PULC Model Zoo

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The PULC model zoo is provided here, mainly providing indicators, model storage size, and download links of the model. The pre-trained model can be used for fine-tuning training, and the inference model can be directly used for prediction and deployment.

Model name	Model Description	Metrics	Storage Size	Latency	Download Address
person_exists	Human Exists Classification	96.23	7.0M	2.58ms	inference model / pretrained model
person_attribute	Pedestrian Attribute Classification	78.59	7.2M	2.01ms	inference model / pretrained model
safety_helmet	Classification of Whether Wearing Safety Helmet	99.38	7.1M	2.03ms	inference model / pretrained model
traffic_sign	Traffic Sign Classification	98.35	8.2M	2.10ms	inference model / pretrained model
vehicle_attribute	Vehicle Attribute Classification	90.81	7.2M	2.36ms	inference model / pretrained model
car_exists	Car Exists Classification	95.92	7.1M	2.38ms	inference model / pretrained model
text_image_orientation	Text Image Orientation Classification	99.06	7.1M	2.16ms	inference model / pretrained model
textline_orientation	Text-line Orientation Classification	96.01	7.0M	2.72ms	inference model / pretrained model
language_classification	Language Classification	99.26	7.1M	2.58ms	inference model / pretrained model

Note：

• The backbone of all the above models is PPLCNet_x1_0. The different sizes of some models are caused by the different output sizes of the classification layer. The inference time is tested on the Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz. During the test process, the MKLDNN acceleration strategy is turned on, and the number of threads is 10. There will be slight fluctuations during the speed test process.

• The evaluation indicators of person_exists, safety_helmet, and car_exists are TprAtFpr. The evaluation indicators of person_attribute and vehicle_attribute are ma. The evaluation indicators of traffic_sign, text_image_orientation, textline_orientation and language_classification are Top-1 Acc.