数据集下载地址: https://www.kaggle.com/datasets/hsankesara/flickr-image-datasetwww.kaggle.com/datasets/hsankesara/flickr-image-dataset class CFG:Flickr Image datasetclass CFG: debug=False image_path="../input/flickr-image-dataset/flickr30k_images/flickr30k_images" captions_path="." batch_s...
代码如下: your_dataset = YourDataset(img_root= '/images',meta_root= '/meta', is_train=True,preprocess=preprocess) dataset_size_your = len(your_dataset) your_dataloader=DataLoader(your_dataset,batch_size=4,shuffle=True,num_workers=4,pin_memory=False) 训练模型 训练代码按照模板来写即可,总共要...
(1)Contrastive pre-training:预训练阶段,使用图片-文本对进行对比学习训练; (2)Create dataset classifier from label text:提取预测类别文本特征; (3)Use for zero-shot predictiion:进行 Zero-Shoot 推理预测。 图3:CLIP网络结构图 具体来说,在预训练阶段,CLIP通过...
最后我们将标题的原始文本与关键字“标题”一起输入字典。 class CLIPDataset(torch.utils.data.Dataset): def __init__(self, image_filenames, captions, tokenizer, transforms): """ image_filenames and cpations must have the same length; so, if there are multiple captions for each image, the ...
image_path = "../input/flickr-image-dataset/flickr30k_images/flickr30k_images" captions_path = "." batch_size = 32 num_workers = 4 head_lr = 1e-3 image_encoder_lr = 1e-4 text_encoder_lr = 1e-5 weight_decay = 1e-3
vtk ClipDataSetWithPolyData 任意polydata 切割矩形物体,所以这个也有限制,一方必须是矩形网格。 The example that shows how to use the vtkClipDataSet to clip a vtkRectilinearGrid with an arbitrary polydata. vtkImplicitPolyDataDistance is used to turn the polydata into an implicit function. Every point of...
dataset=YourDataset()data_loader=DataLoader(dataset,batch_size=32,shuffle=True)# 训练循环forepochinrange(num_epochs):forimages,textsindata_loader:# 对图像和文本进行编码 image_features=image_encoder(images)input_ids,attention_mask=texts # 假设这些是经过BERTTokenizer处理的文本 ...
Reducing “textness”: training a small model with no hidden layerWe created a dataset of images with and without text in them. The idea was to train a model and then use the weights of the model as an indicator of textness bias:class Model(nn.Module): def __init__(self, dim=5...
test = CIFAR100(root, download=True, train=False, transform=preprocess)defget_features(dataset): all_features = [] all_labels = []withtorch.no_grad():forimages, labelsintqdm(DataLoader(dataset, batch_size=100)): features = model.encode_image(images.to(device)) ...
尽管数据集包含5000张图像,但我们将只利用前100张以加快演示速度。数据集包含一个包含所有图像的文件夹以及一个包含标签的CSV文件。为了便于加载图像路径和标签,我们将自定义Pytorch数据集类来创建CustomDataset()类。你可以在提供的笔记本代码中找到它。 加载CLIP模型 ...