This part mainly explains the training mode of feature learning, which is RecModel training mode in code. The main purpose of feature learning is to support the application, such as vehicle recognition (vehicle fine-grained classification, vehicle Reid), logo recognition, cartoon character recognition , product recognition, which needs to learn robust features to identify objects. Different from training classification network on Imagenet, this feature learning part mainly has the following features:
-
Support to truncate the
backbone, which means feature of any intermediate layer can be extracted -
Support to add configurable layers after
backboneoutput, namelyNeck -
Support
Arcface Lossand othermetric learningloss functions to improve feature learning ability
The overall structure of feature learning is shown in the figure above, which mainly includes Data Augmentation, Backbone, Neck, Metric Learning and so on. The Neck part is a freely added layers, such as Embedding layer. Of course, this module can be omitted if not needed. During training, the loss of Metric Learning is used to optimize the model. Generally speaking, the output of the Neck is used as the feature output when in inference stage.
The feature learning config file description can be found in yaml description.
The following are the pretrained models trained on different dataset.