openai-clip-vit-base-patch32 Overview OpenAI's CLIP (Contrastive Language–Image Pre-training) model was designed to investigate the factors that contribute to the robustness of computer vision tasks. It can seamlessly adapt to a range of image classification tasks without requiring specific training...
display_labels=labels)disp.plot(xticks_rotation="vertical")在clip-vit-base-patch32模型上的accuracy...
首先需要将每个样本转换为图像张量嵌入。from transformers import CLIPProcessorfrom PIL import Imageclip_processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")def clip_embeddings(image): inputs = clip_processor(images=image, return_tensors="pt", padding=True) input_tokens = ...
比如说HuggingFace提供了的这个简单的例子 from PIL import Imageimport requestsfrom transformers import CLIPProcessor, CLIPModelmodel = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")url = "http://images.cocodataset.org...
model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32").to(device) #Extract features from image1 image1 = Image.open('img1.jpg') with torch.no_grad(): inputs1 = processor(images=image1, return_tensors="pt").to(device) ...
clip_processor=CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")defclip_embeddings(image):inputs=clip_processor(images=image,return_tensors="pt",padding=True)input_tokens={k:vfork,vininputs.items()}returninput_tokens['pixel_values']#...scene_clip_embeddings=[]# to hold the scene...
model_name='pretrained_models/clip-vit-base-patch32-projection', model_name='openai/clip-vit-base-patch32', frozen_modules=['all'])), neck=dict(type='YOLOWorldPAFPN', guide_channels=text_channels, Expand Down 2 changes: 1 addition & 1 deletion2...ain/yolo_world_x_dual_vlpan_l2norm...
model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32").to(device) #Extract features from image1 image1 = Image.open('img1.jpg') with torch.no_grad(): inputs1 = processor(images=image1, return_tensors="pt").to(device) ...
首先下载一个高性能的预训练过的CNN,比如ResNet,用它进行特征提取,得到图像特征。 然后,将这些特征作为一个标准分类器(如Logistic Regression)的输入。分类器是以有监督的方式进行训练的,其中图像标签是目标变量(图2)。 如果你选择了K-shot learning,那么在分类阶段的训练集应该只包含每个类别的K个实例。
image_text_embedding.clip['text', 'vec'](model_name='clip_vit_b32',modality='text')用clip_vit_b32将文本 'query here' 编码成向量,向量放到vec列。注意,这里我们使用同样的模型(model_name='clip_vit_b32'),但选择了文本模态(modality='text')。这样可以保证图片和文本的语义向量存在于相同的向量空间...