[Project] Add ACCV workshop 1st Solution. (#1245)

* add accv workshop 1st project * update projects * update projects * fix lint * Update projects/fgia_accv2022_1st/README.md Co-authored-by: Yuan Liu <30762564+YuanLiuuuuuu@users.noreply.github.com> * Update projects/fgia_accv2022_1st/README.md Co-authored-by: Yuan Liu <30762564+YuanLiuuuuuu@users.noreply.github.com> * Update projects/fgia_accv2022_1st/README.md Co-authored-by: Yuan Liu <30762564+YuanLiuuuuuu@users.noreply.github.com> * update * Update projects/fgia_accv2022_1st/README.md Co-authored-by: Yuan Liu <30762564+YuanLiuuuuuu@users.noreply.github.com> * Update projects/fgia_accv2022_1st/README.md Co-authored-by: Yuan Liu <30762564+YuanLiuuuuuu@users.noreply.github.com> * Update projects/fgia_accv2022_1st/README.md Co-authored-by: Yuan Liu <30762564+YuanLiuuuuuu@users.noreply.github.com> * update * Update projects/fgia_accv2022_1st/README.md Co-authored-by: Yuan Liu <30762564+YuanLiuuuuuu@users.noreply.github.com> * Update projects/fgia_accv2022_1st/README.md Co-authored-by: Yuan Liu <30762564+YuanLiuuuuuu@users.noreply.github.com> * Update projects/fgia_accv2022_1st/README.md Co-authored-by: Yuan Liu <30762564+YuanLiuuuuuu@users.noreply.github.com> * Update projects/fgia_accv2022_1st/README.md Co-authored-by: Yuan Liu <30762564+YuanLiuuuuuu@users.noreply.github.com> * update * update * update Co-authored-by: Yuan Liu <30762564+YuanLiuuuuuu@users.noreply.github.com>
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+# Solution of FGIA ACCV 2022(1st Place)
+
+This is fine-tuning part of the 1st Place Solution for Webly-supervised Fine-grained Recognition, refer to the ACCV workshop competition in https://www.cvmart.net/race/10412/base.
+
+## Result
+
+<details>
+
+<summary>Show the result</summary>
+
+<br>
+
+**LB A**
+
+![LB-A](https://user-images.githubusercontent.com/18586273/205498131-5728e470-b4f6-43b7-82a5-5f8e3bd5168e.png)
+
+**LB B**
+
+![LB-B](https://user-images.githubusercontent.com/18586273/205498171-5a3a3055-370a-4a8b-9779-b686254ebc94.png)
+
+</br>
+
+</details>
+
+## Reproduce / 复现
+
+For detailed self-supervised pretrain code, please refer to [MMSelfSup](https://github.com/open-mmlab/mmselfsup/tree/dev-1.x/projects/fgia_accv2022_1st).
+For detailed finetuning and inference code, please refer to [this repo](https://github.com/Ezra-Yu/ACCV2022_FGIA_1st).
+
+## Description
+
+### Overview of Our Solution
+
+![image](https://user-images.githubusercontent.com/18586273/205498371-31dbc1f4-5814-44bc-904a-f0d32515c7dd.png)
+
+### Our Model
+
+- ViT(MAE-pre-train)   # Pretrained from [**MMSelfSup**](https://github.com/open-mmlab/mmselfsup/tree/dev-1.x/projects/fgia_accv2022_1st).
+- Swin-v2(SimMIM-pre-train)   # From [MMCls-swin_transformer_v2](https://github.com/open-mmlab/mmclassification/tree/dev-1.x/configs/swin_transformer_v2).
+
+\*\*The architectures we use \*\*
+
+- ViT + CE-loss + post-LongTail-Adjusment
+- ViT + SubCenterArcFaceWithAdvMargin(CE)
+- Swin-B + SubCenterArcFaceWithAdvMargin(SoftMax-EQL)
+- Swin-L + SubCenterArcFaceWithAdvMargin(SoftMAx-EQL)
+
+## bag of tricks paper and code
+
+- [MAE](https://github.com/open-mmlab/mmselfsup/tree/dev-1.x/configs/selfsup/mae) |  [Config](https://github.com/Ezra-Yu/ACCV_workshop/tree/master/configs/vit)
+- [Swinv2](https://github.com/open-mmlab/mmclassification/tree/dev-1.x/configs/swin_transformer_v2) | [Config](https://github.com/Ezra-Yu/ACCV_workshop/tree/master/configs/swin)
+- [ArcFace](https://arxiv.org/abs/1801.07698)   |   [Code](https://github.com/Ezra-Yu/ACCV_workshop/blob/master/src/models/arcface_head.py)
+- [SubCenterArcFaceWithAdvMargin](https://paperswithcode.com/paper/sub-center-arcface-boosting-face-recognition)   |   [Code](https://github.com/Ezra-Yu/ACCV_workshop/blob/master/src/models/arcface_head.py)
+- [Post-LT-adjusment](https://paperswithcode.com/paper/long-tail-learning-via-logit-adjustment)   |   [Code](https://github.com/Ezra-Yu/ACCV_workshop/blob/master/src/models/linear_head_lt.py)
+- [SoftMaxEQL](https://paperswithcode.com/paper/the-equalization-losses-gradient-driven)   |   [Code](https://github.com/Ezra-Yu/ACCV_workshop/blob/master/src/models/eql.py)
+- FlipTTA [Code](https://github.com/Ezra-Yu/ACCV_workshop/blob/master/src/models/tta_classifier.py)
+- clean dataset
+- self-emsemble: [Uniform-model-soup](https://arxiv.org/abs/2203.05482) | [code](https://github.com/Ezra-Yu/ACCV_workshop/blob/master/tools/model_soup.py)
+- [pseudo](https://lilianweng.github.io/posts/2021-12-05-semi-supervised/)  | [Code](https://github.com/Ezra-Yu/ACCV_workshop/blob/master/tools/creat_pseudo.py)
+- bagging-emsemble [Code](https://github.com/Ezra-Yu/ACCV_workshop/blob/master/tools/emsemble.py),
+- post-process: [re-distribute-label](https://github.com/Ezra-Yu/ACCV_workshop/blob/master/tools/re-distribute-label.py);
+
+![Overview](https://user-images.githubusercontent.com/18586273/205498258-e5720d83-7006-4aea-86b5-aab1a8998c6c.png)
+
+![image](https://user-images.githubusercontent.com/18586273/205498027-def99b0d-a99a-470b-b292-8d5fc83111fc.png)
+
+#### Used but no improvements
+
+1. Using retrieval paradigm to solve this classification task;
+2. Using EfficientNetv2 backbone.
+
+#### Not used but worth to do
+
+1. Try [DiVE](https://arxiv.org/abs/2103.15042) algorithm to improve performance in long tail dataset;
+2. Use SimMIM to pre-train Swin-v2 on the competition dataset;
+3. refine the re-distribute-label tool.