Semi-Supervised Recognition under a Noisy and Fine-grained Dataset
Cheng Cui, Zhi Ye, Yangxi Li, Xinjian Li, Min Yang, Kai Wei, Bing Dai, Yanmei Zhao, Zhongji Liu, Rong Pang
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Abstract
Simi-Supervised Recognition Challenge-FGVC7 is a challenging fine-grained recognition competition. One of the difficulties of this competition is how to use unlabeled data. We adopted pseudo-tag data mining to increase the amount of training data. The other one is how to identify similar birds with a very small difference, especially those have a relatively tiny main-body in examples. We combined generic image recognition and fine-grained image recognition method to solve the problem. All generic image recognition models were training using PaddleClas . Using the combination of two different ways of deep recognition models, we finally won the third place in the competition.
Tasks
Benchmark Results
| Dataset | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| ImageNet | ResNet200_vd_26w_4s_ssld | Top 1 Accuracy | 85.1 | — | Unverified |
| ImageNet | Fix_ResNet50_vd_ssld | Top 1 Accuracy | 84 | — | Unverified |
| ImageNet | ResNet50_vd_ssld | Top 1 Accuracy | 83 | — | Unverified |
| ImageNet | MobileNetV3_large_x1_0_ssld | Top 1 Accuracy | 79 | — | Unverified |