FCAF3D: Fully Convolutional Anchor-Free 3D Object Detection

Thank you for your Greak work! I followed your dockerfile and everything was well-done. training and evaluation were great. but when i visualized my latest.pth there is nothing happened it seemed like processing my checkpoint file but i cannot understand what was done(any file didn't be produced).

should i additionally install more program to visualize something? here is my problem.

[email protected]:/mmdetection3d# python tools/test.py configs/fcaf3d/fcaf3d_sunrgbd-3d-10class.py /mmdetection3d/work_dirs/fcaf3d_sunrgbd-3d-10class/latest.pth --show --show-dir /mmdetection3d/work_dirs/fcaf3d_sunrgbd-3d-10class Use load_from_local loader [>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 5050/5050, 8.0 task/s, elapsed: 632s, ETA: 0sroo[email protected]:/mmdetection3d# [email protected]:/mmdetection3d#

I get some trouble when reproducing the results on the SUN RGB-Ddataset. When I train FCAF3D with the following command for 3 times:

CUDA_VISIBLE_DEVICES=5,6 bash tools/dist_train.sh configs/fcaf3d/fcaf3d_sunrgbd-3d-10class.py 2

I get results as follows:

+-------------+---------+---------+---------+---------+
| classes     | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+-------------+---------+---------+---------+---------+
| bed         | 0.8719  | 0.9845  | 0.6141  | 0.7456  |
| table       | 0.4641  | 0.9672  | 0.2555  | 0.6465  |
| sofa        | 0.6754  | 0.9745  | 0.4773  | 0.7703  |
| chair       | 0.7990  | 0.9602  | 0.6536  | 0.8373  |
| toilet      | 0.9059  | 0.9931  | 0.6496  | 0.8138  |
| desk        | 0.2875  | 0.9431  | 0.0774  | 0.4819  |
| dresser     | 0.3682  | 0.9358  | 0.1909  | 0.5780  |
| night_stand | 0.6696  | 0.9647  | 0.5330  | 0.8118  |
| bookshelf   | 0.2164  | 0.8156  | 0.0430  | 0.2376  |
| bathtub     | 0.7385  | 0.9388  | 0.4736  | 0.6939  |
+-------------+---------+---------+---------+---------+
| Overall     | 0.5997  | 0.9477  | 0.3968  | 0.6617  |
+-------------+---------+---------+---------+---------+

+-------------+---------+---------+---------+---------+
| classes     | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+-------------+---------+---------+---------+---------+
| bed         | 0.8724  | 0.9883  | 0.6265  | 0.7553  |
| table       | 0.4751  | 0.9706  | 0.2678  | 0.6520  |
| sofa        | 0.6932  | 0.9856  | 0.4864  | 0.7352  |
| chair       | 0.8149  | 0.9672  | 0.6757  | 0.8485  |
| toilet      | 0.9139  | 1.0000  | 0.6214  | 0.8000  |
| desk        | 0.2901  | 0.9511  | 0.0759  | 0.4862  |
| dresser     | 0.3544  | 0.9633  | 0.1908  | 0.6147  |
| night_stand | 0.6701  | 0.9608  | 0.5355  | 0.8118  |
| bookshelf   | 0.2548  | 0.8794  | 0.0505  | 0.2872  |
| bathtub     | 0.7533  | 0.9796  | 0.5332  | 0.6735  |
+-------------+---------+---------+---------+---------+
| Overall     | 0.6092  | 0.9646  | 0.4064  | 0.6665  |
+-------------+---------+---------+---------+---------+

+-------------+---------+---------+---------+---------+
| classes     | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 |
+-------------+---------+---------+---------+---------+
| bed         | 0.8681  | 0.9553  | 0.5910  | 0.7146  |
| table       | 0.4706  | 0.9497  | 0.2596  | 0.6427  |
| sofa        | 0.6792  | 0.9665  | 0.4032  | 0.7225  |
| chair       | 0.7962  | 0.9533  | 0.6424  | 0.8232  |
| toilet      | 0.9123  | 0.9931  | 0.6393  | 0.7862  |
| desk        | 0.2962  | 0.9283  | 0.0804  | 0.4718  |
| dresser     | 0.3405  | 0.9083  | 0.1785  | 0.5596  |
| night_stand | 0.6685  | 0.9294  | 0.5227  | 0.7529  |
| bookshelf   | 0.1860  | 0.7730  | 0.0451  | 0.2376  |
| bathtub     | 0.7124  | 0.9592  | 0.3878  | 0.6327  |
+-------------+---------+---------+---------+---------+
| Overall     | 0.5930  | 0.9316  | 0.3750  | 0.6344  |
+-------------+---------+---------+---------+---------+

Hello, an error ocurred when I tried to train the S3DIS dataset:

Traceback (most recent call last): File "tools/train.py", line 223, in main() File "tools/train.py", line 212, in main train_model( File "/mmdetection3d/mmdet3d/apis/train.py", line 27, in train_model train_detector( File "/opt/conda/lib/python3.8/site-packages/mmdet/apis/train.py", line 170, in train_detector runner.run(data_loaders, cfg.workflow) File "/opt/conda/lib/python3.8/site-packages/mmcv/runner/epoch_based_runner.py", line 127, in run epoch_runner(data_loaders[i], **kwargs) File "/opt/conda/lib/python3.8/site-packages/mmcv/runner/epoch_based_runner.py", line 50, in train self.run_iter(data_batch, train_mode=True, **kwargs) File "/opt/conda/lib/python3.8/site-packages/mmcv/runner/epoch_based_runner.py", line 29, in run_iter outputs = self.model.train_step(data_batch, self.optimizer, File "/opt/conda/lib/python3.8/site-packages/mmcv/parallel/data_parallel.py", line 67, in train_step return self.module.train_step(*inputs[0], **kwargs[0]) File "/opt/conda/lib/python3.8/site-packages/mmdet/models/detectors/base.py", line 237, in train_step losses = self(**data) File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 889, in _call_impl result = self.forward(*input, **kwargs) File "/opt/conda/lib/python3.8/site-packages/mmcv/runner/fp16_utils.py", line 98, in new_func return old_func(*args, **kwargs) File "/mmdetection3d/mmdet3d/models/detectors/base.py", line 58, in forward return self.forward_train(**kwargs) File "/mmdetection3d/mmdet3d/models/detectors/single_stage_sparse.py", line 48, in forward_train x = self.extract_feat(points, img_metas) File "/mmdetection3d/mmdet3d/models/detectors/single_stage_sparse.py", line 40, in extract_feat x = self.neck_with_head(x) File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 889, in _call_impl result = self.forward(*input, **kwargs) File "/mmdetection3d/mmdet3d/models/dense_heads/fcaf3d_neck_with_head.py", line 102, in forward x = self._prune(x, scores) File "/mmdetection3d/mmdet3d/models/dense_heads/fcaf3d_neck_with_head.py", line 124, in _prune prune_mask[permutation[mask]] = True RuntimeError: invalid shape dimension -255

I don't know how to solve it. Looking forward to your reply!

Thanks for your great work, when I read the code, I have some confuse.

In the file mmdet3d/models/dense_heads/fcaf3d_neck_with_head.py, we can see function loss_single for train, and get_box_single for evaluation. I just want to know, the origin of the box predicated by network is (0.5, 0.5, 0) or (0.5, 0.5, 0.5). Because I see that when we get the box_loss in function '_loss_single', the gt_box is convert to the class with origin(0.5, 0.5, 0.5), and the box predicated by the network do nothing(after _bbox_pred_to_bbox), then we get the loss_bbox. So we can think the p red box's origin is (0.5, 0.5, 0.5). But when we evaluate the network, the predicated box in function '_get_box_single' is convert to box with origin(0.5, 0.5, 0.5), then the boxes are evaluated by the function('indoor_eval') with gt_box which was convert to box with origin(0.5, 0.5, 0,5)

So I confused with above code, thus, when I using the box to test some others tasks, I have no idea to use the original box or convert it to the box with origin(0.5, 0.5, 0.5)

Hi I selected 10 small objects in SUNRGBD and I made those trained in this model. but, result showed only 0.1mAP( learning_rate = 0.001, epoch = 12, Objects were cup,bottle,bowl,book etc) I guess this model was designed for big objects not small things, right? then, how can I increase small objects' mAP? are there any parameters to do that?

Hi, awesome work! But I get some trouble when reproducing the results on the ScanNet dataset. Could you give me some advice? When I train FCAF3D with the following command:

bash tools/dist_train.sh configs/fcaf3d/fcaf3d_scannet-3d-18class.py 2

I get results as follows

+----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | cabinet | 0.5393 | 0.9059 | 0.3704 | 0.7366 | | bed | 0.8674 | 0.9259 | 0.7992 | 0.8519 | | chair | 0.9542 | 0.9854 | 0.8964 | 0.9415 | | sofa | 0.9118 | 0.9794 | 0.7914 | 0.9381 | | table | 0.7015 | 0.8971 | 0.6479 | 0.8029 | | door | 0.6186 | 0.9229 | 0.4389 | 0.7195 | | window | 0.5750 | 0.8865 | 0.3336 | 0.5993 | | bookshelf | 0.5852 | 0.8831 | 0.5157 | 0.7922 | | picture | 0.2571 | 0.5721 | 0.1564 | 0.3649 | | counter | 0.6106 | 0.8846 | 0.2338 | 0.5385 | | desk | 0.7157 | 0.9685 | 0.5472 | 0.8819 | | curtain | 0.5929 | 0.8955 | 0.4346 | 0.7015 | | refrigerator | 0.4901 | 0.8772 | 0.4010 | 0.8246 | | showercurtrain | 0.8306 | 0.9643 | 0.4340 | 0.7857 | | toilet | 1.0000 | 1.0000 | 0.9378 | 0.9655 | | sink | 0.8543 | 0.9592 | 0.5197 | 0.6837 | | bathtub | 0.8684 | 0.9032 | 0.8299 | 0.8710 | | garbagebin | 0.6439 | 0.8717 | 0.5762 | 0.7604 | +----------------+---------+---------+---------+---------+ | Overall | 0.7009 | 0.9046 | 0.5480 | 0.7644 | +----------------+---------+---------+---------+---------+

There is about 0.6 mAP gap between my result (70.09) and paper result (70.7) on AP 0.25 metric, and there is about 1.2 mAP gap between my result (54.8) and paper result (56.0) on AP 0.5 metric. Is this variance? or do I need to modify something to achieve comparable results with paper? Thanks ~

Hello,I successfully run your code,but I want to output all predict bbox coordinate position.I have tried print the bbox_list in single_stage_mono3d.py,but when i run the tools/test.py ,I found it did not call the single_stage_mono3d.py.Could you please help me ?

Hi, thank you for the great work!

I noticed that the code uses MinkowskiEngine as the backend. I am trying to replace the backend with Spconv, which is one of the different sparse convolution libraries for a research purpose.

I found out that fcaf3d is using MinkowkiGenerativeConvolutionTranspose in fcaf3d_neck_with_head.py. I am not sure how I could replace that function with spconv apis. How is MinkowkiGenerativeConvolutionTranspose different from MinkowskiConvolutionTranspose? The documentation says it produces new coordinates, but is this necessary, or can be replaced with MinkowskiConvolutionTranspose? If not, is there any workaround for this?

Thanks!

Hi, thanks for sharing your excellent work first! I have several questions.

I met the following error while testing. For my understanding, there are no annotations available while testing. So after a hard debug, I found that the second composer of test_pipeline called the 'annos' key of my test.pkl data, so I comment out this line dict(type='GlobalAlignment', rotation_axis=2), in the config file so that I can get a result. But actually I'm not sure will it cause any alignment problem on the results.

File "tools/test.py", line 214, in main() File "tools/test.py", line 184, in main outputs = single_gpu_test(model, data_loader, args.show, args.show_dir) File "/media/yangtian/SATA3/Workspace/fcaf3d-master/mmdet3d/apis/test.py", line 36, in single_gpu_test for i, data in enumerate(data_loader): File "/home/yangtian/anaconda3/envs/open-mmlab/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 517, in next data = self._next_data() File "/home/yangtian/anaconda3/envs/open-mmlab/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1199, in _next_data return self._process_data(data) File "/home/yangtian/anaconda3/envs/open-mmlab/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1225, in _process_data data.reraise() File "/home/yangtian/anaconda3/envs/open-mmlab/lib/python3.8/site-packages/torch/_utils.py", line 429, in reraise raise self.exc_type(msg) KeyError: Caught KeyError in DataLoader worker process 0. Original Traceback (most recent call last): File "/home/yangtian/anaconda3/envs/open-mmlab/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py", line 202, in _worker_loop data = fetcher.fetch(index) File "/home/yangtian/anaconda3/envs/open-mmlab/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch data = [self.dataset[idx] for idx in possibly_batched_index] File "/home/yangtian/anaconda3/envs/open-mmlab/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 44, in data = [self.dataset[idx] for idx in possibly_batched_index] File "/media/yangtian/SATA3/Workspace/fcaf3d-master/mmdet3d/datasets/custom_3d.py", line 354, in getitem return self.prepare_test_data(idx) File "/media/yangtian/SATA3/Workspace/fcaf3d-master/mmdet3d/datasets/scannet_dataset.py", line 135, in prepare_test_data example = self.pipeline(input_dict) File "/home/yangtian/anaconda3/envs/open-mmlab/lib/python3.8/site-packages/mmdet/datasets/pipelines/compose.py", line 41, in call data = t(data) File "/media/yangtian/SATA3/Workspace/fcaf3d-master/mmdet3d/datasets/pipelines/transforms_3d.py", line 475, in call assert 'axis_align_matrix' in input_dict['ann_info'].keys(),
KeyError: 'ann_info'

And after that, I wanna visualize the results, so I added--show --show_dir ./results/custom_test/ . However met another error:

File "tools/test.py", line 214, in main() File "tools/test.py", line 184, in main outputs = single_gpu_test(model, data_loader, args.show, args.show_dir) File "/media/yangtian/SATA3/Workspace/fcaf3d-master/mmdet3d/apis/test.py", line 46, in single_gpu_test model.module.show_results(data, result, out_dir) File "/media/yangtian/SATA3/Workspace/fcaf3d-master/mmdet3d/models/detectors/base.py", line 107, in show_results show_result(points, None, pred_bboxes, out_dir, file_name) TypeError: show_result() missing 2 required positional arguments: 'out_dir' and 'filename'

Not fixed yet. But I found that in show_result.py, the function show_result() takes gt_labels and pred_labels as well while in the testing procedure it doesn't pass this two values. I'm gonna continue debugging on it.

Finally, I'm wondering if there's a convenience way to get per point classification results?

Hello： There is a obvious difference between the actual evaluation results of the algorithm and those described in the paper, the cause of the problem has not been found yet, please give me some suggestions.

1.Evaluation results for the model provided in github of SamsungLabs/fcaf3d：(12 epoches) 1.1 sunrgbd

Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed.

Use load_from_local loader Use load_from_local loader [ ] 0/5050, elapsed: [>>>>>>>>>>>>>>>>] 5050/5050, 15.9 task/s, elapsed: 318s, ETA: 0s +-------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +-------------+---------+---------+---------+---------+ | bed | 0.0496 | 0.7612 | 0.0094 | 0.5883 | | table | 0.1570 | 0.8514 | 0.1222 | 0.7909 | | sofa | 0.2261 | 0.8469 | 0.1440 | 0.7735 | | chair | 0.1985 | 0.7662 | 0.1671 | 0.7272 | | toilet | 0.3586 | 0.9379 | 0.2778 | 0.8552 | | desk | 0.0970 | 0.7641 | 0.0665 | 0.6982 | | dresser | 0.2063 | 0.9128 | 0.1956 | 0.8670 | | night_stand | 0.4174 | 0.9608 | 0.4127 | 0.9451 | | bookshelf | 0.1585 | 0.8865 | 0.1489 | 0.8511 | | bathtub | 0.3658 | 0.9592 | 0.3561 | 0.8571 | +-------------+---------+---------+---------+---------+ | Overall | 0.2235 | 0.8647 | 0.1900 | 0.7954 | +-------------+---------+---------+---------+---------+ {'bed_AP_0.25': 0.049614179879426956, 'table_AP_0.25': 0.15698249638080597, 'sofa_AP_0.25': 0.2261233627796173, 'chair_AP_0.25': 0.19846875965595245, 'toilet_AP_0.25': 0.35864582657814026, 'desk_AP_0.25': 0.09701932221651077, 'dresser_AP_0.25': 0.20626720786094666, 'night_stand_AP_0.25': 0.41742968559265137, 'bookshelf_AP_0.25': 0.15852327644824982, 'bathtub_AP_0.25': 0.3657997250556946, 'mAP_0.25': 0.22348737716674805, 'bed_rec_0.25': 0.7611650485436893, 'table_rec_0.25': 0.8513628620102215, 'sofa_rec_0.25': 0.84688995215311, 'chair_rec_0.25': 0.7661741214057508, 'toilet_rec_0.25': 0.9379310344827586, 'desk_rec_0.25': 0.7640807651434643, 'dresser_rec_0.25': 0.9128440366972477, 'night_stand_rec_0.25': 0.9607843137254902, 'bookshelf_rec_0.25': 0.8865248226950354, 'bathtub_rec_0.25': 0.9591836734693877, 'mAR_0.25': 0.8646940630326154, 'bed_AP_0.50': 0.009411284700036049, 'table_AP_0.50': 0.12223674356937408, 'sofa_AP_0.50': 0.14403118193149567, 'chair_AP_0.50': 0.1670922487974167, 'toilet_AP_0.50': 0.27775609493255615, 'desk_AP_0.50': 0.06645272672176361, 'dresser_AP_0.50': 0.19564399123191833, 'night_stand_AP_0.50': 0.41268840432167053, 'bookshelf_AP_0.50': 0.14886672794818878, 'bathtub_AP_0.50': 0.35609957575798035, 'mAP_0.50': 0.19002790749073029, 'bed_rec_0.50': 0.5883495145631068, 'table_rec_0.50': 0.790885860306644, 'sofa_rec_0.50': 0.773524720893142, 'chair_rec_0.50': 0.7272364217252396, 'toilet_rec_0.50': 0.8551724137931035, 'desk_rec_0.50': 0.6981934112646121, 'dresser_rec_0.50': 0.8669724770642202, 'night_stand_rec_0.50': 0.9450980392156862, 'bookshelf_rec_0.50': 0.851063829787234, 'bathtub_rec_0.50': 0.8571428571428571, 'mAR_0.50': 0.7953639545755846}

1.2 scannet

Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed.

Use load_from_local loader Use load_from_local loader [ ] 0/312, elapsed: 0[>>>>>>>>>>>>>>>>>>>] 312/312, 10.4 task/s, elapsed: 30s, ETA: 0s +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | cabinet | 0.0077 | 0.2742 | 0.0052 | 0.2419 | | bed | 0.0003 | 0.0370 | 0.0000 | 0.0247 | | chair | 0.0193 | 0.2661 | 0.0138 | 0.2354 | | sofa | 0.0015 | 0.0825 | 0.0005 | 0.0825 | | table | 0.0016 | 0.1429 | 0.0011 | 0.1229 | | door | 0.0104 | 0.2441 | 0.0079 | 0.2313 | | window | 0.0028 | 0.1773 | 0.0021 | 0.1631 | | bookshelf | 0.0081 | 0.1558 | 0.0042 | 0.1429 | | picture | 0.0028 | 0.2658 | 0.0025 | 0.2568 | | counter | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | desk | 0.0012 | 0.0866 | 0.0010 | 0.0709 | | curtain | 0.0000 | 0.0448 | 0.0000 | 0.0448 | | refrigerator | 0.0000 | 0.0351 | 0.0000 | 0.0000 | | showercurtrain | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | toilet | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | sink | 0.0000 | 0.0102 | 0.0000 | 0.0000 | | bathtub | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | garbagebin | 0.0048 | 0.2113 | 0.0039 | 0.1811 | +----------------+---------+---------+---------+---------+ | Overall | 0.0034 | 0.1130 | 0.0024 | 0.0999 | +----------------+---------+---------+---------+---------+ {'cabinet_AP_0.25': 0.007672073319554329, 'bed_AP_0.25': 0.0003270927118137479, 'chair_AP_0.25': 0.019300922751426697, 'sofa_AP_0.25': 0.0015425414312630892, 'table_AP_0.25': 0.0016294954111799598, 'door_AP_0.25': 0.010358083993196487, 'window_AP_0.25': 0.0027569225057959557, 'bookshelf_AP_0.25': 0.008131678216159344, 'picture_AP_0.25': 0.002778079127892852, 'counter_AP_0.25': 0.0, 'desk_AP_0.25': 0.0011750475969165564, 'curtain_AP_0.25': 2.2466720110969618e-05, 'refrigerator_AP_0.25': 1.3386352293309756e-05, 'showercurtrain_AP_0.25': 0.0, 'toilet_AP_0.25': 0.0, 'sink_AP_0.25': 1.440034111510613e-06, 'bathtub_AP_0.25': 0.0, 'garbagebin_AP_0.25': 0.00475729675963521, 'mAP_0.25': 0.003359251655638218, 'cabinet_rec_0.25': 0.27419354838709675, 'bed_rec_0.25': 0.037037037037037035, 'chair_rec_0.25': 0.26608187134502925, 'sofa_rec_0.25': 0.08247422680412371, 'table_rec_0.25': 0.14285714285714285, 'door_rec_0.25': 0.24411134903640258, 'window_rec_0.25': 0.1773049645390071, 'bookshelf_rec_0.25': 0.15584415584415584, 'picture_rec_0.25': 0.26576576576576577, 'counter_rec_0.25': 0.0, 'desk_rec_0.25': 0.08661417322834646, 'curtain_rec_0.25': 0.04477611940298507, 'refrigerator_rec_0.25': 0.03508771929824561, 'showercurtrain_rec_0.25': 0.0, 'toilet_rec_0.25': 0.0, 'sink_rec_0.25': 0.01020408163265306, 'bathtub_rec_0.25': 0.0, 'garbagebin_rec_0.25': 0.21132075471698114, 'mAR_0.25': 0.11298182832749847, 'cabinet_AP_0.50': 0.005249418318271637, 'bed_AP_0.50': 7.024568276392529e-06, 'chair_AP_0.50': 0.013795703649520874, 'sofa_AP_0.50': 0.0005232820985838771, 'table_AP_0.50': 0.001064108102582395, 'door_AP_0.50': 0.007914243265986443, 'window_AP_0.50': 0.0021169877145439386, 'bookshelf_AP_0.50': 0.0042017120867967606, 'picture_AP_0.50': 0.0025250331964343786, 'counter_AP_0.50': 0.0, 'desk_AP_0.50': 0.0010463166981935501, 'curtain_AP_0.50': 2.2466720110969618e-05, 'refrigerator_AP_0.50': 0.0, 'showercurtrain_AP_0.50': 0.0, 'toilet_AP_0.50': 0.0, 'sink_AP_0.50': 0.0, 'bathtub_AP_0.50': 0.0, 'garbagebin_AP_0.50': 0.003941362723708153, 'mAP_0.50': 0.0023559811525046825, 'cabinet_rec_0. 50': 0.24193548387096775, 'bed_rec_0.50': 0.024691358024691357, 'chair_rec_0.50': 0.23538011695906433, 'sofa_rec_0.50': 0.08247422680412371, 'table_rec_0.50': 0.12285714285714286, 'door_rec_0.50': 0.23126338329764454, 'window_rec_0.50': 0.16312056737588654, 'bookshelf_rec_0.50': 0.14285714285714285, 'picture_rec_0.50': 0.25675675675675674, 'counter_rec_0.50': 0.0, 'desk_rec_0.50': 0.07086614173228346, 'curtain_rec_0. 50': 0.04477611940298507, 'refrigerator_rec_0.50': 0.0, 'showercurtrain_rec_0.50': 0.0, 'toilet_rec_0.50': 0.0, 'sink_rec_0.50': 0.0, 'batht ub_rec_0.50': 0.0, 'garbagebin_rec_0.50': 0.1811320754716981, 'mAR_0.50': 0.0998950286339104}

2.Evaluation results for the model trained by myself：(12 epoches) 2.1 sunrgbd

Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed.

Use load_from_local loader Use load_from_local loader [ ] 0/5050, elapsed: [>>>>>>>>>>>>>>>] 5050/5050, 0.3 task/s, elapsed: 16300s, ETA: 0s +-------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +-------------+---------+---------+---------+---------+ | bed | 0.5560 | 0.9903 | 0.4873 | 0.8738 | | table | 0.3110 | 0.9817 | 0.2184 | 0.7785 | | sofa | 0.4583 | 0.9841 | 0.4016 | 0.8740 | | chair | 0.5615 | 0.9633 | 0.4967 | 0.8643 | | toilet | 0.6838 | 0.9931 | 0.6078 | 0.8966 | | desk | 0.1938 | 0.9644 | 0.0907 | 0.6791 | | dresser | 0.2366 | 0.9541 | 0.1876 | 0.7752 | | night_stand | 0.4379 | 0.9765 | 0.3945 | 0.8824 | | bookshelf | 0.2321 | 0.9184 | 0.0987 | 0.5426 | | bathtub | 0.3912 | 0.9796 | 0.3296 | 0.8367 | +-------------+---------+---------+---------+---------+ | Overall | 0.4062 | 0.9705 | 0.3313 | 0.8003 | +-------------+---------+---------+---------+---------+ {'bed_AP_0.25': 0.5559849143028259, 'table_AP_0.25': 0.3109968602657318, 'sofa_AP_0.25': 0.4582793712615967, 'chair_AP_0.25': 0.5614634156227112, 'toilet_AP_0.25': 0.6837515830993652, 'desk_AP_0.25': 0.1938285529613495, 'dresser_AP_0.25': 0.23655451834201813, 'night_stand_AP_0.25': 0.43787485361099243, 'bookshelf_AP_0.25': 0.2321251481771469, 'bathtub_AP_0.25': 0.3912363648414612, 'mAP_0.25': 0.4062095582485199, 'bed_rec_0.25': 0.9902912621359223, 'table_rec_0.25': 0.981686541737649, 'sofa_rec_0.25': 0.9840510366826156, 'chair_rec_0.25': 0.963258785942492, 'toilet_rec_0.25': 0.993103448275862, 'desk_rec_0.25': 0.9643995749202976, 'dresser_rec_0.25': 0.9541284403669725, 'night_stand_rec_0.25': 0.9764705882352941, 'bookshelf_rec_0.25': 0.9184397163120568, 'bathtub_rec_0.25': 0.9795918367346939, 'mAR_0.25': 0.9705421231343856, 'bed_AP_0.50': 0.4872896671295166, 'table_AP_0.50': 0.21839171648025513, 'sofa_AP_0.50': 0.40156134963035583, 'chair_AP_0.50': 0.4966593384742737, 'toilet_AP_0.50': 0.607752799987793, 'desk_AP_0.50': 0.09067006409168243, 'dresser_AP_0.50': 0.18762624263763428, 'night_stand_AP_0.50': 0.39452967047691345, 'bookshelf_AP_0.50': 0.09874459356069565, 'bathtub_AP_0.50': 0.32958272099494934, 'mAP_0.50': 0.33128082752227783, 'bed_rec_0.50': 0.8737864077669902, 'table_rec_0.50': 0.778534923339012, 'sofa_rec_0.50': 0.8740031897926634, 'chair_rec_0.50': 0.8643170926517572, 'toilet_rec_0.50': 0.896551724137931, 'desk_rec_0.50': 0.6790648246546227, 'dresser_rec_0.50': 0.7752293577981652, 'night_stand_rec_0.50': 0.8823529411764706, 'bookshelf_rec_0.50': 0.5425531914893617, 'bathtub_rec_0.50': 0.8367346938775511, 'mAR_0.50': 0.8003128346684527}

2.2 scannet

Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tun$ the variable for optimal performance in your application as needed.

Use load_from_local loader Use load_from_local loader [ ] 0/312, elapsed: 0[>>>>>>> ] 116/312, 0.2 task/s, elapsed[ ] 117/312, 0.2 t$ sk/s, elapsed: 547s,[ ] 118/312, 0.2 task/s, elapsed: 547s, ETA: 89[>>>>>>>>>>>>> ] 240/312, [> ] 241/312, 0.2 task/s, elapsed: 1262$ [> ] 242/312, 0.2 task/s, elapsed: 1262s[> ] 243/312, 0.2 task/s, elap[>>>>>>>>>>>>>>>>>>] 312/312, 0.2 task/s, elapsed: 1531s, ETA: 0s +----------------+---------+---------+---------+---------+ | classes | AP_0.25 | AR_0.25 | AP_0.50 | AR_0.50 | +----------------+---------+---------+---------+---------+ | cabinet | 0.0089 | 0.2500 | 0.0071 | 0.2419 | | bed | 0.0022 | 0.0494 | 0.0005 | 0.0123 | | chair | 0.0195 | 0.2661 | 0.0131 | 0.2383 | | sofa | 0.0009 | 0.0825 | 0.0000 | 0.0309 | | table | 0.0030 | 0.1400 | 0.0021 | 0.1257 | | door | 0.0072 | 0.2698 | 0.0066 | 0.2548 | | window | 0.0030 | 0.1844 | 0.0020 | 0.1525 | | bookshelf | 0.0041 | 0.1429 | 0.0031 | 0.1429 | | picture | 0.0049 | 0.2523 | 0.0045 | 0.2342 | | counter | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | desk | 0.0041 | 0.0709 | 0.0041 | 0.0551 | | curtain | 0.0001 | 0.0597 | 0.0001 | 0.0597 | | refrigerator | 0.0001 | 0.0877 | 0.0001 | 0.0877 | | showercurtrain | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | toilet | 0.0005 | 0.0345 | 0.0001 | 0.0345 | | sink | 0.0000 | 0.0408 | 0.0000 | 0.0408 | | bathtub | 0.0000 | 0.0000 | 0.0000 | 0.0000 | | garbagebin | 0.0076 | 0.2038 | 0.0063 | 0.1887 | +----------------+---------+---------+---------+---------+ | Overall | 0.0037 | 0.1186 | 0.0028 | 0.1056 | +----------------+---------+---------+---------+---------+ {'cabinet_AP_0.25': 0.0088879419490695, 'bed_AP_0.25': 0.002196365036070347, 'chair_AP_0.25': 0.019513951614499092, 'sofa_AP_0.25': 0.00092$ 0919025614858, 'table_AP_0.25': 0.0029890856239944696, 'door_AP_0.25': 0.007219596300274134, 'window_AP_0.25': 0.002968191634863615, 'books$ elf_AP_0.25': 0.0041231452487409115, 'picture_AP_0.25': 0.004887890536338091, 'counter_AP_0.25': 0.0, 'desk_AP_0.25': 0.004088526125997305, 'curtain_AP_0.25': 0.0001449216069886461, 'refrigerator_AP_0.25': 0.00010905037925112993, 'showercurtrain_AP_0.25': 0.0, 'toilet_AP_0.25': $ .0005089639453217387, 'sink_AP_0.25': 3.358132016728632e-05, 'bathtub_AP_0.25': 0.0, 'garbagebin_AP_0.25': 0.007584888022392988, 'mAP_0.25'$ 0.003676677355542779, 'cabinet_rec_0.25': 0.25, 'bed_rec_0.25': 0.04938271604938271, 'chair_rec_0.25': 0.26608187134502925, 'sofa_rec_0.25$ : 0.08247422680412371, 'table_rec_0.25': 0.14, 'door_rec_0.25': 0.2698072805139186, 'window_rec_0.25': 0.18439716312056736, 'bookshelf_rec_0 .25': 0.14285714285714285, 'picture_rec_0.25': 0.25225225225225223, 'counter_rec_0.25': 0.0, 'desk_rec_0.25': 0.07086614173228346, 'curtain_ rec_0.25': 0.05970149253731343, 'refrigerator_rec_0.25': 0.08771929824561403, 'showercurtrain_rec_0.25': 0.0, 'toilet_rec_0.25': 0.034482758 620689655, 'sink_rec_0.25': 0.04081632653061224, 'bathtub_rec_0.25': 0.0, 'garbagebin_rec_0.25': 0.2037735849056604, 'mAR_0.25': 0.118589569 75081056, 'cabinet_AP_0.50': 0.007079910486936569, 'bed_AP_0.50': 0.0005367686389945447, 'chair_AP_0.50': 0.013134335167706013, 'sofa_AP_0.5 0': 4.525698022916913e-05, 'table_AP_0.50': 0.0021134275011718273, 'door_AP_0.50': 0.00658177025616169, 'window_AP_0.50': 0.0019997931085526 943, 'bookshelf_AP_0.50': 0.0031221157405525446, 'picture_AP_0.50': 0.004526467993855476, 'counter_AP_0.50': 0.0, 'desk_AP_0.50': 0.00407800 916582346, 'curtain_AP_0.50': 0.0001449216069886461, 'refrigerator_AP_0.50': 0.0001018800976453349, 'showercurtrain_AP_0.50': 0.0, 'toilet_A P_0.50': 5.409148434409872e-05, 'sink_AP_0.50': 3.358132016728632e-05, 'bathtub_AP_0.50': 0.0, 'garbagebin_AP_0.50': 0.006303954869508743, ' mAP_0.50': 0.0027697933837771416, 'cabinet_rec_0.50': 0.24193548387096775, 'bed_rec_0.50': 0.012345679012345678, 'chair_rec_0.50': 0.2383040 9356725146, 'sofa_rec_0.50': 0.030927835051546393, 'table_rec_0.50': 0.12571428571428572, 'door_rec_0.50': 0.25481798715203424, 'window_rec_ 0.50': 0.1524822695035461, 'bookshelf_rec_0.50': 0.14285714285714285, 'picture_rec_0.50': 0.23423423423423423, 'counter_rec_0.50': 0.0, 'des k_rec_0.50': 0.05511811023622047, 'curtain_rec_0.50': 0.05970149253731343, 'refrigerator_rec_0.50': 0.08771929824561403, 'showercurtrain_rec

@filaPro hi thanks for opensourcing code base , does this architecture been trained on KITTI , ARGOVerse, Nuscense dataset? if not what changes have to be made for the training on those dataset Thanks in advance

--resume-from option in train.py works only once due to the bug regarding epoch update in mmcv-full=1.3.8. The bug is solved in 1.3.9. so I suggest to update requirement in mminstal.txt accordingly.

I want to use the trained model to test point cloud,but I meet following question: Traceback (most recent call last): File "/media/ketizu/086C03DE086C03DE/project1/fcaf3d-master/demo/pcd_demo.py", line 102, in main() File "/media/ketizu/086C03DE086C03DE/project1/fcaf3d-master/demo/pcd_demo.py", line 90, in main show_result_meshlab( File "/media/ketizu/086C03DE086C03DE/project1/fcaf3d-master/mmdet3d/apis/inference.py", line 481, in show_result_meshlab file_name = show_det_result_meshlab(data, result, out_dir, score_thr, File "/media/ketizu/086C03DE086C03DE/project1/fcaf3d-master/mmdet3d/apis/inference.py", line 324, in show_det_result_meshlab show_result( TypeError: show_result() missing 2 required positional arguments: 'out_dir' and 'filename'

Process finished with exit code 1