Fashionpedia: Ontology, Segmentation, and an Attribute Localization Dataset

Menglin Jia*, Mengyun Shi*, Mikhail Sirotenko*, Yin Cui*, Claire Cardie, Bharath Hariharan, Hartwig Adam, Serge Belongie (*equal contribution) [dataset] [arXiv]

We release the checkpoints of Attribute-Mask R-CNN model with ResNet-FPN and SpineNet backbone.

Other code including data conversion, model training and inference will be released soon.

Checkpoint

Object detection and instance segmentation on Fashionpedia:

backbone	input size	lr sched	FLOPs	Params	box AP IoU / IoU+F1	mask AP IoU / IoU+F1	download
ResNet-50 FPN	1024	1x	296.7B	46.4M	38.7 / 26.6	34.3 / 25.5	N/A
ResNet-50 FPN	1024	2x	296.7B	46.4M	41.6 / 29.3	38.1 / 28.5	N/A
ResNet-50 FPN	1024	3x	296.7B	46.4M	43.4 / 30.7	39.2 / 29.5	ckpt \| config
ResNet-50 FPN	1024	6x	296.7B	46.4M	42.9 / 31.2	38.9 / 30.2	N/A
ResNet-101 FPN	1024	1x	374.3B	65.4M	41.0 / 28.6	36.7 / 27.6	N/A
ResNet-101 FPN	1024	2x	374.3B	65.4M	43.5 / 31.0	39.2 / 29.8	N/A
ResNet-101 FPN	1024	3x	374.3B	65.4M	44.9 / 32.8	40.7 / 31.4	ckpt \| config
ResNet-101 FPN	1024	6x	374.3B	65.4M	44.3 / 32.9	39.7 / 31.3	N/A
SpineNet-49	1024	6x	267.2B	40.8M	43.7 / 32.4	39.6 / 31.4	ckpt \| config
SpineNet-96	1024	6x	314.0B	55.2M	46.4 / 34.0	41.2 / 31.8	ckpt \| config
SpineNet-143	1280	6x	498.0B	79.2M	48.7 / 35.7	43.1 / 33.3	ckpt \| config

For calculating AP (IoU without attribute prediction or IoU + F1 with attribute prediction), please refer to the [Fahionpedia API].

Citation

@inproceedings{jia2020fashionpedia,
  title={Fashionpedia: Ontology, Segmentation, and an Attribute Localization Dataset},
  author={Jia, Menglin and Shi, Mengyun and Sirotenko, Mikhail and Cui, Yin and Cardie, Claire and Hariharan, Bharath and Adam, Hartwig and Belongie, Serge},
  booktitle={European Conference on Computer Vision (ECCV)},
  year={2020}
}