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(url, stream=True).raw) processor = ViTImageProcessor.from_pretrained('google/vit-base-patch16-224-in21k') model = ViTModel.from_pretrained('google/vit-base-patch16-224-in21k') inputs = processor(images=image, return_tensors="pt") outputs = model(**inputs) last_hidden_states = ...
vit_base_patch16_224_in21k.zipTē**мο 上传306.01 MB 文件格式 zip Transformer 计算机视觉 pytorch 人工智能 用于Vision Transformer的预训练模型,导入后提高训练准确率起点,有利于模型拟合。点赞(0) 踩踩(0) 反馈 所需:3 积分 电信网络下载 Copyright © 2015 - 2025 https://www.coder100.com/ All...
模型训练: importosimportmathimportargparseimporttorchimporttorch.optimasoptimimporttorch.optim.lr_scheduleraslr_schedulerfromtorch.utils.tensorboardimportSummaryWriterfromtorchvisionimporttransformsfrommy_datasetimportMyDataSetfromtimm.models.vision_transformerimportvit_base_patch16_224_in21kascreate_modelfromutilsimport...
input = torch.ones(1, 3, 224, 224) # 1为batch_size(3 224 224)即表示输入图片尺寸 print(input.shape) model = vit_base_patch16_224_in21k() #使用VIT_Base模型,在imageNet21k上进行预训练 output = model(input) print(output.shape)
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patch_size=16, embed_dim=768, depth=12, num_heads=12, representation_size=None, num_classes=num_classes) return model def vit_base_patch16_224_in21k(num_classes: int = 21843, has_logits: bool = True): """ ViT-Base model (ViT-B/16) from original paper (https://arxiv.org/abs/...
from_pretrained('google/vit-base-patch16-224-in21k') 步骤3:提取特征使用Hugging Face模型的forward方法提取特征。将图像输入到模型中并获取特征图: def extract_features(image): with torch.no_grad(): outputs = model(image) features = outputs[1] return features.numpy() 步骤4:转换Hugging Face模型为...
224, patch_size=16, embed_dim=768, depth=12, num_heads=12, representation_size=None, num_classes=num_classes) return model def vit_base_patch16_224_in21k(num_classes: int = 21843, has_logits: bool = True): """ ViT-Base model (ViT-B/16) from original paper (https://arxiv.org...
input = torch.ones(1, 3, 224, 224) # 1为batch_size (3 224 224)即表示输入图片尺寸 print(input.shape) model = vit_base_patch16_224_in21k() #使用VIT_Base模型,在imageNet21k上进行预训练 output = model(input) print(output.shape) ...