ConvNeXt V2 从小到大的模型架构依次是: Atto (3.7M),Femto (5.2M),Pico (9.1M),Nano (15.6M),Tiny (28M),Base (89M),Large (198M),Huge (659M) ConvNeXt V2-A: C=40, B=(2, 2, 6, 2) ConvNeXt V2-F: C=48, B=(2, 2, 6, 2) ConvNeXt V2-P: C=64, B=(2, 2, 6, 2)...
End-to-end IN-1K fine-tuning setting for Atto (A), Femto (F),Pico (P) and Nano (N) models End-to-end IN-1K fine-tuning setting for Tiny model End-to-end IN-1K fine-tuning setting for Base (B), Large (L), and Huge (H) models End-to-end IN-22K intermediate fine-tuning s...
While trying to load the ConvNeXt V2 Tiny model from huggingface_hub and timm I get this error --- RuntimeError Traceback (most recent call last) [<ipython-input-9-1...
15_convnextv2_tiny_fold0_e5 Data CardCode (0)Discussion (0)Suggestions (0) Suggestions search tuneAll FiltersClear Allclose Typeexpand_morePendingexpand_more Recently updated No results found To see more results, try reducing the number of filters. ...
YOLOXのmは25.3Mなので、NanoとTinyの中間ぐらいのサイズ感、YOLOXのXは99.1M -- 参考文献 convnext v1 https://devblog.thebase.in/entry/2022/03/28/110000 https://lab.mo-t.com/blog/convnext https://github.com/facebookresearch/ConvNeXt/tree/main ...
1187999946 2023-03-22 22:37:08 paddle_model_1k_ft/convnextv2_nano.pdparams 93574863 2023-03-22 22:33:50 paddle_model_1k_ft/convnextv2_pico.pdparams 54331788 2023-03-22 22:39:18 paddle_model_1k_ft/convnextv2_tiny.pdparams 172225071 2023-03-22 22:35:58 下载查看更多关于...
摘要:在2020年代初,随着改进的架构和更好的表示学习框架的出现,视觉识别领域经历了快速的现代化和性能提升。例如,现代的ConvNets,如ConvNeXt 在各种场景中展现出强大的性能。虽然这些模型最初是为带有ImageNet标签的监督学习而设计的,但它们也可能受益于自监督学习技术,如掩码自编码器(MAE)。
This repo contains the PyTorch version of 8 model definitions (Atto, Femto, Pico, Nano, Tiny, Base, Large, Huge), pre-training/fine-tuning code and pre-trained weights (converted from JAX weights trained on TPU) for our ConvNeXt V2 paper. ConvNeXt V2: Co-designing and Scaling ConvNets ...
微软亚研提出TinyMIM,成功在小型ViTs上应用掩码图像建模(MIM)预训练 TinyMIM: An Empirical Study of Distilling MIM Pre-trained Models 掩码图像建模(MIM)在视觉 Transformers (ViTs) 的众多下游视觉任务中的应用取得了令人瞩目的成功,然而不能有效应用于小型 ViTs。基于此,微软亚研提出 TinyMIM,一种针对小型 ViTs...
Table 10: End-to-end IN-1K fine-tuning setting for Tiny model. Parse references config value optimizer AdamW base learning rate 8e-4 weight decay 0.05 optimizer momentum β1,β2=0.9,0.999 layer-wise lr decay [16, 3] 0.9 batch size 1024 learning rate schedule cosine decay warmup epochs ...