Submission name | 4th. stage | ||||||||||||||||||||||||||
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Submission time (UTC) | Sept. 28, 2023, 3:34 a.m. | ||||||||||||||||||||||||||
User | felix.stillger | ||||||||||||||||||||||||||
Task | Model-based 2D segmentation of unseen objects | ||||||||||||||||||||||||||
Dataset | ITODD | ||||||||||||||||||||||||||
Description | |||||||||||||||||||||||||||
Evaluation scores |
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User | felix.stillger |
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Publication | |
Implementation | |
Training image modalities | RGB |
Test image modalities | RGB |
Description | Training: There is no training step. Onboarding: For each object, 43 random pre-rendered images from the "train_pbr" dataset are selected. The masks of these objects are then extracted and encoded using CLIP. This data is input into a simple expert binary classifier, trained for each object. Test: During testing, Fastsam-s extracts masks from a test image, and the object's ID is determined by the expert binary classifiers. |
Computer specifications | RTX 3090, AMD Ryzen 9 3900X 12-Core Processor |