  Fine-Grained Visual Classification (FGVC) is a longstanding and fundamental
problem in computer vision and pattern recognition, and underpins a diverse set
of real-world applications. This paper describes our contribution at
SnakeCLEF2022 with FGVC. Firstly, we design a strong multimodal backbone to
utilize various meta-information to assist in fine-grained identification.
Secondly, we provide new loss functions to solve the long tail distribution
with dataset. Then, in order to take full advantage of unlabeled datasets, we
use self-supervised learning and supervised learning joint training to provide
pre-trained model. Moreover, some effective data process tricks also are
considered in our experiments. Last but not least, fine-tuned in downstream
task with hard mining, ensambled kinds of model performance. Extensive
experiments demonstrate that our method can effectively improve the performance
of fine-grained recognition. Our method can achieve a macro f1 score 92.7% and
89.4% on private and public dataset, respectively, which is the 1st place among
the participators on private leaderboard.
