vit-base-patch16-224-in21k.zip Za**ny上传306.01MB文件格式zip vit模型 (0)踩踩(0) 所需:1积分
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) 那么下面我们就一步步的对代码进行解读,首先我们先对输入进行Patch_embedding操作,这部分我在理论详解篇有详细的介绍过,其就是采用一个卷积核大小为16*16,步长为16的卷...
(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 = ...
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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) ...
ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer. weights ported from official Google JAX impl: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_patch16_224_in21k-e5005f0a.pth """ model = Vision...
--weights ./vit_base_patch16_224_in21k.pth 下那个在21k上预训练的权重 2022-11-24 15:392回复 you点小帅把else ['pre_logits.fc.weight', 'pre_logits.fc.bias', 'head.weight', 'head.bias']改成else ['patch_embed.proj.weight', 'patch_embed.proj.bias', 'head.weight', 'head.bias']...
花卉分类数据集下载完成后,需要将其解压到项目文件夹中。该项目文件夹包含训练脚本(train.py)和预测脚本(predict.py),以及保存了5类花卉图片数据的flower_photos文件夹。此外,还需要一个预训练模型(jx_vit_base_patch16_224_in21k-e5005f0a.pth),用于基于此训练模型来训练自己的数据集。 通过使用VIT模型进行花卉...
BeitBasePatch16, 21k_ft1k 86.53M 17.61G 224 85.240 321.226 qps - 21k_ft1k, 384 86.74M 55.70G 384 86.808 164.705 qps BeitLargePatch16, 21k_ft1k 304.43M 61.68G 224 87.476 105.998 qps - 21k_ft1k, 384 305.00M 191.65G 384 88.382 45.7307 qps - 21k_ft1k, 512 305.67M 363.46G 512 88.58...
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...