blip-image-captioning-bas是一个用1400W参数训练出来的模型,该模型在huggingface的大小有990M,有两种方式使用该模型,一种是通过API调用的方式,前提是必须在云环境中事先部署好该模型的应用服务,然后提供api key和 Inference Endpoint来供调用,这种方式不占用本地存储空间资源,但会占用网络资源,第二种方式是将blip-...
数据处理部分主要有两个模块,captioning(用于生成给定图像的文字描述)和filtering(用于去除噪声图像文本对),两者均以MED进行初始化,并在数据集COCO上微调。最后合并两者的数据集,以新的数据集预训练一个新的模型。 3.3 OFA 论文:OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Seque...
image_to_text=pipeline("image-to-text",model="nlpconnect/vit-gpt2-image-captioning")output=image_to_text("./parrots.png")print(output) 执行后,自动下载模型文件并进行识别: 2.5 模型排名 在huggingface上,我们将图片转文本(image-to-text)模型按热度从高到低排序,总计700个模型,ViT-GPT2排名第三,CLI...
Caption-Anything is a versatile tool combining image segmentation, visual captioning, and ChatGPT, generating tailored captions with diverse controls for user preferences.https://huggingface.co/spaces/TencentARC/Caption-Anythinghttps://huggingface.co/spaces/VIPLab/Caption-Anything ...
JoyCaption is an open, free, and uncensored captioning Visual Language Model (VLM). Try the Demo on HuggingFace|Download the Current Model on Hugging Face|Latest Release Post What is JoyCaption? JoyCaption is an image captioning Visual Language Model (VLM) being built from the ground up as ...
import requests API_URL = "https://api-inference.huggingface.co/models/zoumana/beans_health_type_classifier" headers = {"Authorization": "Bearer xxxxxxxxxxxxxxxxx"} def query(filename): with open(filename, "rb") as f: data = f.read() response = requests.post(API_URL, headers=headers,...
from transformers import AutoTokenizer model = BlipForConditionalGeneration.from_pretrained("huggingface.co/Salesforce/blip-image-captioning-base") text_config = BlipTextConfig() model.text_decoder = BlipTextLMHeadModel(text_config) 1. 2.
3. After clicking on an image an asynchronous request will be sent to a HuggingFaceSalesforce/blip-image-captioning-baseImageToText model to process and generate a description of the image, it may take a few seconds. 4. Since HuggingFace with its inference API creates a common interface for ...
These leaderboards are used to track progress in Image to text TrendDatasetBest ModelPaperCodeCompareLibraries Use these libraries to find Image to text models and implementations huggingface/transformers 3 papers 138,464 jbdel/vilmedic 2 papers 166 Datasets...
out = model.generate(**inputs) print(processor.decode(out[0], skip_special_tokens=True)) # unconditional image captioning # inputs = processor(raw_image, return_tensors="pt").to("cuda") # out = model.generate(**inputs) # print(processor.decode(out[0], skip_special_tokens=True)) ...