filaPro
s3dis
Hello, I would like to train solely on the S3DIS dataset, but my results are extremely poor. Could you please help me understand why? To train on S3DIS only, I should only need to modify the configuration file, right? I’ve made changes to the config as follows: base = ['mmdet3d::base/default_runtime.py'] custom_imports = dict(imports=['unidet3d'])
classes_scannet = ['cabinet', 'bed', 'chair', 'sofa', 'table', 'door', 'window', 'bookshelf', 'picture', 'counter', 'desk', 'curtain', 'refrigerator', 'showercurtrain', 'toilet', 'sink', 'bathtub', 'otherfurniture'] classes_s3dis = ['table', 'chair', 'sofa', 'bookcase', 'board']
classes_multiscan = ['door', 'table', 'chair', 'cabinet', 'window', 'sofa', 'microwave', 'pillow', 'tv_monitor', 'curtain', 'trash_can', 'suitcase', 'sink', 'backpack', 'bed', 'refrigerator', 'toilet']
classes_3rscan = classes_scannet
classes_scannetpp = ['table', 'door', 'ceiling lamp', 'cabinet', 'blinds', 'curtain', 'chair', 'storage cabinet', 'office chair', 'bookshelf', 'whiteboard', 'window', 'box', 'monitor', 'shelf', 'heater', 'kitchen cabinet', 'sofa', 'bed', 'trash can', 'book', 'plant', 'blanket', 'tv', 'computer tower', 'refrigerator', 'jacket', 'sink', 'bag', 'picture', 'pillow', 'towel', 'suitcase', 'backpack', 'crate', 'keyboard', 'rack', 'toilet', 'printer', 'poster', 'painting', 'microwave', 'shoes', 'socket', 'bottle', 'bucket', 'cushion', 'basket', 'shoe rack', 'telephone', 'file folder', 'laptop', 'plant pot', 'exhaust fan', 'cup', 'coat hanger', 'light switch', 'speaker', 'table lamp', 'kettle', 'smoke detector', 'container', 'power strip', 'slippers', 'paper bag', 'mouse', 'cutting board', 'toilet paper', 'paper towel', 'pot', 'clock', 'pan', 'tap', 'jar', 'soap dispenser', 'binder', 'bowl', 'tissue box', 'whiteboard eraser', 'toilet brush', 'spray bottle', 'headphones', 'stapler', 'marker']
classes_arkitscenes = ['cabinet', 'refrigerator', 'shelf', 'stove', 'bed', 'sink', 'washer', 'toilet', 'bathtub', 'oven', 'dishwasher', 'fireplace', 'stool', 'chair', 'table', 'tv_monitor', 'sofa']
model settings
num_channels=32 voxel_size=0.02
model = dict( type='UniDet3D', data_preprocessor=dict(type='Det3DDataPreprocessor_'), in_channels=6, num_channels=num_channels, voxel_size=voxel_size, min_spatial_shape=128, query_thr=3000, bbox_by_mask=[True], target_by_distance=[False], use_superpoints=[True], fast_nms=[True], backbone=dict( type='SpConvUNet', num_planes=[num_channels * (i + 1) for i in range(5)], return_blocks=True), decoder=dict( type='UniDet3DEncoder', num_layers=6, datasets_classes=[classes_s3dis], in_channels=num_channels, d_model=256, num_heads=8, hidden_dim=1024, dropout=0.0, activation_fn='gelu', datasets=['s3dis'], angles=[False]), criterion=dict( type='UniDet3DCriterion', datasets=['s3dis'], datasets_weights=[1], bbox_loss_simple=dict( type='UniDet3DAxisAlignedIoULoss', mode='diou', reduction='none'), bbox_loss_rotated=dict( type='UniDet3DRotatedIoU3DLoss', mode='diou', reduction='none'), matcher=dict( type='UniMatcher', costs=[ dict(type='QueryClassificationCost', weight=0.5), dict(type='BboxCostJointTraining', weight=2.0, loss_simple=dict( type='UniDet3DAxisAlignedIoULoss', mode='diou', reduction='none'), loss_rotated=dict( type='UniDet3DRotatedIoU3DLoss', mode='diou', reduction='none'))]), loss_weight=[0.5, 1.0], non_object_weight=0.1, topk=[6], iter_matcher=True), train_cfg=dict(topk=6), test_cfg=dict( low_sp_thr=0.18, up_sp_thr=0.81, topk_insts=1000, score_thr=0, iou_thr=[0.5]))
scannet dataset settings
metainfo_scannet = dict(classes=classes_scannet) data_root_scannet = 'data/scannet/'
max_class_scannet = 20 dataset_type_scannet = 'ScanNetDetDataset' data_prefix_scannet = dict( pts='points', pts_instance_mask='instance_mask', pts_semantic_mask='semantic_mask', sp_pts_mask='super_points')
train_pipeline_scannet = [ dict( type='LoadPointsFromFile', coord_type='DEPTH', shift_height=False, use_color=True, load_dim=6, use_dim=[0, 1, 2, 3, 4, 5]), dict( type='LoadAnnotations3D_', with_bbox_3d=False, with_label_3d=False, with_mask_3d=True, with_seg_3d=True, with_sp_mask_3d=True), dict(type='GlobalAlignment', rotation_axis=2), dict(type='PointSegClassMapping'), dict( type='RandomFlip3D', sync_2d=False, flip_ratio_bev_horizontal=0.5, flip_ratio_bev_vertical=0.5), dict( type='GlobalRotScaleTrans', rot_range=[-3.14, 3.14], scale_ratio_range=[0.8, 1.2], translation_std=[0.1, 0.1, 0.1], shift_height=False), dict( type='NormalizePointsColor_', color_mean=[127.5, 127.5, 127.5]), dict( type='PointDetClassMappingScanNet', num_classes=max_class_scannet, stuff_classes=[0, 1]), dict( type='ElasticTransfrom', gran=[6, 20], mag=[40, 160], voxel_size=voxel_size, p=0.5), dict( type='Pack3DDetInputs_', keys=[ 'points', 'gt_labels_3d', 'pts_semantic_mask', 'pts_instance_mask', 'sp_pts_mask', 'gt_sp_masks', 'elastic_coords' ]) ] test_pipeline_scannet = [ dict( type='LoadPointsFromFile', coord_type='DEPTH', shift_height=False, use_color=True, load_dim=6, use_dim=[0, 1, 2, 3, 4, 5]), dict( type='LoadAnnotations3D_', with_bbox_3d=False, with_label_3d=False, with_mask_3d=True, with_seg_3d=True, with_sp_mask_3d=True), dict(type='GlobalAlignment', rotation_axis=2), dict( type='MultiScaleFlipAug3D', img_scale=(1333, 800), pts_scale_ratio=1, flip=False, transforms=[ dict( type='NormalizePointsColor_', color_mean=[127.5, 127.5, 127.5])]), dict(type='Pack3DDetInputs_', keys=['points', 'sp_pts_mask']) ]
s3dis dataset settings
dataset_type_s3dis = 'S3DISSegDetDataset' data_root_s3dis = 'data/s3dis/' data_prefix_s3dis = dict( pts='points', pts_instance_mask='instance_mask', pts_semantic_mask='semantic_mask', sp_pts_mask='super_points')
train_area = [1, 2, 3, 4, 6] test_area = 5
train_pipeline_s3dis = [ dict( type='LoadPointsFromFile', coord_type='DEPTH', shift_height=False, use_color=True, load_dim=6, use_dim=[0, 1, 2, 3, 4, 5]), dict( type='LoadAnnotations3D_', with_label_3d=False, with_bbox_3d=False, with_mask_3d=True, with_seg_3d=True, with_sp_mask_3d=True), dict( type='PointSample_', num_points=180000), dict( type='RandomFlip3D', sync_2d=False, flip_ratio_bev_horizontal=0.5, flip_ratio_bev_vertical=0.5), dict( type='GlobalRotScaleTrans', rot_range=[0.0, 0.0], scale_ratio_range=[0.9, 1.1], translation_std=[.1, .1, .1], shift_height=False), dict(type='PointDetClassMappingS3DIS', classes=[7, 8, 9, 10, 11]), dict( type='NormalizePointsColor_', color_mean=[127.5, 127.5, 127.5]), dict( type='ElasticTransfrom', gran=[6, 20], mag=[40, 160], voxel_size=voxel_size, p=-1), dict( type='Pack3DDetInputs_', keys=[ 'points', 'elastic_coords', 'gt_labels_3d', 'sp_pts_mask', 'gt_sp_masks', 'pts_semantic_mask', 'pts_instance_mask' ]) ] test_pipeline_s3dis = [ dict( type='LoadPointsFromFile', coord_type='DEPTH', shift_height=False, use_color=True, load_dim=6, use_dim=[0, 1, 2, 3, 4, 5]), dict( type='LoadAnnotations3D_', with_bbox_3d=False, with_label_3d=False, with_mask_3d=True, with_seg_3d=True, with_sp_mask_3d=True), dict( type='MultiScaleFlipAug3D', img_scale=(1333, 800), pts_scale_ratio=1, flip=False, transforms=[ dict( type='PointSample_', num_points=180000), dict( type='NormalizePointsColor_', color_mean=[127.5, 127.5, 127.5])]), dict(type='Pack3DDetInputs_', keys=['points', 'sp_pts_mask']) ]
multiscan dataset settings
data_root_multiscan = 'data/multiscan/bins' dataset_type_multiscan = 'MultiScan_' data_prefix_multiscan = dict( pts='points', pts_instance_mask='instance_mask', pts_semantic_mask='semantic_mask', sp_pts_mask='super_points')
train_pipeline_multiscan = [ dict( type='LoadPointsFromFile', coord_type='DEPTH', shift_height=False, use_color=True, load_dim=6, use_dim=[0, 1, 2, 3, 4, 5]), dict(type='LoadAnnotations3D_', with_label_3d=True, with_bbox_3d=True, with_sp_mask_3d=True), dict(type='PointSample_', num_points=100000), dict( type='RandomFlip3D', sync_2d=False, flip_ratio_bev_horizontal=0.5, flip_ratio_bev_vertical=0.5), dict( type='GlobalRotScaleTrans', rot_range=[0, 0], scale_ratio_range=[0.9, 1.1], translation_std=[0.1, 0.1, 0.1], shift_height=False), dict( type='NormalizePointsColor_', color_mean=[127.5, 127.5, 127.5]), dict( type='ElasticTransfrom', gran=[6, 20], mag=[40, 160], voxel_size=voxel_size, p=-1), dict( type='Pack3DDetInputs_', keys=['points', 'elastic_coords', 'gt_bboxes_3d', 'gt_labels_3d', 'sp_pts_mask']) ] test_pipeline_multiscan = [ dict( type='LoadPointsFromFile', coord_type='DEPTH', shift_height=False, use_color=True, load_dim=6, use_dim=[0, 1, 2, 3, 4, 5]), dict(type='LoadAnnotations3D_', with_label_3d=False, with_bbox_3d=False, with_sp_mask_3d=True), dict( type='MultiScaleFlipAug3D', img_scale=(1333, 800), pts_scale_ratio=1, flip=False, transforms=[ dict(type='PointSample_', num_points=100000), dict( type='NormalizePointsColor_', color_mean=[127.5, 127.5, 127.5]) ]), dict(type='Pack3DDetInputs_', keys=['points', 'sp_pts_mask']) ]
3rscan dataset settings
data_root_3rscan = 'data/3rscan' dataset_type_3rscan = 'ThreeRScan_' data_prefix_3rscan = dict( pts='points', pts_instance_mask='instance_mask', pts_semantic_mask='semantic_mask', sp_pts_mask='super_points_spt')
train_pipeline_3rscan = [ dict( type='LoadPointsFromFile', coord_type='DEPTH', shift_height=False, use_color=True, load_dim=6, use_dim=[0, 1, 2, 3, 4, 5]), dict(type='LoadAnnotations3D_', with_label_3d=True, with_bbox_3d=True, with_sp_mask_3d=True), dict(type='PointSample_', num_points=100000), dict( type='RandomFlip3D', sync_2d=False, flip_ratio_bev_horizontal=0.5, flip_ratio_bev_vertical=0.5), dict( type='GlobalRotScaleTrans', rot_range=[0, 0], scale_ratio_range=[0.9, 1.1], translation_std=[0.1, 0.1, 0.1], shift_height=False), dict( type='NormalizePointsColor_', color_mean=[127.5, 127.5, 127.5]), dict( type='ElasticTransfrom', gran=[6, 20], mag=[40, 160], voxel_size=voxel_size, p=-1), dict( type='Pack3DDetInputs_', keys=['points', 'elastic_coords', 'gt_bboxes_3d', 'gt_labels_3d', 'sp_pts_mask']) ] test_pipeline_3rscan = [ dict( type='LoadPointsFromFile', coord_type='DEPTH', shift_height=False, use_color=True, load_dim=6, use_dim=[0, 1, 2, 3, 4, 5]), dict(type='LoadAnnotations3D_', with_label_3d=False, with_bbox_3d=False, with_sp_mask_3d=True), dict( type='MultiScaleFlipAug3D', img_scale=(1333, 800), pts_scale_ratio=1, flip=False, transforms=[ dict(type='PointSample_', num_points=100000), dict( type='NormalizePointsColor_', color_mean=[127.5, 127.5, 127.5]) ]), dict(type='Pack3DDetInputs_', keys=['points', 'sp_pts_mask']) ]
scannetpp dataset settings
data_root_scannetpp = 'data/scannetpp/bins' dataset_type_scannetpp = 'Scannetpp_' data_prefix_scannetpp = dict( pts='points', pts_instance_mask='instance_mask', pts_semantic_mask='semantic_mask', sp_pts_mask='super_points_spt')
train_pipeline_scannetpp = [ dict( type='LoadPointsFromFile', coord_type='DEPTH', shift_height=False, use_color=True, load_dim=6, use_dim=[0, 1, 2, 3, 4, 5]), dict(type='LoadAnnotations3D_', with_label_3d=True, with_bbox_3d=True, with_sp_mask_3d=True), dict(type='PointSample_', num_points=200000), dict( type='RandomFlip3D', sync_2d=False, flip_ratio_bev_horizontal=0.5, flip_ratio_bev_vertical=0.5), dict( type='GlobalRotScaleTrans', rot_range=[0, 0], scale_ratio_range=[0.9, 1.1], translation_std=[0.1, 0.1, 0.1], shift_height=False), dict( type='NormalizePointsColor_', color_mean=[127.5, 127.5, 127.5]), dict( type='ElasticTransfrom', gran=[6, 20], mag=[40, 160], voxel_size=voxel_size, p=-1), dict( type='Pack3DDetInputs_', keys=['points', 'elastic_coords', 'gt_bboxes_3d', 'gt_labels_3d', 'sp_pts_mask']) ] test_pipeline_scannetpp = [ dict( type='LoadPointsFromFile', coord_type='DEPTH', shift_height=False, use_color=True, load_dim=6, use_dim=[0, 1, 2, 3, 4, 5]), dict(type='LoadAnnotations3D_', with_label_3d=False, with_bbox_3d=False, with_sp_mask_3d=True), dict( type='MultiScaleFlipAug3D', img_scale=(1333, 800), pts_scale_ratio=1, flip=False, transforms=[ dict(type='PointSample_', num_points=200000), dict( type='NormalizePointsColor_', color_mean=[127.5, 127.5, 127.5]) ]), dict(type='Pack3DDetInputs_', keys=['points', 'sp_pts_mask']) ]
arkitscenes dataset settings
dataset_type_arkitscenes = 'ARKitScenesOfflineDataset' data_root_arkitscenes = 'data/arkitscenes' data_prefix_arkitscenes = dict( pts='offline_prepared_data', sp_pts_mask='super_points')
train_pipeline_arkitscenes = [ dict( type='LoadPointsFromFile', coord_type='DEPTH', shift_height=False, use_color=True, load_dim=6, use_dim=[0, 1, 2, 3, 4, 5]), dict(type='LoadAnnotations3D_', with_label_3d=True, with_bbox_3d=True, with_sp_mask_3d=True), dict(type='PointSample_', num_points=100000), dict( type='DenormalizePointsColor', color_mean=[0, 0, 0], color_std=[255, 255, 255]), dict( type='NormalizePointsColor_', color_mean=[127.5, 127.5, 127.5]), dict( type='RandomFlip3D', sync_2d=False, flip_ratio_bev_horizontal=0.5, flip_ratio_bev_vertical=0.5), dict( type='GlobalRotScaleTrans', rot_range=[-0.5, 0.5], scale_ratio_range=[0.9, 1.1], translation_std=[0.1, 0.1, 0.1], shift_height=False), dict( type='ElasticTransfrom', gran=[6, 20], mag=[40, 160], voxel_size=voxel_size, p=-1), dict( type='Pack3DDetInputs_', keys=['points', 'elastic_coords', 'gt_bboxes_3d', 'gt_labels_3d', 'sp_pts_mask']) ] test_pipeline_arkitscenes = [ dict( type='LoadPointsFromFile', coord_type='DEPTH', shift_height=False, use_color=True, load_dim=6, use_dim=[0, 1, 2, 3, 4, 5]), dict(type='LoadAnnotations3D_', with_label_3d=False, with_bbox_3d=False, with_sp_mask_3d=True), dict( type='MultiScaleFlipAug3D', img_scale=(1333, 800), pts_scale_ratio=1, flip=False, transforms=[ dict(type='PointSample_', num_points=100000), dict( type='DenormalizePointsColor', color_mean=[0, 0, 0], color_std=[255, 255, 255]), dict( type='NormalizePointsColor_', color_mean=[127.5, 127.5, 127.5]) ]), dict(type='Pack3DDetInputs_', keys=['points', 'sp_pts_mask']) ]
run settings
train_dataloader = dict(
batch_size=8,
num_workers=8,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=True),
dataset=dict(
type='ConcatDataset_',
# datasets=[dict(
# type=dataset_type_scannet,
# ann_file='scannet_infos_train.pkl',
# data_prefix=data_prefix_scannet,
# data_root=data_root_scannet,
# metainfo=metainfo_scannet,
# pipeline=train_pipeline_scannet,
# ignore_index=max_class_scannet,
# scene_idxs=None,
# test_mode=False)] #+
datasets=[dict(
type=dataset_type_s3dis,
data_root=data_root_s3dis,
ann_file=f's3dis_sp_infos_Area_{i}.pkl',
partition=0.33,
pipeline=train_pipeline_s3dis,
filter_empty_gt=True,
data_prefix=data_prefix_s3dis,
box_type_3d='Depth',
backend_args=None) for i in train_area]
# [dict(
# type=dataset_type_multiscan,
# ann_file='multiscan_infos_train.pkl',
# partition=0.25,
# data_prefix=data_prefix_multiscan,
# data_root=data_root_multiscan,
# pipeline=train_pipeline_multiscan,
# test_mode=False)] +
# [dict(
# type=dataset_type_3rscan,
# ann_file='3rscan_infos_train.pkl',
# partition=0.15,
# data_prefix=data_prefix_3rscan,
# data_root=data_root_3rscan,
# pipeline=train_pipeline_3rscan,
# test_mode=False)] +
# [dict(
# type=dataset_type_scannetpp,
# ann_file='scannetpp_infos_train.pkl',
# partition=0.33,
# data_prefix=data_prefix_scannetpp,
# data_root=data_root_scannetpp,
# pipeline=train_pipeline_scannetpp,
# test_mode=False)] +
# [dict(
# type=dataset_type_arkitscenes,
# ann_file='arkitscenes_offline_infos_train.pkl',
# partition=0.08,
# data_prefix=data_prefix_arkitscenes,
# data_root=data_root_arkitscenes,
# pipeline=train_pipeline_arkitscenes,
# test_mode=False)]
))
val_dataloader = dict(
batch_size=1,
num_workers=1,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type='ConcatDataset_',
datasets=
# [dict(
# type=dataset_type_scannet,
# ann_file='scannet_infos_val.pkl',
# data_prefix=data_prefix_scannet,
# data_root=data_root_scannet,
# metainfo=metainfo_scannet,
# pipeline=test_pipeline_scannet,
# ignore_index=max_class_scannet,
# test_mode=True)] #+
[dict(
type=dataset_type_s3dis,
data_root=data_root_s3dis,
ann_file=f's3dis_sp_infos_Area_{test_area}.pkl',
pipeline=test_pipeline_s3dis,
test_mode=True,
data_prefix=data_prefix_s3dis,
box_type_3d='Depth',
backend_args=None)]
# [dict(
# type=dataset_type_multiscan,
# ann_file='multiscan_infos_val.pkl',
# data_prefix=data_prefix_multiscan,
# data_root=data_root_multiscan,
# pipeline=test_pipeline_multiscan,
# test_mode=True)] +
# [dict(
# type=dataset_type_3rscan,
# ann_file='3rscan_infos_val.pkl',
# data_prefix=data_prefix_3rscan,
# data_root=data_root_3rscan,
# pipeline=test_pipeline_3rscan,
# test_mode=True)] +
# [dict(
# type=dataset_type_scannetpp,
# ann_file='scannetpp_infos_val.pkl',
# data_prefix=data_prefix_scannetpp,
# data_root=data_root_scannetpp,
# pipeline=test_pipeline_scannetpp,
# test_mode=True)] +
# [dict(
# type=dataset_type_arkitscenes,
# ann_file='arkitscenes_offline_infos_val.pkl',
# data_prefix=data_prefix_arkitscenes,
# data_root=data_root_arkitscenes,
# pipeline=test_pipeline_arkitscenes,
# test_mode=True)]
))
test_dataloader = val_dataloader
load_from = 'work_dirs/tmp/oneformer3d_1xb4_scannet.pth'
test_evaluator = dict(type='IndoorMetric_', datasets=['s3dis'], datasets_classes=[classes_s3dis])
val_evaluator = test_evaluator
optim_wrapper = dict( type='OptimWrapper', optimizer=dict(type='AdamW', lr=0.0001 * 2*2, weight_decay=0.05), clip_grad=dict(max_norm=10, norm_type=2))
param_scheduler = dict(type='PolyLR', begin=0, end=1024, power=0.9)
custom_hooks = [dict(type='EmptyCacheHook', after_iter=True)] default_hooks = dict( checkpoint=dict(interval=1, max_keep_ckpts=16))
train_cfg = dict( type='EpochBasedTrainLoop', max_epochs=1024, dynamic_intervals=[(1, 16), (1024 - 16, 1)]) val_cfg = dict(type='ValLoop') test_cfg = dict(type='TestLoop')
Is it fine when training on all 6 datasets? Also I noticed that you modified learning rate. Not sure if it is a good idea.
Hello, I have not tried running the experiments on all six datasets, but I can confirm that my setup works correctly on the ScanNet dataset alone. Now, for the paper I need to add a second dataset, so I would like to report results on S3DIS. I followed the instructions in the config file: I commented out all other datasets and kept only the S3DIS section, using exactly the same data paths and hyper-parameters that you provided. Your published [email protected] is 63.6, whereas my reproduction only reaches 5.2. Could you please advise what might cause this huge gap and suggest a solution?
Hi! Could you test the released model trained on 6 datasets only on s3dis?
You only need to change this part of the config:
val_dataloader = dict(
batch_size=1,
num_workers=1,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type='ConcatDataset_',
datasets= \
# [dict(
# type=dataset_type_scannet,
# ann_file='scannet_infos_val.pkl',
# data_prefix=data_prefix_scannet,
# data_root=data_root_scannet,
# metainfo=metainfo_scannet,
# pipeline=test_pipeline_scannet,
# ignore_index=max_class_scannet,
# test_mode=True)] + \
[dict(
type=dataset_type_s3dis,
data_root=data_root_s3dis,
ann_file=f's3dis_sp_infos_Area_{test_area}.pkl',
pipeline=test_pipeline_s3dis,
test_mode=True,
data_prefix=data_prefix_s3dis,
box_type_3d='Depth',
backend_args=None)] #+ \
# [dict(
# type=dataset_type_multiscan,
# ann_file='multiscan_infos_val.pkl',
# data_prefix=data_prefix_multiscan,
# data_root=data_root_multiscan,
# pipeline=test_pipeline_multiscan,
# test_mode=True)] + \
# [dict(
# type=dataset_type_3rscan,
# ann_file='3rscan_infos_val.pkl',
# data_prefix=data_prefix_3rscan,
# data_root=data_root_3rscan,
# pipeline=test_pipeline_3rscan,
# test_mode=True)] + \
# [dict(
# type=dataset_type_scannetpp,
# ann_file='scannetpp_infos_val.pkl',
# data_prefix=data_prefix_scannetpp,
# data_root=data_root_scannetpp,
# pipeline=test_pipeline_scannetpp,
# test_mode=True)] + \
# [dict(
# type=dataset_type_arkitscenes,
# ann_file='arkitscenes_offline_infos_val.pkl',
# data_prefix=data_prefix_arkitscenes,
# data_root=data_root_arkitscenes,
# pipeline=test_pipeline_arkitscenes,
# test_mode=True)]
))
Hello, thank you very much for providing the solution. I have conducted multiple tests following your suggestions, but the results were not satisfactory. Below is the code I modified according to your advice. base = ['mmdet3d::base/default_runtime.py'] custom_imports = dict(imports=['unidet3d'])
classes_scannet = ['cabinet', 'bed', 'chair', 'sofa', 'table', 'door', 'window', 'bookshelf', 'picture', 'counter', 'desk', 'curtain', 'refrigerator', 'showercurtrain', 'toilet', 'sink', 'bathtub', 'otherfurniture'] classes_s3dis = ['table', 'chair', 'sofa', 'bookcase', 'board']
classes_multiscan = ['door', 'table', 'chair', 'cabinet', 'window', 'sofa', 'microwave', 'pillow',
'tv_monitor', 'curtain', 'trash_can', 'suitcase', 'sink', 'backpack', 'bed',
'refrigerator', 'toilet']
classes_3rscan = classes_scannet
classes_scannetpp = ['table', 'door', 'ceiling lamp', 'cabinet', 'blinds', 'curtain', 'chair', 'storage cabinet', 'office chair', 'bookshelf', 'whiteboard', 'window', 'box', 'monitor', 'shelf', 'heater', 'kitchen cabinet', 'sofa', 'bed', 'trash can', 'book', 'plant', 'blanket', 'tv', 'computer tower', 'refrigerator', 'jacket', 'sink', 'bag', 'picture', 'pillow', 'towel', 'suitcase', 'backpack', 'crate', 'keyboard', 'rack', 'toilet', 'printer', 'poster', 'painting', 'microwave', 'shoes', 'socket', 'bottle', 'bucket', 'cushion', 'basket', 'shoe rack', 'telephone', 'file folder', 'laptop', 'plant pot', 'exhaust fan', 'cup', 'coat hanger', 'light switch', 'speaker', 'table lamp', 'kettle', 'smoke detector', 'container', 'power strip', 'slippers', 'paper bag', 'mouse', 'cutting board', 'toilet paper', 'paper towel', 'pot', 'clock', 'pan', 'tap', 'jar', 'soap dispenser', 'binder', 'bowl', 'tissue box', 'whiteboard eraser', 'toilet brush', 'spray bottle', 'headphones', 'stapler', 'marker']
classes_arkitscenes = ['cabinet', 'refrigerator', 'shelf', 'stove', 'bed', 'sink', 'washer', 'toilet', 'bathtub', 'oven', 'dishwasher', 'fireplace', 'stool', 'chair', 'table', 'tv_monitor', 'sofa']
model settings
num_channels=32 voxel_size=0.02
model = dict( type='UniDet3D', data_preprocessor=dict(type='Det3DDataPreprocessor_'), in_channels=6, num_channels=num_channels, voxel_size=voxel_size, min_spatial_shape=128, query_thr=3000, bbox_by_mask=[True, True, False, False, False, False], target_by_distance=[False, False, True, True, True, True], use_superpoints=[True, True, True, False, False, False], fast_nms=[True, False, True, True, True, None], backbone=dict( type='SpConvUNet', num_planes=[num_channels * (i + 1) for i in range(5)], return_blocks=True), decoder=dict( type='UniDet3DEncoder', num_layers=6, datasets_classes=[classes_scannet, classes_s3dis, classes_multiscan, classes_3rscan, classes_scannetpp, classes_arkitscenes], in_channels=num_channels, d_model=256, num_heads=8, hidden_dim=1024, dropout=0.0, activation_fn='gelu', datasets=['scannet', 's3dis', 'multiscan', '3rscan', 'scannetpp', 'arkitscenes'], angles=[False, False, False, False, False, True]), criterion=dict( type='UniDet3DCriterion', datasets=['scannet', 's3dis', 'multiscan', '3rscan', 'scannetpp', 'arkitscenes'], datasets_weights=[1, 1, 1, 1, 1, 1], bbox_loss_simple=dict( type='UniDet3DAxisAlignedIoULoss', mode='diou', reduction='none'), bbox_loss_rotated=dict( type='UniDet3DRotatedIoU3DLoss', mode='diou', reduction='none'), matcher=dict( type='UniMatcher', costs=[ dict(type='QueryClassificationCost', weight=0.5), dict(type='BboxCostJointTraining', weight=2.0, loss_simple=dict( type='UniDet3DAxisAlignedIoULoss', mode='diou', reduction='none'), loss_rotated=dict( type='UniDet3DRotatedIoU3DLoss', mode='diou', reduction='none'))]), loss_weight=[0.5, 1.0], non_object_weight=0.1, topk=[6, 6, 3, 3, 3, 3], iter_matcher=True), train_cfg=dict(topk=6), test_cfg=dict( low_sp_thr=0.18, up_sp_thr=0.81, topk_insts=1000, score_thr=0, iou_thr=[0.5, 0.55, 0.55, 0.55, 0.55, 0.55]))
scannet dataset settings
metainfo_scannet = dict(classes=classes_scannet) data_root_scannet = '/home/wanghui/DownLoads/unidet3d-master/data/scannet/'
max_class_scannet = 20 dataset_type_scannet = 'ScanNetDetDataset' data_prefix_scannet = dict( pts='points', pts_instance_mask='instance_mask', pts_semantic_mask='semantic_mask', sp_pts_mask='super_points')
train_pipeline_scannet = [ dict( type='LoadPointsFromFile', coord_type='DEPTH', shift_height=False, use_color=True, load_dim=6, use_dim=[0, 1, 2, 3, 4, 5]), dict( type='LoadAnnotations3D_', with_bbox_3d=False, with_label_3d=False, with_mask_3d=True, with_seg_3d=True, with_sp_mask_3d=True), dict(type='GlobalAlignment', rotation_axis=2), dict(type='PointSegClassMapping'), dict( type='RandomFlip3D', sync_2d=False, flip_ratio_bev_horizontal=0.5, flip_ratio_bev_vertical=0.5), dict( type='GlobalRotScaleTrans', rot_range=[-3.14, 3.14], scale_ratio_range=[0.8, 1.2], translation_std=[0.1, 0.1, 0.1], shift_height=False), dict( type='NormalizePointsColor_', color_mean=[127.5, 127.5, 127.5]), dict( type='PointDetClassMappingScanNet', num_classes=max_class_scannet, stuff_classes=[0, 1]), dict( type='ElasticTransfrom', gran=[6, 20], mag=[40, 160], voxel_size=voxel_size, p=0.5), dict( type='Pack3DDetInputs_', keys=[ 'points', 'gt_labels_3d', 'pts_semantic_mask', 'pts_instance_mask', 'sp_pts_mask', 'gt_sp_masks', 'elastic_coords' ]) ] test_pipeline_scannet = [ dict( type='LoadPointsFromFile', coord_type='DEPTH', shift_height=False, use_color=True, load_dim=6, use_dim=[0, 1, 2, 3, 4, 5]), dict( type='LoadAnnotations3D_', with_bbox_3d=False, with_label_3d=False, with_mask_3d=True, with_seg_3d=True, with_sp_mask_3d=True), dict(type='GlobalAlignment', rotation_axis=2), dict( type='MultiScaleFlipAug3D', img_scale=(1333, 800), pts_scale_ratio=1, flip=False, transforms=[ dict( type='NormalizePointsColor_', color_mean=[127.5, 127.5, 127.5])]), dict(type='Pack3DDetInputs_', keys=['points', 'sp_pts_mask']) ]
s3dis dataset settings
dataset_type_s3dis = 'S3DISSegDetDataset' data_root_s3dis = '/home/wanghui/DownLoads/unidet3d-master/data/s3dis/' data_prefix_s3dis = dict( pts='points', pts_instance_mask='instance_mask', pts_semantic_mask='semantic_mask', sp_pts_mask='super_points')
train_area = [1, 2, 3, 4, 6] test_area = 5
train_pipeline_s3dis = [ dict( type='LoadPointsFromFile', coord_type='DEPTH', shift_height=False, use_color=True, load_dim=6, use_dim=[0, 1, 2, 3, 4, 5]), dict( type='LoadAnnotations3D_', with_label_3d=False, with_bbox_3d=False, with_mask_3d=True, with_seg_3d=True, with_sp_mask_3d=True), dict( type='PointSample_', num_points=180000), dict( type='RandomFlip3D', sync_2d=False, flip_ratio_bev_horizontal=0.5, flip_ratio_bev_vertical=0.5), dict( type='GlobalRotScaleTrans', rot_range=[0.0, 0.0], scale_ratio_range=[0.9, 1.1], translation_std=[.1, .1, .1], shift_height=False), dict(type='PointDetClassMappingS3DIS', classes=[7, 8, 9, 10, 11]), dict( type='NormalizePointsColor_', color_mean=[127.5, 127.5, 127.5]), dict( type='ElasticTransfrom', gran=[6, 20], mag=[40, 160], voxel_size=voxel_size, p=-1), dict( type='Pack3DDetInputs_', keys=[ 'points', 'elastic_coords', 'gt_labels_3d', 'sp_pts_mask', 'gt_sp_masks', 'pts_semantic_mask', 'pts_instance_mask' ]) ] test_pipeline_s3dis = [ dict( type='LoadPointsFromFile', coord_type='DEPTH', shift_height=False, use_color=True, load_dim=6, use_dim=[0, 1, 2, 3, 4, 5]), dict( type='LoadAnnotations3D_', with_bbox_3d=False, with_label_3d=False, with_mask_3d=True, with_seg_3d=True, with_sp_mask_3d=True), dict( type='MultiScaleFlipAug3D', img_scale=(1333, 800), pts_scale_ratio=1, flip=False, transforms=[ dict( type='PointSample_', num_points=180000), dict( type='NormalizePointsColor_', color_mean=[127.5, 127.5, 127.5])]), dict(type='Pack3DDetInputs_', keys=['points', 'sp_pts_mask']) ]
multiscan dataset settings
data_root_multiscan = 'data/multiscan/bins' dataset_type_multiscan = 'MultiScan_' data_prefix_multiscan = dict( pts='points', pts_instance_mask='instance_mask', pts_semantic_mask='semantic_mask', sp_pts_mask='super_points')
train_pipeline_multiscan = [ dict( type='LoadPointsFromFile', coord_type='DEPTH', shift_height=False, use_color=True, load_dim=6, use_dim=[0, 1, 2, 3, 4, 5]), dict(type='LoadAnnotations3D_', with_label_3d=True, with_bbox_3d=True, with_sp_mask_3d=True), dict(type='PointSample_', num_points=100000), dict( type='RandomFlip3D', sync_2d=False, flip_ratio_bev_horizontal=0.5, flip_ratio_bev_vertical=0.5), dict( type='GlobalRotScaleTrans', rot_range=[0, 0], scale_ratio_range=[0.9, 1.1], translation_std=[0.1, 0.1, 0.1], shift_height=False), dict( type='NormalizePointsColor_', color_mean=[127.5, 127.5, 127.5]), dict( type='ElasticTransfrom', gran=[6, 20], mag=[40, 160], voxel_size=voxel_size, p=-1), dict( type='Pack3DDetInputs_', keys=['points', 'elastic_coords', 'gt_bboxes_3d', 'gt_labels_3d', 'sp_pts_mask']) ] test_pipeline_multiscan = [ dict( type='LoadPointsFromFile', coord_type='DEPTH', shift_height=False, use_color=True, load_dim=6, use_dim=[0, 1, 2, 3, 4, 5]), dict(type='LoadAnnotations3D_', with_label_3d=False, with_bbox_3d=False, with_sp_mask_3d=True), dict( type='MultiScaleFlipAug3D', img_scale=(1333, 800), pts_scale_ratio=1, flip=False, transforms=[ dict(type='PointSample_', num_points=100000), dict( type='NormalizePointsColor_', color_mean=[127.5, 127.5, 127.5]) ]), dict(type='Pack3DDetInputs_', keys=['points', 'sp_pts_mask']) ]
3rscan dataset settings
data_root_3rscan = 'data/3rscan' dataset_type_3rscan = 'ThreeRScan_' data_prefix_3rscan = dict( pts='points', pts_instance_mask='instance_mask', pts_semantic_mask='semantic_mask', sp_pts_mask='super_points_spt')
train_pipeline_3rscan = [ dict( type='LoadPointsFromFile', coord_type='DEPTH', shift_height=False, use_color=True, load_dim=6, use_dim=[0, 1, 2, 3, 4, 5]), dict(type='LoadAnnotations3D_', with_label_3d=True, with_bbox_3d=True, with_sp_mask_3d=True), dict(type='PointSample_', num_points=100000), dict( type='RandomFlip3D', sync_2d=False, flip_ratio_bev_horizontal=0.5, flip_ratio_bev_vertical=0.5), dict( type='GlobalRotScaleTrans', rot_range=[0, 0], scale_ratio_range=[0.9, 1.1], translation_std=[0.1, 0.1, 0.1], shift_height=False), dict( type='NormalizePointsColor_', color_mean=[127.5, 127.5, 127.5]), dict( type='ElasticTransfrom', gran=[6, 20], mag=[40, 160], voxel_size=voxel_size, p=-1), dict( type='Pack3DDetInputs_', keys=['points', 'elastic_coords', 'gt_bboxes_3d', 'gt_labels_3d', 'sp_pts_mask']) ] test_pipeline_3rscan = [ dict( type='LoadPointsFromFile', coord_type='DEPTH', shift_height=False, use_color=True, load_dim=6, use_dim=[0, 1, 2, 3, 4, 5]), dict(type='LoadAnnotations3D_', with_label_3d=False, with_bbox_3d=False, with_sp_mask_3d=True), dict( type='MultiScaleFlipAug3D', img_scale=(1333, 800), pts_scale_ratio=1, flip=False, transforms=[ dict(type='PointSample_', num_points=100000), dict( type='NormalizePointsColor_', color_mean=[127.5, 127.5, 127.5]) ]), dict(type='Pack3DDetInputs_', keys=['points', 'sp_pts_mask']) ]
scannetpp dataset settings
data_root_scannetpp = 'data/scannetpp/bins' dataset_type_scannetpp = 'Scannetpp_' data_prefix_scannetpp = dict( pts='points', pts_instance_mask='instance_mask', pts_semantic_mask='semantic_mask', sp_pts_mask='super_points_spt')
train_pipeline_scannetpp = [ dict( type='LoadPointsFromFile', coord_type='DEPTH', shift_height=False, use_color=True, load_dim=6, use_dim=[0, 1, 2, 3, 4, 5]), dict(type='LoadAnnotations3D_', with_label_3d=True, with_bbox_3d=True, with_sp_mask_3d=True), dict(type='PointSample_', num_points=200000), dict( type='RandomFlip3D', sync_2d=False, flip_ratio_bev_horizontal=0.5, flip_ratio_bev_vertical=0.5), dict( type='GlobalRotScaleTrans', rot_range=[0, 0], scale_ratio_range=[0.9, 1.1], translation_std=[0.1, 0.1, 0.1], shift_height=False), dict( type='NormalizePointsColor_', color_mean=[127.5, 127.5, 127.5]), dict( type='ElasticTransfrom', gran=[6, 20], mag=[40, 160], voxel_size=voxel_size, p=-1), dict( type='Pack3DDetInputs_', keys=['points', 'elastic_coords', 'gt_bboxes_3d', 'gt_labels_3d', 'sp_pts_mask']) ] test_pipeline_scannetpp = [ dict( type='LoadPointsFromFile', coord_type='DEPTH', shift_height=False, use_color=True, load_dim=6, use_dim=[0, 1, 2, 3, 4, 5]), dict(type='LoadAnnotations3D_', with_label_3d=False, with_bbox_3d=False, with_sp_mask_3d=True), dict( type='MultiScaleFlipAug3D', img_scale=(1333, 800), pts_scale_ratio=1, flip=False, transforms=[ dict(type='PointSample_', num_points=200000), dict( type='NormalizePointsColor_', color_mean=[127.5, 127.5, 127.5]) ]), dict(type='Pack3DDetInputs_', keys=['points', 'sp_pts_mask']) ]
arkitscenes dataset settings
dataset_type_arkitscenes = 'ARKitScenesOfflineDataset' data_root_arkitscenes = 'data/arkitscenes' data_prefix_arkitscenes = dict( pts='offline_prepared_data', sp_pts_mask='super_points')
train_pipeline_arkitscenes = [ dict( type='LoadPointsFromFile', coord_type='DEPTH', shift_height=False, use_color=True, load_dim=6, use_dim=[0, 1, 2, 3, 4, 5]), dict(type='LoadAnnotations3D_', with_label_3d=True, with_bbox_3d=True, with_sp_mask_3d=True), dict(type='PointSample_', num_points=100000), dict( type='DenormalizePointsColor', color_mean=[0, 0, 0], color_std=[255, 255, 255]), dict( type='NormalizePointsColor_', color_mean=[127.5, 127.5, 127.5]), dict( type='RandomFlip3D', sync_2d=False, flip_ratio_bev_horizontal=0.5, flip_ratio_bev_vertical=0.5), dict( type='GlobalRotScaleTrans', rot_range=[-0.5, 0.5], scale_ratio_range=[0.9, 1.1], translation_std=[0.1, 0.1, 0.1], shift_height=False), dict( type='ElasticTransfrom', gran=[6, 20], mag=[40, 160], voxel_size=voxel_size, p=-1), dict( type='Pack3DDetInputs_', keys=['points', 'elastic_coords', 'gt_bboxes_3d', 'gt_labels_3d', 'sp_pts_mask']) ] test_pipeline_arkitscenes = [ dict( type='LoadPointsFromFile', coord_type='DEPTH', shift_height=False, use_color=True, load_dim=6, use_dim=[0, 1, 2, 3, 4, 5]), dict(type='LoadAnnotations3D_', with_label_3d=False, with_bbox_3d=False, with_sp_mask_3d=True), dict( type='MultiScaleFlipAug3D', img_scale=(1333, 800), pts_scale_ratio=1, flip=False, transforms=[ dict(type='PointSample_', num_points=100000), dict( type='DenormalizePointsColor', color_mean=[0, 0, 0], color_std=[255, 255, 255]), dict( type='NormalizePointsColor_', color_mean=[127.5, 127.5, 127.5]) ]), dict(type='Pack3DDetInputs_', keys=['points', 'sp_pts_mask']) ]
run settings
train_dataloader = dict(
batch_size=8,
num_workers=8,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=True),
dataset=dict(
type='ConcatDataset_',
datasets=
[dict(
type=dataset_type_scannet,
ann_file='/home/wanghui/DownLoads/unidet3d-master/data/scannet/scannet_infos_train.pkl',
data_prefix=data_prefix_scannet,
data_root=data_root_scannet,
metainfo=metainfo_scannet,
pipeline=train_pipeline_scannet,
ignore_index=max_class_scannet,
scene_idxs=None,
test_mode=False)]# +
# [dict(
# type=dataset_type_s3dis,
# data_root=data_root_s3dis,
# ann_file=f's3dis_sp_infos_Area_{i}.pkl',
# partition=0.33,
# pipeline=train_pipeline_s3dis,
# filter_empty_gt=True,
# data_prefix=data_prefix_s3dis,
# box_type_3d='Depth',
# backend_args=None) for i in train_area]
# [dict(
# type=dataset_type_multiscan,
# ann_file='multiscan_infos_train.pkl',
# partition=0.25,
# data_prefix=data_prefix_multiscan,
# data_root=data_root_multiscan,
# pipeline=train_pipeline_multiscan,
# test_mode=False)] +
# [dict(
# type=dataset_type_3rscan,
# ann_file='3rscan_infos_train.pkl',
# partition=0.15,
# data_prefix=data_prefix_3rscan,
# data_root=data_root_3rscan,
# pipeline=train_pipeline_3rscan,
# test_mode=False)] +
# [dict(
# type=dataset_type_scannetpp,
# ann_file='scannetpp_infos_train.pkl',
# partition=0.33,
# data_prefix=data_prefix_scannetpp,
# data_root=data_root_scannetpp,
# pipeline=train_pipeline_scannetpp,
# test_mode=False)] +
# [dict(
# type=dataset_type_arkitscenes,
# ann_file='arkitscenes_offline_infos_train.pkl',
# partition=0.08,
# data_prefix=data_prefix_arkitscenes,
# data_root=data_root_arkitscenes,
# pipeline=train_pipeline_arkitscenes,
# test_mode=False)]
))
val_dataloader = dict(
batch_size=1,
num_workers=1,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type='ConcatDataset_',
datasets=
# [dict(
# type=dataset_type_scannet,
# ann_file='/home/wanghui/DownLoads/unidet3d-master/data/scannet/scannet_infos_val.pkl',
# data_prefix=data_prefix_scannet,
# data_root=data_root_scannet,
# metainfo=metainfo_scannet,
# pipeline=test_pipeline_scannet,
# ignore_index=max_class_scannet,
# test_mode=True)] +
[dict(
type=dataset_type_s3dis,
data_root=data_root_s3dis,
ann_file=f's3dis_sp_infos_Area_{test_area}.pkl',
pipeline=test_pipeline_s3dis,
test_mode=True,
data_prefix=data_prefix_s3dis,
box_type_3d='Depth',
backend_args=None)]
# [dict(
# type=dataset_type_multiscan,
# ann_file='multiscan_infos_val.pkl',
# data_prefix=data_prefix_multiscan,
# data_root=data_root_multiscan,
# pipeline=test_pipeline_multiscan,
# test_mode=True)] +
# [dict(
# type=dataset_type_3rscan,
# ann_file='3rscan_infos_val.pkl',
# data_prefix=data_prefix_3rscan,
# data_root=data_root_3rscan,
# pipeline=test_pipeline_3rscan,
# test_mode=True)] +
# [dict(
# type=dataset_type_scannetpp,
# ann_file='scannetpp_infos_val.pkl',
# data_prefix=data_prefix_scannetpp,
# data_root=data_root_scannetpp,
# pipeline=test_pipeline_scannetpp,
# test_mode=True)] +
# [dict(
# type=dataset_type_arkitscenes,
# ann_file='arkitscenes_offline_infos_val.pkl',
# data_prefix=data_prefix_arkitscenes,
# data_root=data_root_arkitscenes,
# pipeline=test_pipeline_arkitscenes,
# test_mode=True)]
))
test_dataloader = val_dataloader
load_from = '/home/wanghui/DownLoads/unidet3d-master/work_dirs/tmp/unidet3d.pth'
test_evaluator = dict(type='IndoorMetric_', datasets=['scannet', 's3dis', 'multiscan', '3rscan', 'scannetpp', 'arkitscenes'], datasets_classes=[classes_scannet, classes_s3dis, classes_multiscan, classes_3rscan, classes_scannetpp, classes_arkitscenes])
val_evaluator = test_evaluator
optim_wrapper = dict( type='OptimWrapper', optimizer=dict(type='AdamW', lr=0.0001 * 2, weight_decay=0.05), clip_grad=dict(max_norm=10, norm_type=2))
param_scheduler = dict(type='PolyLR', begin=0, end=1024, power=0.9)
custom_hooks = [dict(type='EmptyCacheHook', after_iter=True)] default_hooks = dict( checkpoint=dict(interval=1, max_keep_ckpts=16))
train_cfg = dict( type='EpochBasedTrainLoop', max_epochs=1024, dynamic_intervals=[(1, 2), (1024 - 16, 1)]) val_cfg = dict(type='ValLoop') test_cfg = dict(type='TestLoop')
The following are the test results. +----------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------+---------+---------+---------+---------+ | table | 0.0554 | 0.7143 | 0.0076 | 0.2597 | | chair | 0.0688 | 0.6395 | 0.0119 | 0.2519 | | sofa | 0.0328 | 0.3636 | 0.0145 | 0.2727 | | bookcase | 0.0217 | 0.5806 | 0.0053 | 0.3088 | | board | 0.0530 | 0.3333 | 0.0361 | 0.1190 | +----------+---------+---------+---------+---------+ | Overall | 0.0464 | 0.5263 | 0.0150 | 0.2424 | +----------+---------+---------+---------+---------+ The reproduced results on the ScanNet dataset are consistent with those reported in the paper, whereas the performance degrades considerably when training exclusively on the S3DIS dataset. Could you suggest some approaches for resolving the problem of poor training performance on the S3DIS single dataset?
Hello, I have already downloaded the preprocessed dataset, and following your advice I re-downloaded it once again and configured it according to the instructions in the README. However, the results still remain consistently low without any improvement. Could you please explain what other possible reasons might cause such results?