Instructions to use torch-pointcloud/second-multihead.nuscenes.openpcdet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- torch-pointcloud
How to use torch-pointcloud/second-multihead.nuscenes.openpcdet with torch-pointcloud:
import torch_pointcloud as tp model = tp.create_model("second-multihead.nuscenes.openpcdet", pretrained=True) - Notebooks
- Google Colab
- Kaggle
Update config.json for second-multihead.nuscenes.openpcdet
Browse files- config.json +140 -0
config.json
ADDED
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{
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"library_name": "torch-pointcloud",
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"name": "second-multihead.nuscenes.openpcdet",
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"task": "detection",
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"hparams": {
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"in_channels": 5,
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"num_classes": 10,
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"voxel_size": [
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0.1,
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0.1,
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0.2
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],
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"point_cloud_range": [
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-51.2,
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-51.2,
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-5.0,
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51.2,
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51.2,
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3.0
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],
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"anchor_sizes": [
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[
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4.63,
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1.97,
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1.74
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],
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[
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6.93,
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2.51,
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2.84
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],
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[
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6.37,
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2.85,
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3.19
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],
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[
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10.5,
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2.94,
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3.47
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],
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[
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12.29,
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2.9,
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3.87
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],
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[
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0.5,
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2.53,
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0.98
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],
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[
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2.11,
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0.77,
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1.47
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],
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[
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1.7,
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0.6,
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1.28
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],
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[
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0.73,
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0.67,
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1.77
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],
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[
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0.41,
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0.41,
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1.07
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]
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],
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"anchor_bottom_heights": [
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-0.95,
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-0.6,
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-0.225,
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-0.085,
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0.115,
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-1.33,
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-1.085,
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-1.18,
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-0.935,
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-1.285
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],
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"head_class_groups": [
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[
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0
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],
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[
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1,
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],
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],
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],
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],
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[
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]
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],
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"feature_map_stride": 8,
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"norm_kwargs": {
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"eps": 0.001,
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"momentum": 0.01
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}
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},
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"input_keys": [
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"voxel",
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"pos_voxel",
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"voxel_num_points",
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"batch_pos_voxel"
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],
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"dataset": "nuscenes",
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"metrics": {
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"mAP": 50.75,
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"NDS": 61.89
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},
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"classes": [
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"car",
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"truck",
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"construction_vehicle",
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"bus",
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"trailer",
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"barrier",
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"motorcycle",
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"bicycle",
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"pedestrian",
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"traffic_cone"
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],
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"author": "openpcdet",
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"license": "Apache-2.0"
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}
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