readme文件里的两个模型的TOP1 是不是写反了？模型大的准确度比模型小的准确度小一些？

Arch | Weight | Top-1 Acc | Top-5 Acc | Crop ratio | # Params -- | -- | -- | -- | -- | -- mlp_mixer_b16_224 | pretrain 1k | 76.60 | 92.23 | 0.875 | 60.0M mlp_mixer_l16_224 | pretrain 1k | 72.06 | 87.67 | 0.875 | 208.2M

小弟想问问,对于图像分类的自监督,目前是什么进展呢?比如猫狗分类这种典型的二分类准确率如何?imagenet1k分类准确率如何?PASSL里面的关于图像分类的自监督算法或者模型,有哪些?能给个例子,让我知道如何使用吗?目前看到PASSLissues才1条,文档完全没看到.方便加个微信或者QQ聊几句吗?小弟对于图像分类的自监督高度重视.还有一个疑问,关于图像分类的自监督模型,是不是我给一堆图片,模型运行后,就会把图片归类呢?我需不需要给出类别的数量呢?说白了,我想知道图像分类的自监督的一个使用流程.现在都1.0了,该有点用处了吧.如果一个模型运行后,图像就分好类了,归纳为N类,我有什么办法判断分类的正确性呢?这方面有算法吗? 提了很多问题,跪求每个问题都回答一下,谢谢大佬.

https://github.com/PaddlePaddle/PASSL/blob/83c49e6a5ba3444cee7f054122559d7759152764/passl/modeling/backbones/clip.py#L317

check this issue for reference https://github.com/PaddlePaddle/Paddle/issues/43710

Suggested approach (with non-public API)

logit_scale_buffer = self.logit_scale.clip(-4.6, 4.6)
logit_scale_buffer._share_buffer_to(self.logit_scale)

1.passl很多文字都是英文的,包括快速使用等文档,希望可以提供中文文档. 2.希望知道图像分类自监督学习的技术研究目前到达什么程度了.比如猫狗这种二分类准确率如何,imagenet准确率如何,使用passl进行图像分类,需要给类别总数量吗? 3.能加个QQ或者微信聊几句吗?有些疑问,拜托了,大佬. QQ:1226194560 微信:18820785964

DeiT/B-16 pretrained on ImageNet1K:

[01/21 02:54:46] passl.engine.trainer INFO: Validate Epoch [290] acc1 (81.336), acc5 (95.544)
[01/21 03:02:31] passl.engine.trainer INFO: Validate Epoch [291] acc1 (81.328), acc5 (95.580)
[01/21 03:10:20] passl.engine.trainer INFO: Validate Epoch [292] acc1 (81.390), acc5 (95.608)
[01/21 03:18:10] passl.engine.trainer INFO: Validate Epoch [293] acc1 (81.484), acc5 (95.636)
[01/21 03:26:00] passl.engine.trainer INFO: Validate Epoch [294] acc1 (81.452), acc5 (95.600)
[01/21 03:33:52] passl.engine.trainer INFO: Validate Epoch [295] acc1 (81.354), acc5 (95.528)
[01/21 03:41:38] passl.engine.trainer INFO: Validate Epoch [296] acc1 (81.338), acc5 (95.562)
[01/21 03:49:25] passl.engine.trainer INFO: Validate Epoch [297] acc1 (81.344), acc5 (95.542)
[01/21 03:57:15] passl.engine.trainer INFO: Validate Epoch [298] acc1 (81.476), acc5 (95.550)
[01/21 04:05:03] passl.engine.trainer INFO: Validate Epoch [299] acc1 (81.476), acc5 (95.572)
[01/21 04:12:51] passl.engine.trainer INFO: Validate Epoch [300] acc1 (81.386), acc5 (95.536)

• 在byol的config 中设置了 num_classes=1000: https://github.com/PaddlePaddle/PASSL/blob/9d7a9fd4af41772e29120553dddab1c162e4cb70/configs/byol/byol_r50_IM.yaml#L34
• 在model中设置了self.classifier = nn.Linear(embedding_dim, num_classes)，并且forward中将classif_out和label一起传给了head

https://github.com/PaddlePaddle/PASSL/blob/9d7a9fd4af41772e29120553dddab1c162e4cb70/passl/modeling/architectures/BYOL.py#L263

• 在L2 Head中将对比loss和有监督的CE loss加在了一起返回

https://github.com/PaddlePaddle/PASSL/blob/9d7a9fd4af41772e29120553dddab1c162e4cb70/passl/modeling/heads/l2_head.py#L43

PR types

New features

PR changes

APIs

Describe

• Task: https://github.com/PaddlePaddle/Paddle/issues/41482
• 添加 passl.model.architectures.dino

Peformance

| Model | Official | Passl | | ---- | ---- | ---- | | DINO | 74.0 | 73.6 |

• [x] 预训练和linear probe代码
• [ ] 预训练和linear probe权重
• [ ] 文档
• [ ] TIPC