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 `backbone` output, namely `Neck`
- Support `Arcface Loss` and other `metric learning`loss 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.
- Cartoon Character Recognition：[iCartoon](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/pretrain/cartoon_rec_ResNet50_iCartoon_v1.0_pretrained.pdparams)
- Logo Recognition：[Logo 3K](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/pretrain/logo_rec_ResNet50_Logo3K_v1.1_pretrained.pdparams)
