bias=True)
+            (linear_k): Linear(in_features=24, out_features=24, bias=True)
+            (linear_v): Sequential(
+              (0): Conv1d(24, 128, kernel_size=(1,), stride=(1,))
+              (1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+              (2): ReLU(inplace=True)
+              (3): Conv1d(128, 128, kernel_size=(1,), stride=(1,))
+              (4): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+              (5): ReLU(inplace=True)
+            )
+            (softmax): Softmax(dim=-1)
+          )
+          (key_grouper): QueryAndGroup()
+        )
+      )
+      (3): Sequential(
+        (0): SetAbstractionCls(
+          (skipconv): Sequential(
+            (0): Conv1d(256, 256, kernel_size=(1,), stride=(1,))
+          )
+          (act): ReLU(inplace=True)
+          (grouper): QueryAndGroup()
+          (scorenet_global): Sequential(
+            (0): Conv1d(24, 9, kernel_size=(1,), stride=(1,))
+            (1): BatchNorm1d(9, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+            (2): ReLU(inplace=True)
+            (3): Conv1d(9, 9, kernel_size=(1,), stride=(1,))
+            (4): BatchNorm1d(9, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+          )
+          (preconv): Sequential(
+            (0): Conv1d(128, 256, kernel_size=(1,), stride=(1,), bias=False)
+            (1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+            (2): ReLU(inplace=True)
+          )
+          (pt): PointTransformerLayer(
+            (linear_q): Linear(in_features=72, out_features=24, bias=True)
+            (linear_k): Linear(in_features=24, out_features=24, bias=True)
+            (linear_p): Sequential(
+              (0): Conv2d(3, 3, kernel_size=(1, 1), stride=(1, 1))
+              (1): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+              (2): ReLU(inplace=True)
+              (3): Conv2d(3, 3, kernel_size=(1, 1), stride=(1, 1))
+            )
+            (w): Sequential(
+              (0): BatchNorm2d(27, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+              (1): ReLU(inplace=True)
+              (2): Conv2d(27, 1, kernel_size=(1, 1), stride=(1, 1))
+              (3): BatchNorm2d(1, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+              (4): ReLU(inplace=True)
+            )
+            (v): Sequential(
+              (0): Conv2d(27, 256, kernel_size=(1, 1), stride=(1, 1))
+              (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+              (2): ReLU(inplace=True)
+              (3): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
+            )
+            (softmax): Softmax(dim=-1)
+            (conv_p): Sequential(
+              (0): Conv2d(3, 3, kernel_size=(1, 1), stride=(1, 1))
+              (1): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+              (2): ReLU(inplace=True)
+              (3): Conv2d(3, 256, kernel_size=(1, 1), stride=(1, 1))
+            )
+          )
+          (conv_finanal): Sequential(
+            (0): Conv1d(256, 256, kernel_size=(1,), stride=(1,), bias=False)
+            (1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+            (2): ReLU(inplace=True)
+          )
+          (selfattention): SelfAttention(
+            (linear_q): Linear(in_features=72, out_features=72, bias=True)
+            (linear_k): Linear(in_features=72, out_features=72, bias=True)
+            (linear_v): Sequential(
+              (0): Conv1d(72, 256, kernel_size=(1,), stride=(1,))
+              (1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+              (2): ReLU(inplace=True)
+              (3): Conv1d(256, 256, kernel_size=(1,), stride=(1,))
+              (4): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+              (5): ReLU(inplace=True)
+            )
+            (softmax): Softmax(dim=-1)
+          )
+        )
+      )
+      (4): Sequential(
+        (0): SetAbstractionCls(
+          (skipconv): Sequential(
+            (0): Conv1d(512, 512, kernel_size=(1,), stride=(1,))
+          )
+          (act): ReLU(inplace=True)
+          (grouper): QueryAndGroup()
+          (scorenet_global): Sequential(
+            (0): Conv1d(72, 27, kernel_size=(1,), stride=(1,))
+            (1): BatchNorm1d(27, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+            (2): ReLU(inplace=True)
+            (3): Conv1d(27, 27, kernel_size=(1,), stride=(1,))
+            (4): BatchNorm1d(27, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+          )
+          (preconv): Sequential(
+            (0): Conv1d(256, 512, kernel_size=(1,), stride=(1,), bias=False)
+            (1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+            (2): ReLU(inplace=True)
+          )
+          (pt): PointTransformerLayer(
+            (linear_q): Linear(in_features=216, out_features=72, bias=True)
+            (linear_k): Linear(in_features=72, out_features=72, bias=True)
+            (linear_p): Sequential(
+              (0): Conv2d(3, 3, kernel_size=(1, 1), stride=(1, 1))
+              (1): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+              (2): ReLU(inplace=True)
+              (3): Conv2d(3, 3, kernel_size=(1, 1), stride=(1, 1))
+            )
+            (w): Sequential(
+              (0): BatchNorm2d(75, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+              (1): ReLU(inplace=True)
+              (2): Conv2d(75, 1, kernel_size=(1, 1), stride=(1, 1))
+              (3): BatchNorm2d(1, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+              (4): ReLU(inplace=True)
+            )
+            (v): Sequential(
+              (0): Conv2d(75, 512, kernel_size=(1, 1), stride=(1, 1))
+              (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+              (2): ReLU(inplace=True)
+              (3): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1))
+            )
+            (softmax): Softmax(dim=-1)
+            (conv_p): Sequential(
+              (0): Conv2d(3, 3, kernel_size=(1, 1), stride=(1, 1))
+              (1): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+              (2): ReLU(inplace=True)
+              (3): Conv2d(3, 512, kernel_size=(1, 1), stride=(1, 1))
+            )
+          )
+          (conv_finanal): Sequential(
+            (0): Conv1d(512, 512, kernel_size=(1,), stride=(1,), bias=False)
+            (1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+            (2): ReLU(inplace=True)
+          )
+          (selfattention): SelfAttention(
+            (linear_q): Linear(in_features=216, out_features=216, bias=True)
+            (linear_k): Linear(in_features=216, out_features=216, bias=True)
+            (linear_v): Sequential(
+              (0): Conv1d(216, 512, kernel_size=(1,), stride=(1,))
+              (1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+              (2): ReLU(inplace=True)
+              (3): Conv1d(512, 512, kernel_size=(1,), stride=(1,))
+              (4): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+              (5): ReLU(inplace=True)
+            )
+            (softmax): Softmax(dim=-1)
+          )
+        )
+      )
+      (5): Sequential(
+        (0): SetAbstractionCls(
+          (grouper): GroupAll()
+          (preconv): Sequential(
+            (0): Conv1d(512, 512, kernel_size=(1,), stride=(1,), bias=False)
+            (1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+            (2): ReLU(inplace=True)
+          )
+        )
+      )
+    )
+  )
+  (prediction): DpnClsHead(
+    (head): Sequential(
+      (0): Sequential(
+        (0): Linear(in_features=512, out_features=512, bias=False)
+        (1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (2): ReLU(inplace=True)
+      )
+      (1): Dropout(p=0.5, inplace=False)
+      (2): Sequential(
+        (0): Linear(in_features=512, out_features=256, bias=False)
+        (1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+        (2): ReLU(inplace=True)
+      )
+      (3): Dropout(p=0.5, inplace=False)
+      (4): Sequential(
+        (0): Linear(in_features=256, out_features=15, bias=True)
+      )
+    )
+  )
+  (criterion): SmoothCrossEntropy()
+)
+[04/01 16:49:46] ScanObjectNNHardest INFO: Number of params: 2.5603 M
+[04/01 16:49:46] ScanObjectNNHardest INFO: Param groups = {
+  "decay": {
+    "weight_decay": 0.05,
+    "params": [
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+      "prediction.head.4.0.bias"
+    ],
+    "lr_scale": 1.0
+  }
+}
+[04/01 16:49:48] ScanObjectNNHardest INFO: Successfully load ScanObjectNN val size: (2882, 1024, 3), num_classes: 15
+[04/01 16:49:48] ScanObjectNNHardest INFO: length of validation dataset: 2882
+[04/01 16:49:49] ScanObjectNNHardest INFO: Successfully load ScanObjectNN val size: (2882, 1024, 3), num_classes: 15
+[04/01 16:49:49] ScanObjectNNHardest INFO: number of classes of the dataset: 15, number of points sampled from dataset: 1024, number of points as model input: 1024
+[04/01 16:49:49] ScanObjectNNHardest INFO: Training from scratch
+[04/01 16:49:52] ScanObjectNNHardest INFO: Successfully load ScanObjectNN train size: (11416, 2048, 3), num_classes: 15
+[04/01 16:49:52] ScanObjectNNHardest INFO: length of training dataset: 11416
+[04/01 16:50:53] ScanObjectNNHardest INFO: Find a better ckpt @E1
+[04/01 16:50:53] ScanObjectNNHardest INFO: 
+Classes	Acc
+bag       : 0.00%
+bin       : 16.58%
+box       : 2.26%
+cabinet   : 35.48%
+chair     : 85.90%
+desk      : 17.33%
+display   : 64.71%
+door      : 90.00%
+shelf     : 58.09%
+table     : 10.37%
+bed       : 96.36%
+pillow    : 16.19%
+sink      : 3.33%
+sofa      : 18.10%
+toilet    : 0.00%
+E@1	OA: 41.05	mAcc: 34.31
+
+[04/01 16:50:53] ScanObjectNNHardest INFO: Epoch 1 LR 0.002000 train_oa 34.28, val_oa 41.05, best val oa 41.05
+[04/01 16:50:53] ScanObjectNNHardest INFO: Found the best model and saved in log/scanobjectnn/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR/checkpoint/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR_ckpt_best.pth
+[04/01 16:51:46] ScanObjectNNHardest INFO: Find a better ckpt @E2
+[04/01 16:51:46] ScanObjectNNHardest INFO: 
+Classes	Acc
+bag       : 0.00%
+bin       : 55.78%
+box       : 0.75%
+cabinet   : 54.30%
+chair     : 62.31%
+desk      : 23.33%
+display   : 64.22%
+door      : 87.14%
+shelf     : 81.74%
+table     : 39.63%
+bed       : 85.45%
+pillow    : 1.90%
+sink      : 24.17%
+sofa      : 82.86%
+toilet    : 1.18%
+E@2	OA: 52.39	mAcc: 44.32
+
+[04/01 16:51:46] ScanObjectNNHardest INFO: Epoch 2 LR 0.002000 train_oa 54.96, val_oa 52.39, best val oa 52.39
+[04/01 16:51:46] ScanObjectNNHardest INFO: Found the best model and saved in log/scanobjectnn/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR/checkpoint/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR_ckpt_best.pth
+[04/01 16:52:40] ScanObjectNNHardest INFO: Find a better ckpt @E3
+[04/01 16:52:40] ScanObjectNNHardest INFO: 
+Classes	Acc
+bag       : 39.76%
+bin       : 67.84%
+box       : 6.02%
+cabinet   : 41.40%
+chair     : 88.46%
+desk      : 10.67%
+display   : 61.76%
+door      : 93.33%
+shelf     : 75.93%
+table     : 64.07%
+bed       : 78.18%
+pillow    : 56.19%
+sink      : 16.67%
+sofa      : 81.90%
+toilet    : 8.24%
+E@3	OA: 59.44	mAcc: 52.69
+
+[04/01 16:52:40] ScanObjectNNHardest INFO: Epoch 3 LR 0.002000 train_oa 60.34, val_oa 59.44, best val oa 59.44
+[04/01 16:52:40] ScanObjectNNHardest INFO: Found the best model and saved in log/scanobjectnn/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR/checkpoint/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR_ckpt_best.pth
+[04/01 16:53:40] ScanObjectNNHardest INFO: Find a better ckpt @E4
+[04/01 16:53:40] ScanObjectNNHardest INFO: 
+Classes	Acc
+bag       : 9.64%
+bin       : 84.92%
+box       : 24.06%
+cabinet   : 50.81%
+chair     : 77.95%
+desk      : 72.67%
+display   : 77.45%
+door      : 95.71%
+shelf     : 72.20%
+table     : 45.93%
+bed       : 80.91%
+pillow    : 52.38%
+sink      : 55.83%
+sofa      : 66.67%
+toilet    : 44.71%
+E@4	OA: 64.43	mAcc: 60.79
+
+[04/01 16:53:40] ScanObjectNNHardest INFO: Epoch 4 LR 0.001999 train_oa 65.58, val_oa 64.43, best val oa 64.43
+[04/01 16:53:41] ScanObjectNNHardest INFO: Found the best model and saved in log/scanobjectnn/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR/checkpoint/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR_ckpt_best.pth
+[04/01 16:54:36] ScanObjectNNHardest INFO: Find a better ckpt @E5
+[04/01 16:54:36] ScanObjectNNHardest INFO: 
+Classes	Acc
+bag       : 45.78%
+bin       : 54.27%
+box       : 36.09%
+cabinet   : 72.58%
+chair     : 94.36%
+desk      : 58.67%
+display   : 78.43%
+door      : 77.62%
+shelf     : 80.91%
+table     : 48.52%
+bed       : 48.18%
+pillow    : 57.14%
+sink      : 39.17%
+sofa      : 91.43%
+toilet    : 35.29%
+E@5	OA: 67.70	mAcc: 61.23
+
+[04/01 16:54:36] ScanObjectNNHardest INFO: Epoch 5 LR 0.001998 train_oa 69.57, val_oa 67.70, best val oa 67.70
+[04/01 16:54:36] ScanObjectNNHardest INFO: Found the best model and saved in log/scanobjectnn/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR/checkpoint/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR_ckpt_best.pth
+[04/01 16:55:29] ScanObjectNNHardest INFO: Epoch 6 LR 0.001997 train_oa 71.75, val_oa 63.60, best val oa 67.70
+[04/01 16:56:27] ScanObjectNNHardest INFO: Find a better ckpt @E7
+[04/01 16:56:27] ScanObjectNNHardest INFO: 
+Classes	Acc
+bag       : 38.55%
+bin       : 83.92%
+box       : 27.07%
+cabinet   : 59.95%
+chair     : 87.44%
+desk      : 74.00%
+display   : 80.88%
+door      : 97.14%
+shelf     : 81.33%
+table     : 57.78%
+bed       : 70.91%
+pillow    : 70.48%
+sink      : 61.67%
+sofa      : 92.86%
+toilet    : 38.82%
+E@7	OA: 72.35	mAcc: 68.19
+
+[04/01 16:56:27] ScanObjectNNHardest INFO: Epoch 7 LR 0.001996 train_oa 73.81, val_oa 72.35, best val oa 72.35
+[04/01 16:56:27] ScanObjectNNHardest INFO: Found the best model and saved in log/scanobjectnn/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR/checkpoint/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR_ckpt_best.pth
+[04/01 16:57:22] ScanObjectNNHardest INFO: Epoch 8 LR 0.001994 train_oa 75.90, val_oa 71.65, best val oa 72.35
+[04/01 16:58:18] ScanObjectNNHardest INFO: Epoch 9 LR 0.001993 train_oa 77.22, val_oa 72.00, best val oa 72.35
+[04/01 16:59:16] ScanObjectNNHardest INFO: Find a better ckpt @E10
+[04/01 16:59:16] ScanObjectNNHardest INFO: 
+Classes	Acc
+bag       : 43.37%
+bin       : 80.40%
+box       : 45.11%
+cabinet   : 76.08%
+chair     : 79.23%
+desk      : 78.00%
+display   : 78.43%
+door      : 93.81%
+shelf     : 82.57%
+table     : 51.11%
+bed       : 76.36%
+pillow    : 68.57%
+sink      : 61.67%
+sofa      : 95.71%
+toilet    : 65.88%
+E@10	OA: 74.46	mAcc: 71.75
+
+[04/01 16:59:16] ScanObjectNNHardest INFO: Epoch 10 LR 0.001991 train_oa 78.49, val_oa 74.46, best val oa 74.46
+[04/01 16:59:16] ScanObjectNNHardest INFO: Found the best model and saved in log/scanobjectnn/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR/checkpoint/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR_ckpt_best.pth
+[04/01 17:00:11] ScanObjectNNHardest INFO: Find a better ckpt @E11
+[04/01 17:00:11] ScanObjectNNHardest INFO: 
+Classes	Acc
+bag       : 33.73%
+bin       : 79.90%
+box       : 36.09%
+cabinet   : 76.88%
+chair     : 94.36%
+desk      : 72.67%
+display   : 84.31%
+door      : 93.33%
+shelf     : 80.50%
+table     : 60.37%
+bed       : 76.36%
+pillow    : 71.43%
+sink      : 66.67%
+sofa      : 93.81%
+toilet    : 76.47%
+E@11	OA: 77.17	mAcc: 73.13
+
+[04/01 17:00:11] ScanObjectNNHardest INFO: Epoch 11 LR 0.001988 train_oa 79.71, val_oa 77.17, best val oa 77.17
+[04/01 17:00:11] ScanObjectNNHardest INFO: Found the best model and saved in log/scanobjectnn/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR/checkpoint/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR_ckpt_best.pth
+[04/01 17:01:06] ScanObjectNNHardest INFO: Epoch 12 LR 0.001986 train_oa 80.06, val_oa 76.16, best val oa 77.17
+[04/01 17:02:03] ScanObjectNNHardest INFO: Epoch 13 LR 0.001983 train_oa 80.84, val_oa 76.72, best val oa 77.17
+[04/01 17:03:05] ScanObjectNNHardest INFO: Find a better ckpt @E14
+[04/01 17:03:05] ScanObjectNNHardest INFO: 
+Classes	Acc
+bag       : 48.19%
+bin       : 81.91%
+box       : 57.89%
+cabinet   : 83.33%
+chair     : 78.97%
+desk      : 65.33%
+display   : 76.47%
+door      : 91.43%
+shelf     : 85.06%
+table     : 80.37%
+bed       : 83.64%
+pillow    : 70.48%
+sink      : 50.83%
+sofa      : 88.57%
+toilet    : 75.29%
+E@14	OA: 77.83	mAcc: 74.52
+
+[04/01 17:03:05] ScanObjectNNHardest INFO: Epoch 14 LR 0.001980 train_oa 82.26, val_oa 77.83, best val oa 77.83
+[04/01 17:03:05] ScanObjectNNHardest INFO: Found the best model and saved in log/scanobjectnn/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR/checkpoint/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR_ckpt_best.pth
+[04/01 17:04:03] ScanObjectNNHardest INFO: Epoch 15 LR 0.001977 train_oa 83.02, val_oa 76.79, best val oa 77.83
+[04/01 17:04:59] ScanObjectNNHardest INFO: Epoch 16 LR 0.001974 train_oa 83.96, val_oa 76.06, best val oa 77.83
+[04/01 17:05:58] ScanObjectNNHardest INFO: Find a better ckpt @E17
+[04/01 17:05:58] ScanObjectNNHardest INFO: 
+Classes	Acc
+bag       : 49.40%
+bin       : 89.45%
+box       : 54.14%
+cabinet   : 76.88%
+chair     : 94.62%
+desk      : 57.33%
+display   : 75.49%
+door      : 85.71%
+shelf     : 83.82%
+table     : 72.22%
+bed       : 88.18%
+pillow    : 70.48%
+sink      : 69.17%
+sofa      : 85.24%
+toilet    : 61.18%
+E@17	OA: 78.00	mAcc: 74.22
+
+[04/01 17:05:58] ScanObjectNNHardest INFO: Epoch 17 LR 0.001970 train_oa 84.21, val_oa 78.00, best val oa 78.00
+[04/01 17:05:58] ScanObjectNNHardest INFO: Found the best model and saved in log/scanobjectnn/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR/checkpoint/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR_ckpt_best.pth
+[04/01 17:06:55] ScanObjectNNHardest INFO: Find a better ckpt @E18
+[04/01 17:06:55] ScanObjectNNHardest INFO: 
+Classes	Acc
+bag       : 33.73%
+bin       : 85.43%
+box       : 46.62%
+cabinet   : 80.91%
+chair     : 93.85%
+desk      : 78.00%
+display   : 82.35%
+door      : 91.43%
+shelf     : 88.80%
+table     : 61.85%
+bed       : 74.55%
+pillow    : 84.76%
+sink      : 65.83%
+sofa      : 94.29%
+toilet    : 70.59%
+E@18	OA: 79.56	mAcc: 75.53
+
+[04/01 17:06:55] ScanObjectNNHardest INFO: Epoch 18 LR 0.001966 train_oa 85.17, val_oa 79.56, best val oa 79.56
+[04/01 17:06:55] ScanObjectNNHardest INFO: Found the best model and saved in log/scanobjectnn/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR/checkpoint/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR_ckpt_best.pth
+[04/01 17:07:51] ScanObjectNNHardest INFO: Epoch 19 LR 0.001962 train_oa 85.67, val_oa 74.77, best val oa 79.56
+[04/01 17:08:45] ScanObjectNNHardest INFO: Find a better ckpt @E20
+[04/01 17:08:45] ScanObjectNNHardest INFO: 
+Classes	Acc
+bag       : 43.37%
+bin       : 76.38%
+box       : 59.40%
+cabinet   : 80.11%
+chair     : 91.03%
+desk      : 76.67%
+display   : 86.27%
+door      : 92.38%
+shelf     : 83.40%
+table     : 67.04%
+bed       : 74.55%
+pillow    : 81.90%
+sink      : 72.50%
+sofa      : 95.71%
+toilet    : 78.82%
+E@20	OA: 80.15	mAcc: 77.30
+
+[04/01 17:08:45] ScanObjectNNHardest INFO: Epoch 20 LR 0.001958 train_oa 86.52, val_oa 80.15, best val oa 80.15
+[04/01 17:08:45] ScanObjectNNHardest INFO: Found the best model and saved in log/scanobjectnn/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR/checkpoint/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR_ckpt_best.pth
+[04/01 17:09:39] ScanObjectNNHardest INFO: Find a better ckpt @E21
+[04/01 17:09:39] ScanObjectNNHardest INFO: 
+Classes	Acc
+bag       : 65.06%
+bin       : 77.89%
+box       : 68.42%
+cabinet   : 83.60%
+chair     : 89.23%
+desk      : 74.00%
+display   : 73.04%
+door      : 96.19%
+shelf     : 80.91%
+table     : 74.81%
+bed       : 75.45%
+pillow    : 84.76%
+sink      : 77.50%
+sofa      : 94.76%
+toilet    : 74.12%
+E@21	OA: 81.37	mAcc: 79.32
+
+[04/01 17:09:39] ScanObjectNNHardest INFO: Epoch 21 LR 0.001954 train_oa 87.10, val_oa 81.37, best val oa 81.37
+[04/01 17:09:39] ScanObjectNNHardest INFO: Found the best model and saved in log/scanobjectnn/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR/checkpoint/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR_ckpt_best.pth
+[04/01 17:10:40] ScanObjectNNHardest INFO: Find a better ckpt @E22
+[04/01 17:10:40] ScanObjectNNHardest INFO: 
+Classes	Acc
+bag       : 39.76%
+bin       : 79.90%
+box       : 78.95%
+cabinet   : 79.03%
+chair     : 89.49%
+desk      : 82.00%
+display   : 92.16%
+door      : 97.14%
+shelf     : 90.87%
+table     : 62.96%
+bed       : 84.55%
+pillow    : 78.10%
+sink      : 62.50%
+sofa      : 93.81%
+toilet    : 70.59%
+E@22	OA: 81.58	mAcc: 78.79
+
+[04/01 17:10:40] ScanObjectNNHardest INFO: Epoch 22 LR 0.001949 train_oa 87.21, val_oa 81.58, best val oa 81.58
+[04/01 17:10:40] ScanObjectNNHardest INFO: Found the best model and saved in log/scanobjectnn/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR/checkpoint/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR_ckpt_best.pth
+[04/01 17:11:37] ScanObjectNNHardest INFO: Epoch 23 LR 0.001944 train_oa 87.70, val_oa 80.57, best val oa 81.58
+[04/01 17:12:34] ScanObjectNNHardest INFO: Epoch 24 LR 0.001939 train_oa 88.47, val_oa 80.95, best val oa 81.58
+[04/01 17:13:32] ScanObjectNNHardest INFO: Epoch 25 LR 0.001933 train_oa 88.60, val_oa 80.40, best val oa 81.58
+[04/01 17:14:26] ScanObjectNNHardest INFO: Epoch 26 LR 0.001928 train_oa 89.15, val_oa 81.05, best val oa 81.58
+[04/01 17:15:21] ScanObjectNNHardest INFO: Epoch 27 LR 0.001922 train_oa 89.88, val_oa 80.01, best val oa 81.58
+[04/01 17:16:12] ScanObjectNNHardest INFO: Epoch 28 LR 0.001916 train_oa 89.87, val_oa 79.15, best val oa 81.58
+[04/01 17:17:11] ScanObjectNNHardest INFO: Epoch 29 LR 0.001910 train_oa 90.19, val_oa 80.60, best val oa 81.58
+[04/01 17:18:10] ScanObjectNNHardest INFO: Epoch 30 LR 0.001903 train_oa 90.85, val_oa 80.92, best val oa 81.58
+[04/01 17:19:05] ScanObjectNNHardest INFO: Epoch 31 LR 0.001896 train_oa 91.25, val_oa 81.47, best val oa 81.58
+[04/01 17:20:04] ScanObjectNNHardest INFO: Find a better ckpt @E32
+[04/01 17:20:04] ScanObjectNNHardest INFO: 
+Classes	Acc
+bag       : 55.42%
+bin       : 85.93%
+box       : 39.10%
+cabinet   : 86.83%
+chair     : 95.90%
+desk      : 60.67%
+display   : 84.80%
+door      : 96.67%
+shelf     : 89.63%
+table     : 72.59%
+bed       : 73.64%
+pillow    : 86.67%
+sink      : 65.83%
+sofa      : 90.95%
+toilet    : 92.94%
+E@32	OA: 82.10	mAcc: 78.50
+
+[04/01 17:20:04] ScanObjectNNHardest INFO: Epoch 32 LR 0.001890 train_oa 91.56, val_oa 82.10, best val oa 82.10
+[04/01 17:20:05] ScanObjectNNHardest INFO: Found the best model and saved in log/scanobjectnn/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR/checkpoint/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR_ckpt_best.pth
+[04/01 17:21:07] ScanObjectNNHardest INFO: Epoch 33 LR 0.001882 train_oa 91.90, val_oa 81.78, best val oa 82.10
+[04/01 17:22:03] ScanObjectNNHardest INFO: Find a better ckpt @E34
+[04/01 17:22:03] ScanObjectNNHardest INFO: 
+Classes	Acc
+bag       : 54.22%
+bin       : 83.92%
+box       : 64.66%
+cabinet   : 85.75%
+chair     : 86.92%
+desk      : 70.67%
+display   : 85.29%
+door      : 95.71%
+shelf     : 88.80%
+table     : 82.59%
+bed       : 83.64%
+pillow    : 86.67%
+sink      : 59.17%
+sofa      : 92.86%
+toilet    : 75.29%
+E@34	OA: 82.82	mAcc: 79.74
+
+[04/01 17:22:03] ScanObjectNNHardest INFO: Epoch 34 LR 0.001875 train_oa 92.25, val_oa 82.82, best val oa 82.82
+[04/01 17:22:03] ScanObjectNNHardest INFO: Found the best model and saved in log/scanobjectnn/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR/checkpoint/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR_ckpt_best.pth
+[04/01 17:23:02] ScanObjectNNHardest INFO: Epoch 35 LR 0.001868 train_oa 92.51, val_oa 82.34, best val oa 82.82
+[04/01 17:24:01] ScanObjectNNHardest INFO: Epoch 36 LR 0.001860 train_oa 92.84, val_oa 80.78, best val oa 82.82
+[04/01 17:24:59] ScanObjectNNHardest INFO: Epoch 37 LR 0.001852 train_oa 93.11, val_oa 82.10, best val oa 82.82
+[04/01 17:25:56] ScanObjectNNHardest INFO: Find a better ckpt @E38
+[04/01 17:25:56] ScanObjectNNHardest INFO: 
+Classes	Acc
+bag       : 66.27%
+bin       : 80.40%
+box       : 66.17%
+cabinet   : 84.41%
+chair     : 93.85%
+desk      : 80.67%
+display   : 76.96%
+door      : 98.57%
+shelf     : 87.97%
+table     : 63.70%
+bed       : 88.18%
+pillow    : 84.76%
+sink      : 73.33%
+sofa      : 91.43%
+toilet    : 84.71%
+E@38	OA: 82.93	mAcc: 81.42
+
+[04/01 17:25:56] ScanObjectNNHardest INFO: Epoch 38 LR 0.001844 train_oa 93.75, val_oa 82.93, best val oa 82.93
+[04/01 17:25:57] ScanObjectNNHardest INFO: Found the best model and saved in log/scanobjectnn/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR/checkpoint/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR_ckpt_best.pth
+[04/01 17:26:54] ScanObjectNNHardest INFO: Epoch 39 LR 0.001836 train_oa 93.66, val_oa 82.62, best val oa 82.93
+[04/01 17:27:49] ScanObjectNNHardest INFO: Epoch 40 LR 0.001827 train_oa 93.26, val_oa 82.55, best val oa 82.93
+[04/01 17:28:44] ScanObjectNNHardest INFO: Epoch 41 LR 0.001819 train_oa 94.15, val_oa 82.72, best val oa 82.93
+[04/01 17:29:48] ScanObjectNNHardest INFO: Epoch 42 LR 0.001810 train_oa 93.75, val_oa 81.16, best val oa 82.93
+[04/01 17:30:44] ScanObjectNNHardest INFO: Epoch 43 LR 0.001801 train_oa 94.18, val_oa 82.34, best val oa 82.93
+[04/01 17:31:41] ScanObjectNNHardest INFO: Epoch 44 LR 0.001791 train_oa 94.46, val_oa 81.12, best val oa 82.93
+[04/01 17:32:37] ScanObjectNNHardest INFO: Find a better ckpt @E45
+[04/01 17:32:37] ScanObjectNNHardest INFO: 
+Classes	Acc
+bag       : 60.24%
+bin       : 87.44%
+box       : 56.39%
+cabinet   : 87.63%
+chair     : 85.38%
+desk      : 67.33%
+display   : 85.29%
+door      : 91.43%
+shelf     : 89.21%
+table     : 82.96%
+bed       : 77.27%
+pillow    : 83.81%
+sink      : 68.33%
+sofa      : 96.67%
+toilet    : 89.41%
+E@45	OA: 83.21	mAcc: 80.59
+
+[04/01 17:32:37] ScanObjectNNHardest INFO: Epoch 45 LR 0.001782 train_oa 94.85, val_oa 83.21, best val oa 83.21
+[04/01 17:32:37] ScanObjectNNHardest INFO: Found the best model and saved in log/scanobjectnn/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR/checkpoint/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR_ckpt_best.pth
+[04/01 17:33:32] ScanObjectNNHardest INFO: Find a better ckpt @E46
+[04/01 17:33:32] ScanObjectNNHardest INFO: 
+Classes	Acc
+bag       : 65.06%
+bin       : 82.91%
+box       : 72.18%
+cabinet   : 81.99%
+chair     : 94.62%
+desk      : 88.00%
+display   : 84.31%
+door      : 95.71%
+shelf     : 89.63%
+table     : 62.96%
+bed       : 75.45%
+pillow    : 81.90%
+sink      : 71.67%
+sofa      : 97.14%
+toilet    : 78.82%
+E@46	OA: 83.48	mAcc: 81.49
+
+[04/01 17:33:32] ScanObjectNNHardest INFO: Epoch 46 LR 0.001772 train_oa 94.63, val_oa 83.48, best val oa 83.48
+[04/01 17:33:32] ScanObjectNNHardest INFO: Found the best model and saved in log/scanobjectnn/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR/checkpoint/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR_ckpt_best.pth
+[04/01 17:34:33] ScanObjectNNHardest INFO: Epoch 47 LR 0.001763 train_oa 95.06, val_oa 82.06, best val oa 83.48
+[04/01 17:35:27] ScanObjectNNHardest INFO: Epoch 48 LR 0.001753 train_oa 95.17, val_oa 82.96, best val oa 83.48
+[04/01 17:36:23] ScanObjectNNHardest INFO: Epoch 49 LR 0.001743 train_oa 95.73, val_oa 82.10, best val oa 83.48
+[04/01 17:37:18] ScanObjectNNHardest INFO: Find a better ckpt @E50
+[04/01 17:37:18] ScanObjectNNHardest INFO: 
+Classes	Acc
+bag       : 53.01%
+bin       : 78.39%
+box       : 75.19%
+cabinet   : 80.11%
+chair     : 91.28%
+desk      : 82.00%
+display   : 88.24%
+door      : 94.29%
+shelf     : 88.80%
+table     : 75.19%
+bed       : 79.09%
+pillow    : 84.76%
+sink      : 74.17%
+sofa      : 95.24%
+toilet    : 83.53%
+E@50	OA: 83.55	mAcc: 81.55
+
+[04/01 17:37:18] ScanObjectNNHardest INFO: Epoch 50 LR 0.001732 train_oa 95.29, val_oa 83.55, best val oa 83.55
+[04/01 17:37:18] ScanObjectNNHardest INFO: Found the best model and saved in log/scanobjectnn/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR/checkpoint/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR_ckpt_best.pth
+[04/01 17:38:14] ScanObjectNNHardest INFO: Epoch 51 LR 0.001722 train_oa 95.41, val_oa 83.48, best val oa 83.55
+[04/01 17:39:11] ScanObjectNNHardest INFO: Epoch 52 LR 0.001711 train_oa 95.67, val_oa 80.50, best val oa 83.55
+[04/01 17:40:05] ScanObjectNNHardest INFO: Epoch 53 LR 0.001700 train_oa 95.51, val_oa 82.82, best val oa 83.55
+[04/01 17:41:00] ScanObjectNNHardest INFO: Epoch 54 LR 0.001689 train_oa 95.93, val_oa 83.03, best val oa 83.55
+[04/01 17:41:57] ScanObjectNNHardest INFO: Epoch 55 LR 0.001678 train_oa 96.25, val_oa 81.30, best val oa 83.55
+[04/01 17:42:53] ScanObjectNNHardest INFO: Epoch 56 LR 0.001667 train_oa 96.10, val_oa 82.34, best val oa 83.55
+[04/01 17:43:48] ScanObjectNNHardest INFO: Epoch 57 LR 0.001656 train_oa 96.38, val_oa 82.69, best val oa 83.55
+[04/01 17:44:47] ScanObjectNNHardest INFO: Epoch 58 LR 0.001644 train_oa 96.72, val_oa 82.65, best val oa 83.55
+[04/01 17:45:43] ScanObjectNNHardest INFO: Epoch 59 LR 0.001632 train_oa 96.44, val_oa 82.48, best val oa 83.55
+[04/01 17:46:38] ScanObjectNNHardest INFO: Find a better ckpt @E60
+[04/01 17:46:38] ScanObjectNNHardest INFO: 
+Classes	Acc
+bag       : 61.45%
+bin       : 87.44%
+box       : 54.14%
+cabinet   : 88.17%
+chair     : 95.13%
+desk      : 79.33%
+display   : 86.27%
+door      : 87.62%
+shelf     : 83.40%
+table     : 75.93%
+bed       : 83.64%
+pillow    : 84.76%
+sink      : 75.83%
+sofa      : 87.14%
+toilet    : 92.94%
+E@60	OA: 83.80	mAcc: 81.55
+
+[04/01 17:46:38] ScanObjectNNHardest INFO: Epoch 60 LR 0.001620 train_oa 97.09, val_oa 83.80, best val oa 83.80
+[04/01 17:46:38] ScanObjectNNHardest INFO: Found the best model and saved in log/scanobjectnn/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR/checkpoint/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR_ckpt_best.pth
+[04/01 17:47:37] ScanObjectNNHardest INFO: Epoch 61 LR 0.001608 train_oa 96.50, val_oa 82.89, best val oa 83.80
+[04/01 17:48:35] ScanObjectNNHardest INFO: Epoch 62 LR 0.001596 train_oa 96.48, val_oa 83.31, best val oa 83.80
+[04/01 17:49:34] ScanObjectNNHardest INFO: Epoch 63 LR 0.001584 train_oa 96.39, val_oa 83.59, best val oa 83.80
+[04/01 17:50:29] ScanObjectNNHardest INFO: Epoch 64 LR 0.001572 train_oa 96.74, val_oa 83.21, best val oa 83.80
+[04/01 17:51:24] ScanObjectNNHardest INFO: Find a better ckpt @E65
+[04/01 17:51:24] ScanObjectNNHardest INFO: 
+Classes	Acc
+bag       : 72.29%
+bin       : 87.44%
+box       : 42.11%
+cabinet   : 84.14%
+chair     : 89.49%
+desk      : 73.33%
+display   : 93.63%
+door      : 89.52%
+shelf     : 90.04%
+table     : 81.85%
+bed       : 73.64%
+pillow    : 87.62%
+sink      : 78.33%
+sofa      : 92.86%
+toilet    : 89.41%
+E@65	OA: 83.87	mAcc: 81.71
+
+[04/01 17:51:24] ScanObjectNNHardest INFO: Epoch 65 LR 0.001559 train_oa 97.06, val_oa 83.87, best val oa 83.87
+[04/01 17:51:25] ScanObjectNNHardest INFO: Found the best model and saved in log/scanobjectnn/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR/checkpoint/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR_ckpt_best.pth
+[04/01 17:52:19] ScanObjectNNHardest INFO: Epoch 66 LR 0.001546 train_oa 97.38, val_oa 82.03, best val oa 83.87
+[04/01 17:53:17] ScanObjectNNHardest INFO: Epoch 67 LR 0.001534 train_oa 97.03, val_oa 82.34, best val oa 83.87
+[04/01 17:54:12] ScanObjectNNHardest INFO: Find a better ckpt @E68
+[04/01 17:54:12] ScanObjectNNHardest INFO: 
+Classes	Acc
+bag       : 59.04%
+bin       : 87.44%
+box       : 63.91%
+cabinet   : 88.17%
+chair     : 92.31%
+desk      : 76.67%
+display   : 81.37%
+door      : 94.76%
+shelf     : 81.74%
+table     : 78.15%
+bed       : 80.00%
+pillow    : 92.38%
+sink      : 71.67%
+sofa      : 93.33%
+toilet    : 81.18%
+E@68	OA: 83.97	mAcc: 81.47
+
+[04/01 17:54:12] ScanObjectNNHardest INFO: Epoch 68 LR 0.001521 train_oa 97.20, val_oa 83.97, best val oa 83.97
+[04/01 17:54:12] ScanObjectNNHardest INFO: Found the best model and saved in log/scanobjectnn/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR/checkpoint/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR_ckpt_best.pth
+[04/01 17:55:04] ScanObjectNNHardest INFO: Epoch 69 LR 0.001508 train_oa 97.73, val_oa 82.93, best val oa 83.97
+[04/01 17:56:00] ScanObjectNNHardest INFO: Epoch 70 LR 0.001495 train_oa 97.32, val_oa 83.52, best val oa 83.97
+[04/01 17:57:00] ScanObjectNNHardest INFO: Find a better ckpt @E71
+[04/01 17:57:00] ScanObjectNNHardest INFO: 
+Classes	Acc
+bag       : 60.24%
+bin       : 80.40%
+box       : 60.15%
+cabinet   : 92.20%
+chair     : 93.33%
+desk      : 78.67%
+display   : 90.69%
+door      : 85.71%
+shelf     : 82.99%
+table     : 77.78%
+bed       : 81.82%
+pillow    : 85.71%
+sink      : 78.33%
+sofa      : 90.48%
+toilet    : 84.71%
+E@71	OA: 84.18	mAcc: 81.55
+
+[04/01 17:57:00] ScanObjectNNHardest INFO: Epoch 71 LR 0.001481 train_oa 97.33, val_oa 84.18, best val oa 84.18
+[04/01 17:57:01] ScanObjectNNHardest INFO: Found the best model and saved in log/scanobjectnn/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR/checkpoint/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR_ckpt_best.pth
+[04/01 17:57:58] ScanObjectNNHardest INFO: Epoch 72 LR 0.001468 train_oa 97.37, val_oa 82.93, best val oa 84.18
+[04/01 17:59:00] ScanObjectNNHardest INFO: Find a better ckpt @E73
+[04/01 17:59:00] ScanObjectNNHardest INFO: 
+Classes	Acc
+bag       : 69.88%
+bin       : 83.42%
+box       : 63.91%
+cabinet   : 89.78%
+chair     : 95.64%
+desk      : 82.00%
+display   : 91.67%
+door      : 92.86%
+shelf     : 82.57%
+table     : 72.22%
+bed       : 80.91%
+pillow    : 82.86%
+sink      : 74.17%
+sofa      : 93.81%
+toilet    : 81.18%
+E@73	OA: 84.87	mAcc: 82.46
+
+[04/01 17:59:00] ScanObjectNNHardest INFO: Epoch 73 LR 0.001454 train_oa 97.73, val_oa 84.87, best val oa 84.87
+[04/01 17:59:00] ScanObjectNNHardest INFO: Found the best model and saved in log/scanobjectnn/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR/checkpoint/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR_ckpt_best.pth
+[04/01 17:59:55] ScanObjectNNHardest INFO: Epoch 74 LR 0.001441 train_oa 97.47, val_oa 84.21, best val oa 84.87
+[04/01 18:00:54] ScanObjectNNHardest INFO: Epoch 75 LR 0.001427 train_oa 97.62, val_oa 83.76, best val oa 84.87
+[04/01 18:01:52] ScanObjectNNHardest INFO: Epoch 76 LR 0.001414 train_oa 97.81, val_oa 84.07, best val oa 84.87
+[04/01 18:02:48] ScanObjectNNHardest INFO: Epoch 77 LR 0.001400 train_oa 98.12, val_oa 82.96, best val oa 84.87
+[04/01 18:03:42] ScanObjectNNHardest INFO: Epoch 78 LR 0.001386 train_oa 97.74, val_oa 84.25, best val oa 84.87
+[04/01 18:04:36] ScanObjectNNHardest INFO: Epoch 79 LR 0.001372 train_oa 97.66, val_oa 84.21, best val oa 84.87
+[04/01 18:05:32] ScanObjectNNHardest INFO: Epoch 80 LR 0.001358 train_oa 98.02, val_oa 82.69, best val oa 84.87
+[04/01 18:06:26] ScanObjectNNHardest INFO: Epoch 81 LR 0.001344 train_oa 97.79, val_oa 84.35, best val oa 84.87
+[04/01 18:07:23] ScanObjectNNHardest INFO: Epoch 82 LR 0.001329 train_oa 98.03, val_oa 84.70, best val oa 84.87
+[04/01 18:08:18] ScanObjectNNHardest INFO: Epoch 83 LR 0.001315 train_oa 98.16, val_oa 83.76, best val oa 84.87
+[04/01 18:09:16] ScanObjectNNHardest INFO: Epoch 84 LR 0.001301 train_oa 98.10, val_oa 84.00, best val oa 84.87
+[04/01 18:10:12] ScanObjectNNHardest INFO: Epoch 85 LR 0.001286 train_oa 97.81, val_oa 84.04, best val oa 84.87
+[04/01 18:11:11] ScanObjectNNHardest INFO: Epoch 86 LR 0.001272 train_oa 98.15, val_oa 84.70, best val oa 84.87
+[04/01 18:12:08] ScanObjectNNHardest INFO: Epoch 87 LR 0.001257 train_oa 98.14, val_oa 84.28, best val oa 84.87
+[04/01 18:13:06] ScanObjectNNHardest INFO: Epoch 88 LR 0.001243 train_oa 98.28, val_oa 84.42, best val oa 84.87
+[04/01 18:14:05] ScanObjectNNHardest INFO: Epoch 89 LR 0.001228 train_oa 98.47, val_oa 84.77, best val oa 84.87
+[04/01 18:15:02] ScanObjectNNHardest INFO: Epoch 90 LR 0.001213 train_oa 98.22, val_oa 82.27, best val oa 84.87
+[04/01 18:15:56] ScanObjectNNHardest INFO: Find a better ckpt @E91
+[04/01 18:15:56] ScanObjectNNHardest INFO: 
+Classes	Acc
+bag       : 60.24%
+bin       : 89.95%
+box       : 59.40%
+cabinet   : 91.67%
+chair     : 93.85%
+desk      : 80.67%
+display   : 86.27%
+door      : 91.43%
+shelf     : 86.31%
+table     : 72.22%
+bed       : 87.27%
+pillow    : 83.81%
+sink      : 76.67%
+sofa      : 93.81%
+toilet    : 87.06%
+E@91	OA: 85.15	mAcc: 82.71
+
+[04/01 18:15:56] ScanObjectNNHardest INFO: Epoch 91 LR 0.001199 train_oa 98.41, val_oa 85.15, best val oa 85.15
+[04/01 18:15:56] ScanObjectNNHardest INFO: Found the best model and saved in log/scanobjectnn/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR/checkpoint/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR_ckpt_best.pth
+[04/01 18:16:56] ScanObjectNNHardest INFO: Epoch 92 LR 0.001184 train_oa 98.61, val_oa 84.84, best val oa 85.15
+[04/01 18:17:56] ScanObjectNNHardest INFO: Epoch 93 LR 0.001169 train_oa 98.57, val_oa 82.82, best val oa 85.15
+[04/01 18:18:54] ScanObjectNNHardest INFO: Epoch 94 LR 0.001154 train_oa 98.57, val_oa 84.32, best val oa 85.15
+[04/01 18:19:50] ScanObjectNNHardest INFO: Epoch 95 LR 0.001139 train_oa 98.60, val_oa 83.41, best val oa 85.15
+[04/01 18:20:45] ScanObjectNNHardest INFO: Epoch 96 LR 0.001125 train_oa 98.31, val_oa 83.80, best val oa 85.15
+[04/01 18:21:41] ScanObjectNNHardest INFO: Epoch 97 LR 0.001110 train_oa 98.65, val_oa 84.49, best val oa 85.15
+[04/01 18:22:35] ScanObjectNNHardest INFO: Epoch 98 LR 0.001095 train_oa 98.60, val_oa 84.46, best val oa 85.15
+[04/01 18:23:28] ScanObjectNNHardest INFO: Epoch 99 LR 0.001080 train_oa 98.78, val_oa 84.14, best val oa 85.15
+[04/01 18:24:24] ScanObjectNNHardest INFO: Epoch 100 LR 0.001065 train_oa 98.95, val_oa 83.93, best val oa 85.15
+[04/01 18:25:20] ScanObjectNNHardest INFO: Epoch 101 LR 0.001050 train_oa 98.87, val_oa 84.32, best val oa 85.15
+[04/01 18:26:12] ScanObjectNNHardest INFO: Epoch 102 LR 0.001035 train_oa 98.92, val_oa 82.51, best val oa 85.15
+[04/01 18:27:14] ScanObjectNNHardest INFO: Epoch 103 LR 0.001020 train_oa 98.72, val_oa 84.00, best val oa 85.15
+[04/01 18:28:10] ScanObjectNNHardest INFO: Epoch 104 LR 0.001005 train_oa 99.13, val_oa 85.05, best val oa 85.15
+[04/01 18:29:04] ScanObjectNNHardest INFO: Find a better ckpt @E105
+[04/01 18:29:04] ScanObjectNNHardest INFO: 
+Classes	Acc
+bag       : 60.24%
+bin       : 86.93%
+box       : 76.69%
+cabinet   : 87.37%
+chair     : 95.90%
+desk      : 82.00%
+display   : 90.69%
+door      : 96.67%
+shelf     : 84.65%
+table     : 69.63%
+bed       : 80.00%
+pillow    : 89.52%
+sink      : 77.50%
+sofa      : 93.33%
+toilet    : 80.00%
+E@105	OA: 85.57	mAcc: 83.41
+
+[04/01 18:29:04] ScanObjectNNHardest INFO: Epoch 105 LR 0.000990 train_oa 99.02, val_oa 85.57, best val oa 85.57
+[04/01 18:29:05] ScanObjectNNHardest INFO: Found the best model and saved in log/scanobjectnn/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR/checkpoint/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR_ckpt_best.pth
+[04/01 18:30:01] ScanObjectNNHardest INFO: Epoch 106 LR 0.000975 train_oa 98.76, val_oa 83.45, best val oa 85.57
+[04/01 18:30:56] ScanObjectNNHardest INFO: Epoch 107 LR 0.000961 train_oa 98.53, val_oa 85.01, best val oa 85.57
+[04/01 18:31:50] ScanObjectNNHardest INFO: Epoch 108 LR 0.000946 train_oa 99.17, val_oa 85.46, best val oa 85.57
+[04/01 18:32:44] ScanObjectNNHardest INFO: Epoch 109 LR 0.000931 train_oa 99.13, val_oa 84.07, best val oa 85.57
+[04/01 18:33:43] ScanObjectNNHardest INFO: Epoch 110 LR 0.000916 train_oa 99.32, val_oa 84.98, best val oa 85.57
+[04/01 18:34:37] ScanObjectNNHardest INFO: Epoch 111 LR 0.000901 train_oa 99.19, val_oa 83.83, best val oa 85.57
+[04/01 18:35:33] ScanObjectNNHardest INFO: Epoch 112 LR 0.000887 train_oa 99.08, val_oa 83.48, best val oa 85.57
+[04/01 18:36:29] ScanObjectNNHardest INFO: Epoch 113 LR 0.000872 train_oa 99.18, val_oa 83.55, best val oa 85.57
+[04/01 18:37:29] ScanObjectNNHardest INFO: Epoch 114 LR 0.000857 train_oa 99.37, val_oa 83.87, best val oa 85.57
+[04/01 18:38:23] ScanObjectNNHardest INFO: Epoch 115 LR 0.000843 train_oa 98.91, val_oa 84.14, best val oa 85.57
+[04/01 18:39:20] ScanObjectNNHardest INFO: Epoch 116 LR 0.000828 train_oa 99.25, val_oa 84.21, best val oa 85.57
+[04/01 18:40:13] ScanObjectNNHardest INFO: Epoch 117 LR 0.000814 train_oa 99.30, val_oa 84.04, best val oa 85.57
+[04/01 18:41:11] ScanObjectNNHardest INFO: Epoch 118 LR 0.000799 train_oa 99.32, val_oa 84.80, best val oa 85.57
+[04/01 18:42:06] ScanObjectNNHardest INFO: Epoch 119 LR 0.000785 train_oa 99.46, val_oa 84.66, best val oa 85.57
+[04/01 18:43:01] ScanObjectNNHardest INFO: Epoch 120 LR 0.000771 train_oa 99.25, val_oa 84.66, best val oa 85.57
+[04/01 18:43:56] ScanObjectNNHardest INFO: Epoch 121 LR 0.000756 train_oa 99.28, val_oa 84.52, best val oa 85.57
+[04/01 18:44:53] ScanObjectNNHardest INFO: Epoch 122 LR 0.000742 train_oa 99.37, val_oa 85.46, best val oa 85.57
+[04/01 18:45:47] ScanObjectNNHardest INFO: Epoch 123 LR 0.000728 train_oa 99.36, val_oa 84.59, best val oa 85.57
+[04/01 18:46:47] ScanObjectNNHardest INFO: Find a better ckpt @E124
+[04/01 18:46:47] ScanObjectNNHardest INFO: 
+Classes	Acc
+bag       : 71.08%
+bin       : 82.91%
+box       : 66.92%
+cabinet   : 87.37%
+chair     : 95.13%
+desk      : 84.67%
+display   : 91.67%
+door      : 94.29%
+shelf     : 88.38%
+table     : 69.26%
+bed       : 79.09%
+pillow    : 88.57%
+sink      : 76.67%
+sofa      : 95.71%
+toilet    : 87.06%
+E@124	OA: 85.63	mAcc: 83.92
+
+[04/01 18:46:47] ScanObjectNNHardest INFO: Epoch 124 LR 0.000714 train_oa 99.61, val_oa 85.63, best val oa 85.63
+[04/01 18:46:48] ScanObjectNNHardest INFO: Found the best model and saved in log/scanobjectnn/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR/checkpoint/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR_ckpt_best.pth
+[04/01 18:47:47] ScanObjectNNHardest INFO: Epoch 125 LR 0.000700 train_oa 99.59, val_oa 84.07, best val oa 85.63
+[04/01 18:48:40] ScanObjectNNHardest INFO: Epoch 126 LR 0.000686 train_oa 99.56, val_oa 85.36, best val oa 85.63
+[04/01 18:49:40] ScanObjectNNHardest INFO: Epoch 127 LR 0.000673 train_oa 99.42, val_oa 85.43, best val oa 85.63
+[04/01 18:50:44] ScanObjectNNHardest INFO: Find a better ckpt @E128
+[04/01 18:50:44] ScanObjectNNHardest INFO: 
+Classes	Acc
+bag       : 55.42%
+bin       : 82.91%
+box       : 70.68%
+cabinet   : 85.22%
+chair     : 96.41%
+desk      : 68.67%
+display   : 93.14%
+door      : 95.71%
+shelf     : 92.12%
+table     : 78.15%
+bed       : 80.00%
+pillow    : 88.57%
+sink      : 74.17%
+sofa      : 95.24%
+toilet    : 87.06%
+E@128	OA: 85.67	mAcc: 82.90
+
+[04/01 18:50:44] ScanObjectNNHardest INFO: Epoch 128 LR 0.000659 train_oa 99.51, val_oa 85.67, best val oa 85.67
+[04/01 18:50:44] ScanObjectNNHardest INFO: Found the best model and saved in log/scanobjectnn/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR/checkpoint/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR_ckpt_best.pth
+[04/01 18:51:42] ScanObjectNNHardest INFO: Epoch 129 LR 0.000646 train_oa 99.55, val_oa 84.91, best val oa 85.67
+[04/01 18:52:39] ScanObjectNNHardest INFO: Epoch 130 LR 0.000632 train_oa 99.59, val_oa 85.29, best val oa 85.67
+[04/01 18:53:36] ScanObjectNNHardest INFO: Epoch 131 LR 0.000619 train_oa 99.56, val_oa 84.66, best val oa 85.67
+[04/01 18:54:34] ScanObjectNNHardest INFO: Epoch 132 LR 0.000605 train_oa 99.53, val_oa 84.91, best val oa 85.67
+[04/01 18:55:32] ScanObjectNNHardest INFO: Find a better ckpt @E133
+[04/01 18:55:32] ScanObjectNNHardest INFO: 
+Classes	Acc
+bag       : 63.86%
+bin       : 88.44%
+box       : 63.91%
+cabinet   : 88.71%
+chair     : 95.38%
+desk      : 75.33%
+display   : 90.20%
+door      : 92.86%
+shelf     : 88.80%
+table     : 77.41%
+bed       : 80.00%
+pillow    : 86.67%
+sink      : 80.00%
+sofa      : 92.86%
+toilet    : 82.35%
+E@133	OA: 85.74	mAcc: 83.12
+
+[04/01 18:55:32] ScanObjectNNHardest INFO: Epoch 133 LR 0.000592 train_oa 99.63, val_oa 85.74, best val oa 85.74
+[04/01 18:55:32] ScanObjectNNHardest INFO: Found the best model and saved in log/scanobjectnn/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR/checkpoint/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR_ckpt_best.pth
+[04/01 18:56:29] ScanObjectNNHardest INFO: Epoch 134 LR 0.000579 train_oa 99.53, val_oa 85.32, best val oa 85.74
+[04/01 18:57:23] ScanObjectNNHardest INFO: Epoch 135 LR 0.000566 train_oa 99.52, val_oa 85.11, best val oa 85.74
+[04/01 18:58:18] ScanObjectNNHardest INFO: Epoch 136 LR 0.000554 train_oa 99.62, val_oa 85.60, best val oa 85.74
+[04/01 18:59:19] ScanObjectNNHardest INFO: Epoch 137 LR 0.000541 train_oa 99.69, val_oa 84.80, best val oa 85.74
+[04/01 19:00:19] ScanObjectNNHardest INFO: Epoch 138 LR 0.000528 train_oa 99.74, val_oa 85.74, best val oa 85.74
+[04/01 19:01:16] ScanObjectNNHardest INFO: Epoch 139 LR 0.000516 train_oa 99.69, val_oa 85.50, best val oa 85.74
+[04/01 19:02:13] ScanObjectNNHardest INFO: Epoch 140 LR 0.000504 train_oa 99.75, val_oa 84.77, best val oa 85.74
+[04/01 19:03:13] ScanObjectNNHardest INFO: Epoch 141 LR 0.000492 train_oa 99.72, val_oa 84.98, best val oa 85.74
+[04/01 19:04:06] ScanObjectNNHardest INFO: Epoch 142 LR 0.000480 train_oa 99.72, val_oa 85.36, best val oa 85.74
+[04/01 19:05:04] ScanObjectNNHardest INFO: Epoch 143 LR 0.000468 train_oa 99.70, val_oa 85.70, best val oa 85.74
+[04/01 19:06:00] ScanObjectNNHardest INFO: Find a better ckpt @E144
+[04/01 19:06:00] ScanObjectNNHardest INFO: 
+Classes	Acc
+bag       : 66.27%
+bin       : 85.93%
+box       : 69.17%
+cabinet   : 90.86%
+chair     : 95.90%
+desk      : 85.33%
+display   : 90.20%
+door      : 90.00%
+shelf     : 87.97%
+table     : 72.96%
+bed       : 83.64%
+pillow    : 91.43%
+sink      : 77.50%
+sofa      : 93.81%
+toilet    : 88.24%
+E@144	OA: 86.50	mAcc: 84.61
+
+[04/01 19:06:00] ScanObjectNNHardest INFO: Epoch 144 LR 0.000456 train_oa 99.82, val_oa 86.50, best val oa 86.50
+[04/01 19:06:01] ScanObjectNNHardest INFO: Found the best model and saved in log/scanobjectnn/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR/checkpoint/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR_ckpt_best.pth
+[04/01 19:06:58] ScanObjectNNHardest INFO: Epoch 145 LR 0.000444 train_oa 99.76, val_oa 85.50, best val oa 86.50
+[04/01 19:07:57] ScanObjectNNHardest INFO: Epoch 146 LR 0.000433 train_oa 99.75, val_oa 85.74, best val oa 86.50
+[04/01 19:08:55] ScanObjectNNHardest INFO: Epoch 147 LR 0.000422 train_oa 99.81, val_oa 86.26, best val oa 86.50
+[04/01 19:09:55] ScanObjectNNHardest INFO: Find a better ckpt @E148
+[04/01 19:09:55] ScanObjectNNHardest INFO: 
+Classes	Acc
+bag       : 66.27%
+bin       : 85.93%
+box       : 63.16%
+cabinet   : 92.74%
+chair     : 95.90%
+desk      : 76.00%
+display   : 91.18%
+door      : 91.90%
+shelf     : 90.04%
+table     : 78.52%
+bed       : 81.82%
+pillow    : 88.57%
+sink      : 70.00%
+sofa      : 94.76%
+toilet    : 90.59%
+E@148	OA: 86.54	mAcc: 83.82
+
+[04/01 19:09:55] ScanObjectNNHardest INFO: Epoch 148 LR 0.000411 train_oa 99.70, val_oa 86.54, best val oa 86.54
+[04/01 19:09:55] ScanObjectNNHardest INFO: Found the best model and saved in log/scanobjectnn/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR/checkpoint/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR_ckpt_best.pth
+[04/01 19:10:53] ScanObjectNNHardest INFO: Find a better ckpt @E149
+[04/01 19:10:53] ScanObjectNNHardest INFO: 
+Classes	Acc
+bag       : 69.88%
+bin       : 87.94%
+box       : 66.17%
+cabinet   : 88.17%
+chair     : 95.38%
+desk      : 76.67%
+display   : 94.12%
+door      : 96.67%
+shelf     : 88.38%
+table     : 76.30%
+bed       : 83.64%
+pillow    : 90.48%
+sink      : 75.83%
+sofa      : 94.29%
+toilet    : 90.59%
+E@149	OA: 86.85	mAcc: 84.97
+
+[04/01 19:10:53] ScanObjectNNHardest INFO: Epoch 149 LR 0.000400 train_oa 99.82, val_oa 86.85, best val oa 86.85
+[04/01 19:10:54] ScanObjectNNHardest INFO: Found the best model and saved in log/scanobjectnn/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR/checkpoint/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR_ckpt_best.pth
+[04/01 19:11:53] ScanObjectNNHardest INFO: Epoch 150 LR 0.000389 train_oa 99.79, val_oa 85.91, best val oa 86.85
+[04/01 19:12:53] ScanObjectNNHardest INFO: Epoch 151 LR 0.000378 train_oa 99.86, val_oa 85.88, best val oa 86.85
+[04/01 19:13:51] ScanObjectNNHardest INFO: Epoch 152 LR 0.000368 train_oa 99.85, val_oa 86.33, best val oa 86.85
+[04/01 19:14:46] ScanObjectNNHardest INFO: Epoch 153 LR 0.000357 train_oa 99.85, val_oa 86.61, best val oa 86.85
+[04/01 19:15:42] ScanObjectNNHardest INFO: Epoch 154 LR 0.000347 train_oa 99.90, val_oa 86.29, best val oa 86.85
+[04/01 19:16:38] ScanObjectNNHardest INFO: Epoch 155 LR 0.000337 train_oa 99.87, val_oa 86.26, best val oa 86.85
+[04/01 19:17:34] ScanObjectNNHardest INFO: Epoch 156 LR 0.000328 train_oa 99.89, val_oa 85.46, best val oa 86.85
+[04/01 19:18:33] ScanObjectNNHardest INFO: Epoch 157 LR 0.000318 train_oa 99.90, val_oa 85.81, best val oa 86.85
+[04/01 19:19:29] ScanObjectNNHardest INFO: Epoch 158 LR 0.000309 train_oa 99.88, val_oa 85.77, best val oa 86.85
+[04/01 19:20:27] ScanObjectNNHardest INFO: Epoch 159 LR 0.000299 train_oa 99.95, val_oa 86.33, best val oa 86.85
+[04/01 19:21:27] ScanObjectNNHardest INFO: Epoch 160 LR 0.000290 train_oa 99.87, val_oa 85.88, best val oa 86.85
+[04/01 19:22:26] ScanObjectNNHardest INFO: Epoch 161 LR 0.000281 train_oa 99.89, val_oa 86.26, best val oa 86.85
+[04/01 19:23:23] ScanObjectNNHardest INFO: Epoch 162 LR 0.000273 train_oa 99.89, val_oa 85.95, best val oa 86.85
+[04/01 19:24:21] ScanObjectNNHardest INFO: Epoch 163 LR 0.000264 train_oa 99.82, val_oa 86.26, best val oa 86.85
+[04/01 19:25:18] ScanObjectNNHardest INFO: Epoch 164 LR 0.000256 train_oa 99.94, val_oa 85.81, best val oa 86.85
+[04/01 19:26:16] ScanObjectNNHardest INFO: Epoch 165 LR 0.000248 train_oa 99.96, val_oa 85.81, best val oa 86.85
+[04/01 19:27:12] ScanObjectNNHardest INFO: Epoch 166 LR 0.000240 train_oa 99.97, val_oa 86.40, best val oa 86.85
+[04/01 19:28:11] ScanObjectNNHardest INFO: Epoch 167 LR 0.000232 train_oa 99.96, val_oa 86.78, best val oa 86.85
+[04/01 19:29:11] ScanObjectNNHardest INFO: Epoch 168 LR 0.000225 train_oa 99.93, val_oa 86.22, best val oa 86.85
+[04/01 19:30:10] ScanObjectNNHardest INFO: Epoch 169 LR 0.000218 train_oa 99.96, val_oa 86.47, best val oa 86.85
+[04/01 19:31:08] ScanObjectNNHardest INFO: Epoch 170 LR 0.000210 train_oa 99.97, val_oa 85.95, best val oa 86.85
+[04/01 19:32:07] ScanObjectNNHardest INFO: Find a better ckpt @E171
+[04/01 19:32:07] ScanObjectNNHardest INFO: 
+Classes	Acc
+bag       : 66.27%
+bin       : 89.45%
+box       : 61.65%
+cabinet   : 91.40%
+chair     : 96.41%
+desk      : 82.00%
+display   : 90.20%
+door      : 91.90%
+shelf     : 91.70%
+table     : 74.44%
+bed       : 84.55%
+pillow    : 90.48%
+sink      : 70.83%
+sofa      : 94.29%
+toilet    : 94.12%
+E@171	OA: 86.88	mAcc: 84.65
+
+[04/01 19:32:07] ScanObjectNNHardest INFO: Epoch 171 LR 0.000204 train_oa 99.96, val_oa 86.88, best val oa 86.88
+[04/01 19:32:07] ScanObjectNNHardest INFO: Found the best model and saved in log/scanobjectnn/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR/checkpoint/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR_ckpt_best.pth
+[04/01 19:33:00] ScanObjectNNHardest INFO: Epoch 172 LR 0.000197 train_oa 99.98, val_oa 86.68, best val oa 86.88
+[04/01 19:33:56] ScanObjectNNHardest INFO: Epoch 173 LR 0.000190 train_oa 99.94, val_oa 86.19, best val oa 86.88
+[04/01 19:34:51] ScanObjectNNHardest INFO: Epoch 174 LR 0.000184 train_oa 99.95, val_oa 86.50, best val oa 86.88
+[04/01 19:35:47] ScanObjectNNHardest INFO: Find a better ckpt @E175
+[04/01 19:35:47] ScanObjectNNHardest INFO: 
+Classes	Acc
+bag       : 62.65%
+bin       : 88.94%
+box       : 64.66%
+cabinet   : 90.32%
+chair     : 96.92%
+desk      : 84.67%
+display   : 91.18%
+door      : 94.29%
+shelf     : 88.80%
+table     : 72.59%
+bed       : 82.73%
+pillow    : 90.48%
+sink      : 78.33%
+sofa      : 95.24%
+toilet    : 91.76%
+E@175	OA: 87.02	mAcc: 84.90
+
+[04/01 19:35:47] ScanObjectNNHardest INFO: Epoch 175 LR 0.000178 train_oa 99.94, val_oa 87.02, best val oa 87.02
+[04/01 19:35:47] ScanObjectNNHardest INFO: Found the best model and saved in log/scanobjectnn/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR/checkpoint/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR_ckpt_best.pth
+[04/01 19:36:42] ScanObjectNNHardest INFO: Epoch 176 LR 0.000172 train_oa 99.96, val_oa 86.85, best val oa 87.02
+[04/01 19:37:38] ScanObjectNNHardest INFO: Epoch 177 LR 0.000167 train_oa 99.95, val_oa 86.75, best val oa 87.02
+[04/01 19:38:33] ScanObjectNNHardest INFO: Epoch 178 LR 0.000161 train_oa 99.96, val_oa 86.88, best val oa 87.02
+[04/01 19:39:28] ScanObjectNNHardest INFO: Epoch 179 LR 0.000156 train_oa 99.96, val_oa 86.33, best val oa 87.02
+[04/01 19:40:24] ScanObjectNNHardest INFO: Epoch 180 LR 0.000151 train_oa 99.96, val_oa 86.05, best val oa 87.02
+[04/01 19:41:23] ScanObjectNNHardest INFO: Epoch 181 LR 0.000146 train_oa 99.98, val_oa 86.95, best val oa 87.02
+[04/01 19:42:21] ScanObjectNNHardest INFO: Epoch 182 LR 0.000142 train_oa 99.99, val_oa 86.43, best val oa 87.02
+[04/01 19:43:20] ScanObjectNNHardest INFO: Epoch 183 LR 0.000138 train_oa 99.93, val_oa 85.98, best val oa 87.02
+[04/01 19:44:18] ScanObjectNNHardest INFO: Epoch 184 LR 0.000134 train_oa 99.96, val_oa 86.54, best val oa 87.02
+[04/01 19:45:14] ScanObjectNNHardest INFO: Epoch 185 LR 0.000130 train_oa 99.99, val_oa 86.88, best val oa 87.02
+[04/01 19:46:10] ScanObjectNNHardest INFO: Epoch 186 LR 0.000126 train_oa 99.97, val_oa 86.92, best val oa 87.02
+[04/01 19:47:06] ScanObjectNNHardest INFO: Epoch 187 LR 0.000123 train_oa 99.99, val_oa 86.43, best val oa 87.02
+[04/01 19:48:03] ScanObjectNNHardest INFO: Epoch 188 LR 0.000120 train_oa 99.98, val_oa 86.43, best val oa 87.02
+[04/01 19:48:58] ScanObjectNNHardest INFO: Epoch 189 LR 0.000117 train_oa 99.94, val_oa 86.19, best val oa 87.02
+[04/01 19:49:54] ScanObjectNNHardest INFO: Epoch 190 LR 0.000114 train_oa 99.98, val_oa 86.50, best val oa 87.02
+[04/01 19:50:48] ScanObjectNNHardest INFO: Epoch 191 LR 0.000112 train_oa 99.97, val_oa 85.81, best val oa 87.02
+[04/01 19:51:47] ScanObjectNNHardest INFO: Epoch 192 LR 0.000109 train_oa 99.98, val_oa 86.61, best val oa 87.02
+[04/01 19:52:46] ScanObjectNNHardest INFO: Epoch 193 LR 0.000107 train_oa 99.99, val_oa 86.78, best val oa 87.02
+[04/01 19:53:42] ScanObjectNNHardest INFO: Epoch 194 LR 0.000106 train_oa 100.00, val_oa 86.85, best val oa 87.02
+[04/01 19:54:38] ScanObjectNNHardest INFO: Epoch 195 LR 0.000104 train_oa 99.98, val_oa 86.16, best val oa 87.02
+[04/01 19:55:35] ScanObjectNNHardest INFO: Epoch 196 LR 0.000103 train_oa 99.99, val_oa 86.57, best val oa 87.02
+[04/01 19:56:33] ScanObjectNNHardest INFO: Epoch 197 LR 0.000102 train_oa 100.00, val_oa 86.95, best val oa 87.02
+[04/01 19:57:33] ScanObjectNNHardest INFO: Epoch 198 LR 0.000101 train_oa 99.99, val_oa 86.47, best val oa 87.02
+[04/01 19:58:27] ScanObjectNNHardest INFO: Find a better ckpt @E199
+[04/01 19:58:27] ScanObjectNNHardest INFO: 
+Classes	Acc
+bag       : 65.06%
+bin       : 89.95%
+box       : 68.42%
+cabinet   : 89.25%
+chair     : 96.67%
+desk      : 85.33%
+display   : 89.22%
+door      : 95.24%
+shelf     : 88.80%
+table     : 73.70%
+bed       : 84.55%
+pillow    : 89.52%
+sink      : 77.50%
+sofa      : 94.29%
+toilet    : 88.24%
+E@199	OA: 87.06	mAcc: 85.05
+
+[04/01 19:58:27] ScanObjectNNHardest INFO: Epoch 199 LR 0.000100 train_oa 99.98, val_oa 87.06, best val oa 87.06
+[04/01 19:58:27] ScanObjectNNHardest INFO: Found the best model and saved in log/scanobjectnn/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR/checkpoint/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR_ckpt_best.pth
+[04/01 19:59:23] ScanObjectNNHardest INFO: Epoch 200 LR 0.000100 train_oa 99.99, val_oa 86.88, best val oa 87.06
+[04/01 20:00:21] ScanObjectNNHardest INFO: Epoch 201 LR 0.000100 train_oa 100.00, val_oa 86.85, best val oa 87.06
+[04/01 20:01:18] ScanObjectNNHardest INFO: Epoch 202 LR 0.000100 train_oa 99.98, val_oa 86.88, best val oa 87.06
+[04/01 20:02:16] ScanObjectNNHardest INFO: Epoch 203 LR 0.000100 train_oa 99.99, val_oa 86.50, best val oa 87.06
+[04/01 20:03:10] ScanObjectNNHardest INFO: Epoch 204 LR 0.000100 train_oa 100.00, val_oa 86.54, best val oa 87.06
+[04/01 20:04:09] ScanObjectNNHardest INFO: Epoch 205 LR 0.000100 train_oa 99.97, val_oa 86.92, best val oa 87.06
+[04/01 20:05:08] ScanObjectNNHardest INFO: Epoch 206 LR 0.000100 train_oa 99.96, val_oa 86.54, best val oa 87.06
+[04/01 20:06:07] ScanObjectNNHardest INFO: Epoch 207 LR 0.000100 train_oa 99.97, val_oa 86.40, best val oa 87.06
+[04/01 20:07:05] ScanObjectNNHardest INFO: Find a better ckpt @E208
+[04/01 20:07:05] ScanObjectNNHardest INFO: 
+Classes	Acc
+bag       : 74.70%
+bin       : 86.93%
+box       : 60.90%
+cabinet   : 89.52%
+chair     : 95.90%
+desk      : 84.67%
+display   : 90.20%
+door      : 94.29%
+shelf     : 88.38%
+table     : 75.19%
+bed       : 86.36%
+pillow    : 90.48%
+sink      : 81.67%
+sofa      : 95.71%
+toilet    : 88.24%
+E@208	OA: 87.16	mAcc: 85.54
+
+[04/01 20:07:05] ScanObjectNNHardest INFO: Epoch 208 LR 0.000100 train_oa 100.00, val_oa 87.16, best val oa 87.16
+[04/01 20:07:05] ScanObjectNNHardest INFO: Found the best model and saved in log/scanobjectnn/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR/checkpoint/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR_ckpt_best.pth
+[04/01 20:08:02] ScanObjectNNHardest INFO: Epoch 209 LR 0.000100 train_oa 99.97, val_oa 86.81, best val oa 87.16
+[04/01 20:09:01] ScanObjectNNHardest INFO: Epoch 210 LR 0.000100 train_oa 100.00, val_oa 86.78, best val oa 87.16
+[04/01 20:09:55] ScanObjectNNHardest INFO: Epoch 211 LR 0.000100 train_oa 99.99, val_oa 86.57, best val oa 87.16
+[04/01 20:10:51] ScanObjectNNHardest INFO: Epoch 212 LR 0.000100 train_oa 99.99, val_oa 86.40, best val oa 87.16
+[04/01 20:11:49] ScanObjectNNHardest INFO: Epoch 213 LR 0.000100 train_oa 99.98, val_oa 86.71, best val oa 87.16
+[04/01 20:12:47] ScanObjectNNHardest INFO: Epoch 214 LR 0.000100 train_oa 99.99, val_oa 86.78, best val oa 87.16
+[04/01 20:13:44] ScanObjectNNHardest INFO: Epoch 215 LR 0.000100 train_oa 99.98, val_oa 86.61, best val oa 87.16
+[04/01 20:14:40] ScanObjectNNHardest INFO: Epoch 216 LR 0.000100 train_oa 99.97, val_oa 86.71, best val oa 87.16
+[04/01 20:15:44] ScanObjectNNHardest INFO: Epoch 217 LR 0.000100 train_oa 99.99, val_oa 86.02, best val oa 87.16
+[04/01 20:16:41] ScanObjectNNHardest INFO: Epoch 218 LR 0.000100 train_oa 99.99, val_oa 86.16, best val oa 87.16
+[04/01 20:17:37] ScanObjectNNHardest INFO: Epoch 219 LR 0.000100 train_oa 99.98, val_oa 86.22, best val oa 87.16
+[04/01 20:18:30] ScanObjectNNHardest INFO: Epoch 220 LR 0.000100 train_oa 99.99, val_oa 86.02, best val oa 87.16
+[04/01 20:19:26] ScanObjectNNHardest INFO: Epoch 221 LR 0.000100 train_oa 99.98, val_oa 85.57, best val oa 87.16
+[04/01 20:20:25] ScanObjectNNHardest INFO: Epoch 222 LR 0.000100 train_oa 99.96, val_oa 86.22, best val oa 87.16
+[04/01 20:21:26] ScanObjectNNHardest INFO: Epoch 223 LR 0.000100 train_oa 99.99, val_oa 86.29, best val oa 87.16
+[04/01 20:22:23] ScanObjectNNHardest INFO: Epoch 224 LR 0.000100 train_oa 99.98, val_oa 86.47, best val oa 87.16
+[04/01 20:23:19] ScanObjectNNHardest INFO: Epoch 225 LR 0.000100 train_oa 99.98, val_oa 86.47, best val oa 87.16
+[04/01 20:24:15] ScanObjectNNHardest INFO: Epoch 226 LR 0.000100 train_oa 100.00, val_oa 87.09, best val oa 87.16
+[04/01 20:25:16] ScanObjectNNHardest INFO: Epoch 227 LR 0.000100 train_oa 99.97, val_oa 86.43, best val oa 87.16
+[04/01 20:26:16] ScanObjectNNHardest INFO: Epoch 228 LR 0.000100 train_oa 99.99, val_oa 86.61, best val oa 87.16
+[04/01 20:27:14] ScanObjectNNHardest INFO: Epoch 229 LR 0.000100 train_oa 99.99, val_oa 86.05, best val oa 87.16
+[04/01 20:28:12] ScanObjectNNHardest INFO: Epoch 230 LR 0.000100 train_oa 99.99, val_oa 86.64, best val oa 87.16
+[04/01 20:29:09] ScanObjectNNHardest INFO: Epoch 231 LR 0.000100 train_oa 99.98, val_oa 86.02, best val oa 87.16
+[04/01 20:30:07] ScanObjectNNHardest INFO: Epoch 232 LR 0.000100 train_oa 99.99, val_oa 86.29, best val oa 87.16
+[04/01 20:31:09] ScanObjectNNHardest INFO: Epoch 233 LR 0.000100 train_oa 99.99, val_oa 86.43, best val oa 87.16
+[04/01 20:32:10] ScanObjectNNHardest INFO: Epoch 234 LR 0.000100 train_oa 99.96, val_oa 86.71, best val oa 87.16
+[04/01 20:33:08] ScanObjectNNHardest INFO: Epoch 235 LR 0.000100 train_oa 99.99, val_oa 87.16, best val oa 87.16
+[04/01 20:34:06] ScanObjectNNHardest INFO: Epoch 236 LR 0.000100 train_oa 99.98, val_oa 86.71, best val oa 87.16
+[04/01 20:35:01] ScanObjectNNHardest INFO: Epoch 237 LR 0.000100 train_oa 99.99, val_oa 86.33, best val oa 87.16
+[04/01 20:35:58] ScanObjectNNHardest INFO: Epoch 238 LR 0.000100 train_oa 99.98, val_oa 86.64, best val oa 87.16
+[04/01 20:36:52] ScanObjectNNHardest INFO: Epoch 239 LR 0.000100 train_oa 99.97, val_oa 87.13, best val oa 87.16
+[04/01 20:37:48] ScanObjectNNHardest INFO: Find a better ckpt @E240
+[04/01 20:37:48] ScanObjectNNHardest INFO: 
+Classes	Acc
+bag       : 67.47%
+bin       : 88.44%
+box       : 70.68%
+cabinet   : 91.40%
+chair     : 96.15%
+desk      : 86.00%
+display   : 90.69%
+door      : 94.29%
+shelf     : 87.14%
+table     : 73.33%
+bed       : 80.91%
+pillow    : 90.48%
+sink      : 79.17%
+sofa      : 94.29%
+toilet    : 90.59%
+E@240	OA: 87.27	mAcc: 85.40
+
+[04/01 20:37:48] ScanObjectNNHardest INFO: Epoch 240 LR 0.000100 train_oa 100.00, val_oa 87.27, best val oa 87.27
+[04/01 20:37:48] ScanObjectNNHardest INFO: Found the best model and saved in log/scanobjectnn/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR/checkpoint/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR_ckpt_best.pth
+[04/01 20:38:44] ScanObjectNNHardest INFO: Epoch 241 LR 0.000100 train_oa 99.98, val_oa 87.06, best val oa 87.27
+[04/01 20:39:39] ScanObjectNNHardest INFO: Epoch 242 LR 0.000100 train_oa 99.99, val_oa 86.92, best val oa 87.27
+[04/01 20:40:36] ScanObjectNNHardest INFO: Find a better ckpt @E243
+[04/01 20:40:36] ScanObjectNNHardest INFO: 
+Classes	Acc
+bag       : 66.27%
+bin       : 89.45%
+box       : 70.68%
+cabinet   : 91.67%
+chair     : 95.90%
+desk      : 84.67%
+display   : 89.22%
+door      : 94.29%
+shelf     : 88.80%
+table     : 72.22%
+bed       : 81.82%
+pillow    : 90.48%
+sink      : 80.83%
+sofa      : 94.29%
+toilet    : 91.76%
+E@243	OA: 87.30	mAcc: 85.49
+
+[04/01 20:40:36] ScanObjectNNHardest INFO: Epoch 243 LR 0.000100 train_oa 99.99, val_oa 87.30, best val oa 87.30
+[04/01 20:40:36] ScanObjectNNHardest INFO: Found the best model and saved in log/scanobjectnn/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR/checkpoint/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR_ckpt_best.pth
+[04/01 20:41:33] ScanObjectNNHardest INFO: Epoch 244 LR 0.000100 train_oa 99.98, val_oa 87.16, best val oa 87.30
+[04/01 20:42:27] ScanObjectNNHardest INFO: Epoch 245 LR 0.000100 train_oa 99.99, val_oa 86.78, best val oa 87.30
+[04/01 20:43:22] ScanObjectNNHardest INFO: Epoch 246 LR 0.000100 train_oa 99.98, val_oa 86.81, best val oa 87.30
+[04/01 20:44:22] ScanObjectNNHardest INFO: Epoch 247 LR 0.000100 train_oa 99.99, val_oa 86.78, best val oa 87.30
+[04/01 20:45:21] ScanObjectNNHardest INFO: Epoch 248 LR 0.000100 train_oa 99.99, val_oa 86.81, best val oa 87.30
+[04/01 20:46:17] ScanObjectNNHardest INFO: Epoch 249 LR 0.000100 train_oa 99.98, val_oa 87.23, best val oa 87.30
+[04/01 20:47:12] ScanObjectNNHardest INFO: Epoch 250 LR 0.000100 train_oa 99.99, val_oa 87.02, best val oa 87.30
+[04/01 20:47:15] ScanObjectNNHardest INFO: 
+Classes	Acc
+bag       : 73.49%
+bin       : 87.94%
+box       : 63.91%
+cabinet   : 90.32%
+chair     : 96.15%
+desk      : 84.67%
+display   : 90.20%
+door      : 95.71%
+shelf     : 87.97%
+table     : 76.67%
+bed       : 83.64%
+pillow    : 87.62%
+sink      : 80.00%
+sofa      : 93.33%
+toilet    : 89.41%
+E@243	OA: 87.27	mAcc: 85.40
+
+[04/01 20:47:15] ScanObjectNNHardest INFO: Successful Loading the ckpt from log/scanobjectnn/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR/checkpoint/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR_ckpt_best.pth
+[04/01 20:47:15] ScanObjectNNHardest INFO: ckpts @ 243 epoch( {'best_val': 87.30048370361328} )
+[04/01 20:47:19] ScanObjectNNHardest INFO: 
+Classes	Acc
+bag       : 67.47%
+bin       : 88.94%
+box       : 70.68%
+cabinet   : 91.67%
+chair     : 95.90%
+desk      : 84.00%
+display   : 88.73%
+door      : 94.76%
+shelf     : 88.80%
+table     : 72.59%
+bed       : 80.91%
+pillow    : 91.43%
+sink      : 80.83%
+sofa      : 93.81%
+toilet    : 91.76%
+E@243	OA: 87.27	mAcc: 85.49
+

checkpoint/scanobejctnn/scanobjectnn-train-ppv2-s-ngpus1-seed1234-20250401-164946-nvdQKdTEv4B3fR7TMhwGjR_ckpt_best.pth

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