I am facing issues with the BIRADS classification task (6 categories) using breast ultrasound imaging and urgently hope to receive a response. Thank you very much!

[YangJay2004]

我参加了一个比赛，任务是对乳腺超声影像（2500张，各类别不平衡）的BIRADS进行分类任务，所分的类别分别是2类，3类，4A，4B，4C，5类，我尝试了许多模型，但是效果都很差，包括预训练的resnet（acc60%）vit（60%）fastvit(60%),inception_resnet(65%）等等，我在你的项目中使用了你默认的参数（参考了swin-cub.yaml）进行训练，使用swin base达到了70%的正确率！！这让我很激动，并且相信你们的成果的潜力！

但是我不知道接下来该如何继续调参数或换模型来优化，每次train上的acc达到70%模型似乎就开始过拟合了。希望您能给出建议！

还有请问如果需要使用其他backbone，这些.pth的文件应该如何获取呢？我尝试从huggingface下载.bin文件并转换，但尝试了许久做了一些修改还是会报错。希望您能给出建议或下载地址！

比赛的ddl是本周日，这场比赛对我来说很重要，期望能得到您专业的答复！十分感谢！

Here’s the translation of the provided content into English，Since my English is't that good, the content is translated by AI,sorry for that.

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I participated in a competition where the task was to classify BIRADS categories for breast ultrasound images (2500 images, with imbalanced classes). The categories I am classifying are 2, 3, 4A, 4B, 4C, and 5. I tried many models, but the results were poor, including pre-trained models like ResNet (acc 60%), ViT (60%), FastViT (60%), Inception_ResNet (65%), etc. In your project, I used your default parameters (referring to swin-cub.yaml) for training, and using the Swin base model, I achieved a 70% accuracy! This excites me and makes me believe in the potential of your results!

However, I am unsure how to continue optimizing by adjusting parameters or changing models, as it seems that every time the training accuracy reaches 70%, the model begins to o

verfit. I hope you can provide some advice!

Additionally, if I need to use other backbones, how can I obtain the .pth files? I tried downloading the .bin files from Hugging Face and converting them, but despite making some modifications after trying for a long time, I still encountered errors. I hope you can provide some suggestions or download links!

The competition deadline is this Sunday, and this competition is very important to me. I look forward to your professional response! Thank you very much!

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my email is zhenghuiyang0058@gmail.com ,thanks!

6 classes examples are as following:

[mobulan]

非常感谢你认可我们的工作。首先关于不同模型的预训练模型可以在github上对应的项目找到，考虑到时间问题，我直接上传到坚果云链接如下 resnet50, vit. 对应的加载可以直接使用build.py中进行加载，并且在config文件中创建对应的yaml文件。 关于过拟合问题，我建议尝试直接冻结预训练主干网络，只微调我们方法额外引入的可学习参数，通过这种方法可能一定程度解决你们的问题。

[YangJay2004]

收到！万分感谢！