{
  "id": 312240,
  "title": "A strong baseline for fgvc with pretrained model.",
  "url": "/competitions/snakeclef2022/discussion/312240",
  "author_name": "amazingD",
  "post_date": "2022-03-11T02:50:26.408000",
  "votes": 9,
  "comment_count": 0,
  "views": 0,
  "content": "<h3>MetaFormer</h3>\n<p>We are very happy to meet you here and to tell you about our work(MetaFormer). We use a unified framework with meta information to do fine-grained recognition task. We have achieved SOTA performance on INAT18/19/21 and CUB datasets.<br>\nWe very much hope that you can try our model in this competition, we think it will be a good solution. Here are some results of our model.</p>\n<h3>Fine-grained Datasets</h3>\n<p>Result on fine-grained datasets with different pre-trained model.</p>\n<table>\n<thead>\n<tr>\n<th>Name</th>\n<th>Pretrain</th>\n<th>CUB</th>\n<th>NABirds</th>\n<th>iNat2017</th>\n<th>iNat2018</th>\n<th>Cars</th>\n<th>Aircraft</th>\n</tr>\n</thead>\n<tbody>\n<tr>\n<td>MetaFormer-0</td>\n<td>ImageNet-1k</td>\n<td>89.6</td>\n<td>89.1</td>\n<td>75.7</td>\n<td>79.5</td>\n<td>95.0</td>\n<td>91.2</td>\n</tr>\n<tr>\n<td>MetaFormer-0</td>\n<td>ImageNet-21k</td>\n<td>89.7</td>\n<td>89.5</td>\n<td>75.8</td>\n<td>79.9</td>\n<td>94.6</td>\n<td>91.2</td>\n</tr>\n<tr>\n<td>MetaFormer-0</td>\n<td>iNaturalist 2021</td>\n<td>91.8</td>\n<td>91.5</td>\n<td>78.3</td>\n<td>82.9</td>\n<td>95.1</td>\n<td>87.4</td>\n</tr>\n<tr>\n<td>MetaFormer-1</td>\n<td>ImageNet-1k</td>\n<td>89.7</td>\n<td>89.4</td>\n<td>78.2</td>\n<td>81.9</td>\n<td>94.9</td>\n<td>90.8</td>\n</tr>\n<tr>\n<td>MetaFormer-1</td>\n<td>ImageNet-21k</td>\n<td>91.3</td>\n<td>91.6</td>\n<td>79.4</td>\n<td>83.2</td>\n<td>95.0</td>\n<td>92.6</td>\n</tr>\n<tr>\n<td>MetaFormer-1</td>\n<td>iNaturalist 2021</td>\n<td>92.3</td>\n<td>92.7</td>\n<td>82.0</td>\n<td>87.5</td>\n<td>95.0</td>\n<td>92.5</td>\n</tr>\n<tr>\n<td>MetaFormer-2</td>\n<td>ImageNet-1k</td>\n<td>89.7</td>\n<td>89.7</td>\n<td>79.0</td>\n<td>82.6</td>\n<td>95.0</td>\n<td>92.4</td>\n</tr>\n<tr>\n<td>MetaFormer-2</td>\n<td>ImageNet-21k</td>\n<td>91.8</td>\n<td>92.2</td>\n<td>80.4</td>\n<td>84.3</td>\n<td>95.1</td>\n<td>92.9</td>\n</tr>\n<tr>\n<td>MetaFormer-2</td>\n<td>iNaturalist 2021</td>\n<td>92.9</td>\n<td>93.0</td>\n<td>82.8</td>\n<td>87.7</td>\n<td>95.4</td>\n<td>92.8</td>\n</tr>\n</tbody>\n</table>\n<p>Results in iNaturalist 2019, iNaturalist 2018, and iNaturalist 2021 with meta-information.</p>\n<table>\n<thead>\n<tr>\n<th>Name</th>\n<th>Pretrain</th>\n<th>Meta added</th>\n<th>iNat2017</th>\n<th>iNat2018</th>\n<th>iNat2021</th>\n</tr>\n</thead>\n<tbody>\n<tr>\n<td>MetaFormer-0</td>\n<td>ImageNet-1k</td>\n<td>N</td>\n<td>75.7</td>\n<td>79.5</td>\n<td>88.4</td>\n</tr>\n<tr>\n<td>MetaFormer-0</td>\n<td>ImageNet-1k</td>\n<td>Y</td>\n<td>79.8(+4.1)</td>\n<td>85.4(+5.9)</td>\n<td>92.6(+4.2)</td>\n</tr>\n<tr>\n<td>MetaFormer-1</td>\n<td>ImageNet-1k</td>\n<td>N</td>\n<td>78.2</td>\n<td>81.9</td>\n<td>90.2</td>\n</tr>\n<tr>\n<td>MetaFormer-1</td>\n<td>ImageNet-1k</td>\n<td>Y</td>\n<td>81.3(+3.1)</td>\n<td>86.5(+4.6)</td>\n<td>93.4(+3.2)</td>\n</tr>\n<tr>\n<td>MetaFormer-2</td>\n<td>ImageNet-1k</td>\n<td>N</td>\n<td>79.0</td>\n<td>82.6</td>\n<td>89.8</td>\n</tr>\n<tr>\n<td>MetaFormer-2</td>\n<td>ImageNet-1k</td>\n<td>Y</td>\n<td>82.0(+3.0)</td>\n<td>86.8(+4.2)</td>\n<td>93.2(+3.4)</td>\n</tr>\n<tr>\n<td>MetaFormer-2</td>\n<td>ImageNet-21k</td>\n<td>N</td>\n<td>80.4</td>\n<td>84.3</td>\n<td>90.3</td>\n</tr>\n<tr>\n<td>MetaFormer-2</td>\n<td>ImageNet-21k</td>\n<td>Y</td>\n<td>83.4(+3.0)</td>\n<td>88.7(+4.4)</td>\n<td>93.6(+3.3)</td>\n</tr>\n</tbody>\n</table>\n<p>We also provide Imagenet 1k/22k and Inaturalist Pretrain models with various resolution, Welcome to use our pretrain models and codebase.</p>\n<h3>Model zoo</h3>\n<table>\n<thead>\n<tr>\n<th>name</th>\n<th>resolution</th>\n<th>1k model</th>\n<th>21k model</th>\n<th>iNat21 model</th>\n</tr>\n</thead>\n<tbody>\n<tr>\n<td>MetaFormer-0</td>\n<td>224x224</td>\n<td><a href=\"https://drive.google.com/file/d/1BYbe3mrKioN-Ara6hhJiaiEgJLl_thSH/view?usp=sharing\" target=\"_blank\">metafg_0_1k_224</a></td>\n<td><a href=\"https://drive.google.com/file/d/1834jQ9OPHOBZDgv7jD6Qu5mNLsD9aeZv/view?usp=sharing\" target=\"_blank\">metafg_0_21k_224</a></td>\n<td>-</td>\n</tr>\n<tr>\n<td>MetaFormer-1</td>\n<td>224x224</td>\n<td><a href=\"https://drive.google.com/file/d/1p-nIZgnrDatqmSzzDknTFYw-yEEUD_Rz/view?usp=sharing\" target=\"_blank\">metafg_1_1k_224</a></td>\n<td><a href=\"https://drive.google.com/file/d/1AcybDVEY-kXFT0D79w1G7I0h4r1IxLlG/view?usp=sharing\" target=\"_blank\">metafg_1_21k_224</a></td>\n<td>-</td>\n</tr>\n<tr>\n<td>MetaFormer-2</td>\n<td>224x224</td>\n<td><a href=\"https://drive.google.com/file/d/1K6EEyFKbMUBpPqaEJMvo93YHTXCsgH2V/view?usp=sharing\" target=\"_blank\">metafg_2_1k_224</a></td>\n<td><a href=\"https://drive.google.com/file/d/1VygaD_IwYq25KwoupWfttKRZUm2_SPeK/view?usp=sharing\" target=\"_blank\">metafg_2_21k_224</a></td>\n<td>-</td>\n</tr>\n<tr>\n<td>MetaFormer-0</td>\n<td>384x384</td>\n<td><a href=\"https://drive.google.com/file/d/1r62S3CJFRWV_qA5udC9MOFOJYwRf8mE2/view?usp=sharing\" target=\"_blank\">metafg_0_1k_384</a></td>\n<td><a href=\"https://drive.google.com/file/d/1wVmlPjNTA6JKHcF3ROGorEVPxKVO83Ss/view?usp=sharing\" target=\"_blank\">metafg_0_21k_384</a></td>\n<td><a href=\"https://drive.google.com/file/d/11gCk_IuSN7krdkOUSWSM4xlf8GGknmxc/view?usp=sharing\" target=\"_blank\">metafg_0_inat21_384</a></td>\n</tr>\n<tr>\n<td>MetaFormer-1</td>\n<td>384x384</td>\n<td><a href=\"https://drive.google.com/file/d/12OTmZg4J6fMGvs-colOTDfmhdA5EMMvo/view?usp=sharing\" target=\"_blank\">metafg_1_1k_384</a></td>\n<td><a href=\"https://drive.google.com/file/d/13dsarbtsNrkhpG5XpCRlN5ogXDGXO3Z_/view?usp=sharing\" target=\"_blank\">metafg_1_21k_384</a></td>\n<td><a href=\"https://drive.google.com/file/d/1ATUIrDxaQaGqx4lJ8HE2IwX_evMhblPu/view?usp=sharing\" target=\"_blank\">metafg_1_inat21_384</a></td>\n</tr>\n<tr>\n<td>MetaFormer-2</td>\n<td>384x384</td>\n<td><a href=\"https://drive.google.com/file/d/167oBaseORq32aFA3Ex6lpHuasvu2PMb8/view?usp=sharing\" target=\"_blank\">metafg_2_1k_384</a></td>\n<td><a href=\"https://drive.google.com/file/d/1PnpntloQaYduEokFGQ6y79G7DdyjD_u3/view?usp=sharing\" target=\"_blank\">metafg_2_21k_384</a></td>\n<td><a href=\"https://drive.google.com/file/d/17sUNST7ivQhonBAfZEiTOLAgtaHa4F3e/view?usp=sharing\" target=\"_blank\">metafg_2_inat21_384</a></td>\n</tr>\n</tbody>\n</table>\n<p>Repo : <a href=\"https://github.com/dqshuai/MetaFormer\" target=\"_blank\">https://github.com/dqshuai/MetaFormer</a><br>\npaper link: <a href=\"https://arxiv.org/abs/2203.02751\" target=\"_blank\">https://arxiv.org/abs/2203.02751</a></p>",
  "messages": [
    {
      "id": 1718625,
      "postDate": "2022-03-11T02:50:26.410Z",
      "content": "<h3>MetaFormer</h3>\n<p>We are very happy to meet you here and to tell you about our work(MetaFormer). We use a unified framework with meta information to do fine-grained recognition task. We have achieved SOTA performance on INAT18/19/21 and CUB datasets.<br>\nWe very much hope that you can try our model in this competition, we think it will be a good solution. Here are some results of our model.</p>\n<h3>Fine-grained Datasets</h3>\n<p>Result on fine-grained datasets with different pre-trained model.</p>\n<table>\n<thead>\n<tr>\n<th>Name</th>\n<th>Pretrain</th>\n<th>CUB</th>\n<th>NABirds</th>\n<th>iNat2017</th>\n<th>iNat2018</th>\n<th>Cars</th>\n<th>Aircraft</th>\n</tr>\n</thead>\n<tbody>\n<tr>\n<td>MetaFormer-0</td>\n<td>ImageNet-1k</td>\n<td>89.6</td>\n<td>89.1</td>\n<td>75.7</td>\n<td>79.5</td>\n<td>95.0</td>\n<td>91.2</td>\n</tr>\n<tr>\n<td>MetaFormer-0</td>\n<td>ImageNet-21k</td>\n<td>89.7</td>\n<td>89.5</td>\n<td>75.8</td>\n<td>79.9</td>\n<td>94.6</td>\n<td>91.2</td>\n</tr>\n<tr>\n<td>MetaFormer-0</td>\n<td>iNaturalist 2021</td>\n<td>91.8</td>\n<td>91.5</td>\n<td>78.3</td>\n<td>82.9</td>\n<td>95.1</td>\n<td>87.4</td>\n</tr>\n<tr>\n<td>MetaFormer-1</td>\n<td>ImageNet-1k</td>\n<td>89.7</td>\n<td>89.4</td>\n<td>78.2</td>\n<td>81.9</td>\n<td>94.9</td>\n<td>90.8</td>\n</tr>\n<tr>\n<td>MetaFormer-1</td>\n<td>ImageNet-21k</td>\n<td>91.3</td>\n<td>91.6</td>\n<td>79.4</td>\n<td>83.2</td>\n<td>95.0</td>\n<td>92.6</td>\n</tr>\n<tr>\n<td>MetaFormer-1</td>\n<td>iNaturalist 2021</td>\n<td>92.3</td>\n<td>92.7</td>\n<td>82.0</td>\n<td>87.5</td>\n<td>95.0</td>\n<td>92.5</td>\n</tr>\n<tr>\n<td>MetaFormer-2</td>\n<td>ImageNet-1k</td>\n<td>89.7</td>\n<td>89.7</td>\n<td>79.0</td>\n<td>82.6</td>\n<td>95.0</td>\n<td>92.4</td>\n</tr>\n<tr>\n<td>MetaFormer-2</td>\n<td>ImageNet-21k</td>\n<td>91.8</td>\n<td>92.2</td>\n<td>80.4</td>\n<td>84.3</td>\n<td>95.1</td>\n<td>92.9</td>\n</tr>\n<tr>\n<td>MetaFormer-2</td>\n<td>iNaturalist 2021</td>\n<td>92.9</td>\n<td>93.0</td>\n<td>82.8</td>\n<td>87.7</td>\n<td>95.4</td>\n<td>92.8</td>\n</tr>\n</tbody>\n</table>\n<p>Results in iNaturalist 2019, iNaturalist 2018, and iNaturalist 2021 with meta-information.</p>\n<table>\n<thead>\n<tr>\n<th>Name</th>\n<th>Pretrain</th>\n<th>Meta added</th>\n<th>iNat2017</th>\n<th>iNat2018</th>\n<th>iNat2021</th>\n</tr>\n</thead>\n<tbody>\n<tr>\n<td>MetaFormer-0</td>\n<td>ImageNet-1k</td>\n<td>N</td>\n<td>75.7</td>\n<td>79.5</td>\n<td>88.4</td>\n</tr>\n<tr>\n<td>MetaFormer-0</td>\n<td>ImageNet-1k</td>\n<td>Y</td>\n<td>79.8(+4.1)</td>\n<td>85.4(+5.9)</td>\n<td>92.6(+4.2)</td>\n</tr>\n<tr>\n<td>MetaFormer-1</td>\n<td>ImageNet-1k</td>\n<td>N</td>\n<td>78.2</td>\n<td>81.9</td>\n<td>90.2</td>\n</tr>\n<tr>\n<td>MetaFormer-1</td>\n<td>ImageNet-1k</td>\n<td>Y</td>\n<td>81.3(+3.1)</td>\n<td>86.5(+4.6)</td>\n<td>93.4(+3.2)</td>\n</tr>\n<tr>\n<td>MetaFormer-2</td>\n<td>ImageNet-1k</td>\n<td>N</td>\n<td>79.0</td>\n<td>82.6</td>\n<td>89.8</td>\n</tr>\n<tr>\n<td>MetaFormer-2</td>\n<td>ImageNet-1k</td>\n<td>Y</td>\n<td>82.0(+3.0)</td>\n<td>86.8(+4.2)</td>\n<td>93.2(+3.4)</td>\n</tr>\n<tr>\n<td>MetaFormer-2</td>\n<td>ImageNet-21k</td>\n<td>N</td>\n<td>80.4</td>\n<td>84.3</td>\n<td>90.3</td>\n</tr>\n<tr>\n<td>MetaFormer-2</td>\n<td>ImageNet-21k</td>\n<td>Y</td>\n<td>83.4(+3.0)</td>\n<td>88.7(+4.4)</td>\n<td>93.6(+3.3)</td>\n</tr>\n</tbody>\n</table>\n<p>We also provide Imagenet 1k/22k and Inaturalist Pretrain models with various resolution, Welcome to use our pretrain models and codebase.</p>\n<h3>Model zoo</h3>\n<table>\n<thead>\n<tr>\n<th>name</th>\n<th>resolution</th>\n<th>1k model</th>\n<th>21k model</th>\n<th>iNat21 model</th>\n</tr>\n</thead>\n<tbody>\n<tr>\n<td>MetaFormer-0</td>\n<td>224x224</td>\n<td><a href=\"https://drive.google.com/file/d/1BYbe3mrKioN-Ara6hhJiaiEgJLl_thSH/view?usp=sharing\" target=\"_blank\">metafg_0_1k_224</a></td>\n<td><a href=\"https://drive.google.com/file/d/1834jQ9OPHOBZDgv7jD6Qu5mNLsD9aeZv/view?usp=sharing\" target=\"_blank\">metafg_0_21k_224</a></td>\n<td>-</td>\n</tr>\n<tr>\n<td>MetaFormer-1</td>\n<td>224x224</td>\n<td><a href=\"https://drive.google.com/file/d/1p-nIZgnrDatqmSzzDknTFYw-yEEUD_Rz/view?usp=sharing\" target=\"_blank\">metafg_1_1k_224</a></td>\n<td><a href=\"https://drive.google.com/file/d/1AcybDVEY-kXFT0D79w1G7I0h4r1IxLlG/view?usp=sharing\" target=\"_blank\">metafg_1_21k_224</a></td>\n<td>-</td>\n</tr>\n<tr>\n<td>MetaFormer-2</td>\n<td>224x224</td>\n<td><a href=\"https://drive.google.com/file/d/1K6EEyFKbMUBpPqaEJMvo93YHTXCsgH2V/view?usp=sharing\" target=\"_blank\">metafg_2_1k_224</a></td>\n<td><a href=\"https://drive.google.com/file/d/1VygaD_IwYq25KwoupWfttKRZUm2_SPeK/view?usp=sharing\" target=\"_blank\">metafg_2_21k_224</a></td>\n<td>-</td>\n</tr>\n<tr>\n<td>MetaFormer-0</td>\n<td>384x384</td>\n<td><a href=\"https://drive.google.com/file/d/1r62S3CJFRWV_qA5udC9MOFOJYwRf8mE2/view?usp=sharing\" target=\"_blank\">metafg_0_1k_384</a></td>\n<td><a href=\"https://drive.google.com/file/d/1wVmlPjNTA6JKHcF3ROGorEVPxKVO83Ss/view?usp=sharing\" target=\"_blank\">metafg_0_21k_384</a></td>\n<td><a href=\"https://drive.google.com/file/d/11gCk_IuSN7krdkOUSWSM4xlf8GGknmxc/view?usp=sharing\" target=\"_blank\">metafg_0_inat21_384</a></td>\n</tr>\n<tr>\n<td>MetaFormer-1</td>\n<td>384x384</td>\n<td><a href=\"https://drive.google.com/file/d/12OTmZg4J6fMGvs-colOTDfmhdA5EMMvo/view?usp=sharing\" target=\"_blank\">metafg_1_1k_384</a></td>\n<td><a href=\"https://drive.google.com/file/d/13dsarbtsNrkhpG5XpCRlN5ogXDGXO3Z_/view?usp=sharing\" target=\"_blank\">metafg_1_21k_384</a></td>\n<td><a href=\"https://drive.google.com/file/d/1ATUIrDxaQaGqx4lJ8HE2IwX_evMhblPu/view?usp=sharing\" target=\"_blank\">metafg_1_inat21_384</a></td>\n</tr>\n<tr>\n<td>MetaFormer-2</td>\n<td>384x384</td>\n<td><a href=\"https://drive.google.com/file/d/167oBaseORq32aFA3Ex6lpHuasvu2PMb8/view?usp=sharing\" target=\"_blank\">metafg_2_1k_384</a></td>\n<td><a href=\"https://drive.google.com/file/d/1PnpntloQaYduEokFGQ6y79G7DdyjD_u3/view?usp=sharing\" target=\"_blank\">metafg_2_21k_384</a></td>\n<td><a href=\"https://drive.google.com/file/d/17sUNST7ivQhonBAfZEiTOLAgtaHa4F3e/view?usp=sharing\" target=\"_blank\">metafg_2_inat21_384</a></td>\n</tr>\n</tbody>\n</table>\n<p>Repo : <a href=\"https://github.com/dqshuai/MetaFormer\" target=\"_blank\">https://github.com/dqshuai/MetaFormer</a><br>\npaper link: <a href=\"https://arxiv.org/abs/2203.02751\" target=\"_blank\">https://arxiv.org/abs/2203.02751</a></p>",
      "rawMarkdown": "### MetaFormer\nWe are very happy to meet you here and to tell you about our work(MetaFormer). We use a unified framework with meta information to do fine-grained recognition task. We have achieved SOTA performance on INAT18/19/21 and CUB datasets.\nWe very much hope that you can try our model in this competition, we think it will be a good solution. Here are some results of our model.\n###Fine-grained Datasets\nResult on fine-grained datasets with different pre-trained model.\n| Name       | Pretrain   | CUB | NABirds |  iNat2017   | iNat2018  | Cars | Aircraft |\n| :--------: | :----------: | :--------: | :----------: | :------------: | :------------: | :--------: |:--------: |\n| MetaFormer-0|ImageNet-1k|89.6|89.1|75.7|79.5|95.0|91.2|\n| MetaFormer-0|ImageNet-21k|89.7|89.5|75.8|79.9|94.6|91.2|\n| MetaFormer-0|iNaturalist 2021|91.8|91.5|78.3|82.9|95.1|87.4|\n| MetaFormer-1|ImageNet-1k|89.7|89.4|78.2|81.9|94.9|90.8|\n| MetaFormer-1|ImageNet-21k|91.3|91.6|79.4|83.2|95.0|92.6|\n| MetaFormer-1|iNaturalist 2021|92.3|92.7|82.0|87.5|95.0|92.5|\n| MetaFormer-2|ImageNet-1k|89.7|89.7|79.0|82.6|95.0|92.4|\n| MetaFormer-2|ImageNet-21k|91.8|92.2|80.4|84.3|95.1|92.9|\n| MetaFormer-2|iNaturalist 2021|92.9|93.0|82.8|87.7|95.4|92.8|\n\n\nResults in iNaturalist 2019, iNaturalist 2018, and iNaturalist 2021 with meta-information.\n| Name       | Pretrain   | Meta added| iNat2017   |  iNat2018   | iNat2021   |\n| :--------: | :----------: | :--------: | :---------- | :------------ |:------------ |\n|MetaFormer-0|ImageNet-1k|N|75.7|79.5|88.4|\n|MetaFormer-0|ImageNet-1k|Y|79.8(+4.1)|85.4(+5.9)|92.6(+4.2)|\n|MetaFormer-1|ImageNet-1k|N|78.2|81.9|90.2|\n|MetaFormer-1|ImageNet-1k|Y|81.3(+3.1)|86.5(+4.6)|93.4(+3.2)|\n|MetaFormer-2|ImageNet-1k|N|79.0|82.6|89.8|\n|MetaFormer-2|ImageNet-1k|Y|82.0(+3.0)|86.8(+4.2)|93.2(+3.4)|\n|MetaFormer-2|ImageNet-21k|N|80.4|84.3|90.3|\n|MetaFormer-2|ImageNet-21k|Y|83.4(+3.0)|88.7(+4.4)|93.6(+3.3)|\n\nWe also provide Imagenet 1k/22k and Inaturalist Pretrain models with various resolution, Welcome to use our pretrain models and codebase.\n\n### Model zoo\n| name       | resolution   | 1k model   |  21k model   | iNat21 model   |\n| :--------: | :----------: | :--------: | :----------: | :------------: |\n| MetaFormer-0   | 224x224 | [metafg_0_1k_224](https://drive.google.com/file/d/1BYbe3mrKioN-Ara6hhJiaiEgJLl_thSH/view?usp=sharing)|[metafg_0_21k_224](https://drive.google.com/file/d/1834jQ9OPHOBZDgv7jD6Qu5mNLsD9aeZv/view?usp=sharing)|-|\n| MetaFormer-1   | 224x224 | [metafg_1_1k_224](https://drive.google.com/file/d/1p-nIZgnrDatqmSzzDknTFYw-yEEUD_Rz/view?usp=sharing)|[metafg_1_21k_224](https://drive.google.com/file/d/1AcybDVEY-kXFT0D79w1G7I0h4r1IxLlG/view?usp=sharing)|-|\n| MetaFormer-2   | 224x224 | [metafg_2_1k_224](https://drive.google.com/file/d/1K6EEyFKbMUBpPqaEJMvo93YHTXCsgH2V/view?usp=sharing)|[metafg_2_21k_224](https://drive.google.com/file/d/1VygaD_IwYq25KwoupWfttKRZUm2_SPeK/view?usp=sharing)|-|\n| MetaFormer-0   |     384x384      |  [metafg_0_1k_384](https://drive.google.com/file/d/1r62S3CJFRWV_qA5udC9MOFOJYwRf8mE2/view?usp=sharing)  |  [metafg_0_21k_384](https://drive.google.com/file/d/1wVmlPjNTA6JKHcF3ROGorEVPxKVO83Ss/view?usp=sharing)  |  [metafg_0_inat21_384](https://drive.google.com/file/d/11gCk_IuSN7krdkOUSWSM4xlf8GGknmxc/view?usp=sharing)  |\n| MetaFormer-1   |     384x384      |  [metafg_1_1k_384](https://drive.google.com/file/d/12OTmZg4J6fMGvs-colOTDfmhdA5EMMvo/view?usp=sharing)  |  [metafg_1_21k_384](https://drive.google.com/file/d/13dsarbtsNrkhpG5XpCRlN5ogXDGXO3Z_/view?usp=sharing)  |  [metafg_1_inat21_384](https://drive.google.com/file/d/1ATUIrDxaQaGqx4lJ8HE2IwX_evMhblPu/view?usp=sharing)  |\n| MetaFormer-2   |     384x384      |  [metafg_2_1k_384](https://drive.google.com/file/d/167oBaseORq32aFA3Ex6lpHuasvu2PMb8/view?usp=sharing)  |  [metafg_2_21k_384](https://drive.google.com/file/d/1PnpntloQaYduEokFGQ6y79G7DdyjD_u3/view?usp=sharing)  |  [metafg_2_inat21_384](https://drive.google.com/file/d/17sUNST7ivQhonBAfZEiTOLAgtaHa4F3e/view?usp=sharing)  |\n\nRepo : https://github.com/dqshuai/MetaFormer\npaper link: https://arxiv.org/abs/2203.02751",
      "votes": 9
    }
  ],
  "comments": [],
  "raw_markdown_by_id": {
    "1718625": "### MetaFormer\nWe are very happy to meet you here and to tell you about our work(MetaFormer). We use a unified framework with meta information to do fine-grained recognition task. We have achieved SOTA performance on INAT18/19/21 and CUB datasets.\nWe very much hope that you can try our model in this competition, we think it will be a good solution. Here are some results of our model.\n###Fine-grained Datasets\nResult on fine-grained datasets with different pre-trained model.\n| Name       | Pretrain   | CUB | NABirds |  iNat2017   | iNat2018  | Cars | Aircraft |\n| :--------: | :----------: | :--------: | :----------: | :------------: | :------------: | :--------: |:--------: |\n| MetaFormer-0|ImageNet-1k|89.6|89.1|75.7|79.5|95.0|91.2|\n| MetaFormer-0|ImageNet-21k|89.7|89.5|75.8|79.9|94.6|91.2|\n| MetaFormer-0|iNaturalist 2021|91.8|91.5|78.3|82.9|95.1|87.4|\n| MetaFormer-1|ImageNet-1k|89.7|89.4|78.2|81.9|94.9|90.8|\n| MetaFormer-1|ImageNet-21k|91.3|91.6|79.4|83.2|95.0|92.6|\n| MetaFormer-1|iNaturalist 2021|92.3|92.7|82.0|87.5|95.0|92.5|\n| MetaFormer-2|ImageNet-1k|89.7|89.7|79.0|82.6|95.0|92.4|\n| MetaFormer-2|ImageNet-21k|91.8|92.2|80.4|84.3|95.1|92.9|\n| MetaFormer-2|iNaturalist 2021|92.9|93.0|82.8|87.7|95.4|92.8|\n\n\nResults in iNaturalist 2019, iNaturalist 2018, and iNaturalist 2021 with meta-information.\n| Name       | Pretrain   | Meta added| iNat2017   |  iNat2018   | iNat2021   |\n| :--------: | :----------: | :--------: | :---------- | :------------ |:------------ |\n|MetaFormer-0|ImageNet-1k|N|75.7|79.5|88.4|\n|MetaFormer-0|ImageNet-1k|Y|79.8(+4.1)|85.4(+5.9)|92.6(+4.2)|\n|MetaFormer-1|ImageNet-1k|N|78.2|81.9|90.2|\n|MetaFormer-1|ImageNet-1k|Y|81.3(+3.1)|86.5(+4.6)|93.4(+3.2)|\n|MetaFormer-2|ImageNet-1k|N|79.0|82.6|89.8|\n|MetaFormer-2|ImageNet-1k|Y|82.0(+3.0)|86.8(+4.2)|93.2(+3.4)|\n|MetaFormer-2|ImageNet-21k|N|80.4|84.3|90.3|\n|MetaFormer-2|ImageNet-21k|Y|83.4(+3.0)|88.7(+4.4)|93.6(+3.3)|\n\nWe also provide Imagenet 1k/22k and Inaturalist Pretrain models with various resolution, Welcome to use our pretrain models and codebase.\n\n### Model zoo\n| name       | resolution   | 1k model   |  21k model   | iNat21 model   |\n| :--------: | :----------: | :--------: | :----------: | :------------: |\n| MetaFormer-0   | 224x224 | [metafg_0_1k_224](https://drive.google.com/file/d/1BYbe3mrKioN-Ara6hhJiaiEgJLl_thSH/view?usp=sharing)|[metafg_0_21k_224](https://drive.google.com/file/d/1834jQ9OPHOBZDgv7jD6Qu5mNLsD9aeZv/view?usp=sharing)|-|\n| MetaFormer-1   | 224x224 | [metafg_1_1k_224](https://drive.google.com/file/d/1p-nIZgnrDatqmSzzDknTFYw-yEEUD_Rz/view?usp=sharing)|[metafg_1_21k_224](https://drive.google.com/file/d/1AcybDVEY-kXFT0D79w1G7I0h4r1IxLlG/view?usp=sharing)|-|\n| MetaFormer-2   | 224x224 | [metafg_2_1k_224](https://drive.google.com/file/d/1K6EEyFKbMUBpPqaEJMvo93YHTXCsgH2V/view?usp=sharing)|[metafg_2_21k_224](https://drive.google.com/file/d/1VygaD_IwYq25KwoupWfttKRZUm2_SPeK/view?usp=sharing)|-|\n| MetaFormer-0   |     384x384      |  [metafg_0_1k_384](https://drive.google.com/file/d/1r62S3CJFRWV_qA5udC9MOFOJYwRf8mE2/view?usp=sharing)  |  [metafg_0_21k_384](https://drive.google.com/file/d/1wVmlPjNTA6JKHcF3ROGorEVPxKVO83Ss/view?usp=sharing)  |  [metafg_0_inat21_384](https://drive.google.com/file/d/11gCk_IuSN7krdkOUSWSM4xlf8GGknmxc/view?usp=sharing)  |\n| MetaFormer-1   |     384x384      |  [metafg_1_1k_384](https://drive.google.com/file/d/12OTmZg4J6fMGvs-colOTDfmhdA5EMMvo/view?usp=sharing)  |  [metafg_1_21k_384](https://drive.google.com/file/d/13dsarbtsNrkhpG5XpCRlN5ogXDGXO3Z_/view?usp=sharing)  |  [metafg_1_inat21_384](https://drive.google.com/file/d/1ATUIrDxaQaGqx4lJ8HE2IwX_evMhblPu/view?usp=sharing)  |\n| MetaFormer-2   |     384x384      |  [metafg_2_1k_384](https://drive.google.com/file/d/167oBaseORq32aFA3Ex6lpHuasvu2PMb8/view?usp=sharing)  |  [metafg_2_21k_384](https://drive.google.com/file/d/1PnpntloQaYduEokFGQ6y79G7DdyjD_u3/view?usp=sharing)  |  [metafg_2_inat21_384](https://drive.google.com/file/d/17sUNST7ivQhonBAfZEiTOLAgtaHa4F3e/view?usp=sharing)  |\n\nRepo : https://github.com/dqshuai/MetaFormer\npaper link: https://arxiv.org/abs/2203.02751"
  }
}