| Submission name | |||||||||||
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| Submission time (UTC) | Oct. 8, 2023, 7 a.m. | ||||||||||
| User | SEU_WYL | ||||||||||
| Task | Model-based 6D localization of seen objects | ||||||||||
| Dataset | LM-O | ||||||||||
| Description | |||||||||||
| Evaluation scores |
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| User | SEU_WYL |
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| Publication | |
| Implementation | |
| Training image modalities | RGB |
| Test image modalities | RGB |
| Description | Submitted to: BOP Challenge 2023 Training data: real + provided PBR Training Datasets of LM-O are real and PBR-based datasets. Used 3D models: default Notes: Setting: One network (ResNet34) per object was trained (without Refinement) 2D Bounding Box: Faster-RCNN (ZebraPose) Batch Size: 24 Epoch: 38k Learning Rate: 0.0002 |
| Computer specifications |