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Res152c4 on 4 datasets seems not right #23
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Hi, I've managed to run this pretrained model, and the visualization via demo_image.py looks fine.
I've successfully run either tools/test_sg_net.py and tools/demo/demo_image.py, please let me know if I've missing any steps |
Did you run the code on your own dataset? I met some problems, can you help me? |
VinVL's DOWNLOAD.md says
We also provide the X152-C4 objecte detection config file and pretrained model on the merged four datasets (COCO with stuff, Visual Genome, Objects365 and Open Images). The labelmap to decode the 1848 can be found here. The first 1594 classes are exactly VG classes, with the same order. The map from COCO vocabulary to this merged vocabulary can be found here. The map from Objects365 vocabulary to this merged vocabulary can be found here. The map from OpenImages V5 vocabulary to this merged vocabulary can be found here.
But I am wondering how to run this pretrained model?
Obviously Scene Graph Benchmark can't run this pre-trained model since the configuration file is not compatible with that package. I force to change the config file (deleting options one by one until yacs accepts), so I can manage to run the pre-trained model, but results are not right because the number of boxes are too small compared to other detector (which should not be) ...
Any help please?
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