其中,Patch Partitioning层将输入图像分割成多个小块(patches),每个小块作为一个独立的token进行处理。而Window Attention机制则通过限制自注意力计算的范围,降低了计算复杂度,提高了模型的运行效率。 二、训练过程与数据集 swin-large-patch4-window12-384-22kto1k.pth模型是在大规模数据集上
print(timm.models.create_model("swin_large_patch4_window12_384").default_cfg) {'url': 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22kto1k.pth', 'hf_hub_id': 'timm/swin_large_patch4_window12_384.ms_in22k_ft_in1k', 'architect...
预训练模型swin-large-patch4-window12-384-22kto1k.pth Le**go上传transformer人工智能预训练模型 swin transformer 预训练模型swin_large_patch4_window12_384_22kto1k.pth (0)踩踩(0) 所需:10积分
SwinTransformer_base_patch4_window12_384 研 研究院Y 1枚 CC0 计算机视觉 0 2 2023-04-21 详情 相关项目 评论(0) 创建项目 文件列表 SwinTransformer_base_patch4_window12_384.pdparams SwinTransformer_base_patch4_window12_384.pdparams (517.38M) 下载关于...
│ └── model-2.pth ├── pre_weights │ ├── swin_large_patch4_window7_224_22k.pth │ └── swin_tiny_patch4_window7_224.pth ├── labels │ ├── train2017 │ └── val2017 ├── class_indices.json ├── record.txt ...
huggingface_model_id : microsoft/swinv2-base-patch4-window12-192-22k training_dataset : imagenet-1k SharedComputeCapacityEnabled author : Microsoft license : apache-2.0 model_specific_defaults : ordereddict({'apply_deepspeed': 'true', 'apply_ort': 'true'}) task : image-classification hiddenlay...
├── flower_photos │ ├── daisy │ ├── sunflowers │ └── tulips ├── weights │ ├── model-0.pth │ ├── model-1.pth │ └── model-2.pth ├── pre_weights │ ├── swin_large_patch4_window7_224_22k.pth │ └── swin_tiny_patch4_window7_224.pth ├─...
def swin_tiny_patch4_window7_224(num_classes: int = 1000, **kwargs): # trained ImageNet-1K # https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth model = SwinTransformer(in_chans=3, patch_size=4, window_size=7, embed_dim=96...
MODEL: META_ARCHITECTURE: "GeneralizedVLRCNN" WEIGHT: "swin_large_patch4_window12_384_22k.pth" RPN_ONLY: True RPN_ARCHITECTURE: "VLDYHEAD" BACKBONE: CONV_BODY: "SWINT-FPN-RETINANET" OUT_CHANNELS: 256 SWINT: EMBED_DIM: 192 DEPTHS: (2, 2, 18, 2) NUM_HEADS: (6, 12, 24, 48) ...
首先将图片输入到Patch Partition模块中进行分块,即每[Math Processing Error]相邻的像素为一个Patch,然后在channel方向展平(flatten)。假设输入的是RGB三通道图片,那么每个patch就有[Math Processing Error]个像素,然后每个像素有R、G、B三个值所以展平后是[Math Processing Error],所以通过Patch Partition后图像shape...