clip-vit-base-patch32死不**足惜 上传 基于CLIP-ViT-base-patch32 架构的视觉模型,用于图像分类和理解。 点赞(0) 踩踩(0) 反馈 所需:1 积分 电信网络下载 xiaoxing-pro13 2025-01-06 11:55:21 积分:1 thread 2025-01-06 11:54:43 积分:1 ...
("openai/clip-vit-base-patch32") # url = "http://images.cocodataset.org/val2017/000000039769.jpg" # image = Image.open(requests.get(url, stream=True).raw) image = Image.open('liudehua.jpg') text = ["Jackie Chan", "Andy Lau", 'Chow Yun Fat'] inputs = processor(...
openai-clip-vit-large-patch14-336 openai-clip-vit-base-patch16 OFA-Sys 中文 clip model support ofasys_chinese_clip_vit_huge_patch14 ofasys_chinese_clip_vit_large_patch14_336 ofasys_chinese_clip_vit_base_patch16 ofasys_chinese_clip_vit_large_patch14 2. float16,float32模型支持,8bit intege...
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...
脚本运行将下载 CLIP 模型,需指定其客户端存放路径。本示例使用相对路径,指定于压缩包所在文件夹models下。 model_name CLIP 模型名称。 默认为openai/clip-vit-base-patch32模型。如需更换为其他 CLIP 模型,请参见 Huggingface Models。注意格式:Hugging face 仓库名/模型名称。 [graph_upload] local_graph_path ...
model_name='openai/clip-vit-base-patch32', frozen_modules=['all'])), neck=dict(type='YOLOWolrdDualPAFPN', guide_channels=text_channels, Expand Down 2 changes: 1 addition & 1 deletion2configs/finetune_coco/yolo_world_l_efficient_neck_2e-4_80e_8gpus_finetune_coco.py ...
import torch from transformers import CLIPProcessor, CLIPModel # 加载CLIP的处理器和模型 processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") # 准备输入数据:图像和文本 image_inputs = processor(images="path/to...
model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) ...
device="cuda"if torch.cuda.is_available() else"cpu"model_id="openai/clip-vit-base-patch32"# we initialize a tokenizer, image processor, and the model itselftokenizer= CLIPTokenizerFast.from_pretrained(model_id)processor= CLIPProcessor.from_pretrained(model_id)model= CLIPModel.from_pretrained(mod...
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...