JT-Sun

Hi, I trained the model for 70 epoh on the HO3D dataset, but the result sames a far cry from your results. Do anyone know the reason? [email protected] = 0.345...

Thanks for your reply! Thanks for your great work!

python adaptor_meanc_detac.py --version trainval --split train --map_model StreamMapNet --dataroot /home/xx/data/nuscenes/ --index_file /home/xx/data/adaptor_files/traj_scene_frame_full_train.pkl --map_file /home/xx/data/adaptor_files/mapping_results_train_streamnew1_1e-4.pickle --gt_map_file /home/xx/data/adaptor_files/gt_full_train.pickle --save_path /home/xx/data/trj_data/streamnew_1e-4 This is my command I find that like scene-2254.pkl , which sample_token...

And I also search this sample_token in my mapping_results_train_streamnew1_1e-4.pickle, it also doesn't contain this token. What's wrong with this problem, please help me! thanks!

I confuse that whether i get the mapping_results_train.pickle from the following config: data_root = '/home/xx/data/nuscenes/' data_ann = '/home/xx/data/processed/stream/' data_ann_new = '/home/xx/data/processed/stream_new/' data = dict( samples_per_gpu=batch_size, workers_per_gpu=4, train=dict( type='NuscDataset', data_root=data_root, ann_file=data_ann_new...

I have try this way, but the answer is also sample_token is not '53f5977684e14cb0a28f383fee1dd433' contained in new mapping_results_train.pickle.

I wanted to make sure you said retrain, then train=dict( type='NuscDataset', data_root=data_root, ann_file= data_ann + 'nuscenes_map_infos_train.pkl' meta=meta, roi_size=roi_size, cat2id=cat2id, pipeline=train_pipeline, seq_split_num=1, ), Then test=dict( type='NuscDataset', data_root=data_root, #ann_file=data_ann + 'nuscenes_map_infos_val.pkl', ann_file=data_ann...

export PYTHONPATH="${PYTHONPATH}:/MapUncertaintyPrediction/StreamMapNet_modified" python tools/train.py \ plugin/configs/nusc_newsplit_480_60x30_24e.py \ --deterministic \ --no-validate because I use the command as you list，so I just do the test about change the test——config dict

Thank you for your patience! python tools/nuscenes_converter.py --data-root ../nuscenes --newsplit --dest_path ../processed/stream_new python tools/nuscenes_converter.py --data-root ../nuscenes --dest_path ../processed/stream MapUncertaintyPrediction ├── nuscenes/ ├── processed/ │ ├── maptr/ │ ├── maptrv2/ │...

Thank you very much for your reply. Since StreamMapNet's training set contains fewer scenes, I would like to know what was done when your method trained HiVT and denseTNT, because...