blip image captioning large

提供商Salesforce
分类image-to-text
许可证bsd-3-clause
下载量876
星标1

简介

BLIP Image Captioning Large 是由 Salesforce 开发的一款成熟图像描述模型。它擅长将视觉信息转化为自然语言，能准确地为图片生成简洁、客观的文字描述。对于开发者而言，它非常适合用于构建自动化图库标签、为视障人士提供图像辅助描述，或作为多模态流水线中的预处理环节。该模型上手难度低，推理速度快，相比于参数量巨大的通用多模态大模型，它在特定描述任务上更高效且资源占用更少，是实现“图片转文字”功能的实用选择。

核心亮点

  • 精准生成图像描述，有效实现图片自动化打标
  • 轻量级部署，推理速度快于通用多模态大模型
  • BSD-3-Clause 协议，对商业应用非常友好
  • 适用于电商图文生成、无障碍辅助等实际场景

使用方法

安装依赖
# 安装 Hugging Face transformers
pip install transformers torch
SDK 使用
# 使用 transformers 加载模型
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("Salesforce/blip-image-captioning-large")
tokenizer = AutoTokenizer.from_pretrained("Salesforce/blip-image-captioning-large")

Hugging Face 下载

我们推荐使用命令行或者 Hugging Face Hub SDK 来进行模型的下载。

操作指引：在下载前，请先通过如下命令安装 huggingface_hub：

操作指引 
pip install -U huggingface_hub

命令行下载

下载完整模型库

下载完整模型库 
huggingface-cli download Salesforce/blip-image-captioning-large

下载单个文件到指定本地文件夹（以下载 config.json 到当前路径下 ./dir 目录为例）

下载单个文件到指定本地文件夹（以下载 config.json 到当前路径下 ./dir 目录为例） 
huggingface-cli download Salesforce/blip-image-captioning-large config.json --local-dir ./dir

更多命令行下载选项，可参见官方文档

SDK 下载

SDK 下载 
# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('Salesforce/blip-image-captioning-large')

Git 下载

请确保 lfs 已经被正确安装

Git 下载 
git lfs install
git clone https://huggingface.co/Salesforce/blip-image-captioning-large

如果您希望跳过 lfs 大文件下载，可以使用如下命令

跳过 LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/Salesforce/blip-image-captioning-large

模型文件托管在 Hugging Face Hub，使用 HF CLI / SDK / Git 直接下载，不经过本站。

PyTorch / Transformers 使用

安装 Transformers

安装 Transformers 
pip install -U transformers torch

模型加载和推理

模型加载和推理 
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('Salesforce/blip-image-captioning-large')
tokenizer = AutoTokenizer.from_pretrained('Salesforce/blip-image-captioning-large')

模型下载

我们推荐使用命令行或者 ModelScope SDK 来进行模型的下载。

操作指引：在下载前，请先通过如下命令安装 ModelScope：

操作指引 
pip install modelscope

命令行下载

下载完整模型库

下载完整模型库 
modelscope download --model Salesforce/blip-image-captioning-large

下载单个文件到指定本地文件夹（以下载 README.md 到当前路径下 dir 目录为例）

下载单个文件到指定本地文件夹（以下载 README.md 到当前路径下 dir 目录为例） 
modelscope download --model Salesforce/blip-image-captioning-large README.md --local_dir ./dir

更多更丰富的命令行下载选项，可参见具体文档

SDK 下载

SDK 下载 
# 模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('Salesforce/blip-image-captioning-large')

Git 下载

请确保 lfs 已经被正确安装

Git 下载 
git lfs install
git clone https://www.modelscope.cn/Salesforce/blip-image-captioning-large.git

如果您希望跳过 lfs 大文件下载，可以使用如下命令

跳过 LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/Salesforce/blip-image-captioning-large.git

ModelScope 模型页直接下载模型文件；无需将模型文件放在本站服务器。

Notebook 快速开发

下载并安装 ModelScope library

下载并安装 ModelScope library 
pip install "modelscope[audio,cv,nlp,multi-modal,science]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html

模型加载和推理

模型加载和推理 
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

p = pipeline('text-generation', 'Salesforce/blip-image-captioning-large')

完整文档

来源: HuggingFace

---
pipeline_tag: image-to-text
tags:

  • image-captioning

languages:
  • en

license: bsd-3-clause
---

BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation

Model card for image captioning pretrained on COCO dataset - base architecture (with ViT large backbone).

| !BLIP.gif |
|:--:|
| <b> Pull figure from BLIP official repo | Image source: https://github.com/salesforce/BLIP </b>|

TL;DR

Authors from the paper write in the abstract:

*Vision-Language Pre-training (VLP) has advanced the performance for many vision-language tasks. However, most existing pre-trained models only excel in either understanding-based tasks or generation-based tasks. Furthermore, performance improvement has been largely achieved by scaling up the dataset with noisy image-text pairs collected from the web, which is a suboptimal source of supervision. In this paper, we propose BLIP, a new VLP framework which transfers flexibly to both vision-language understanding and generation tasks. BLIP effectively utilizes the noisy web data by bootstrapping the captions, where a captioner generates synthetic captions and a filter removes the noisy ones. We achieve state-of-the-art results on a wide range of vision-language tasks, such as image-text retrieval (+2.7% in average recall@1), image captioning (+2.8% in CIDEr), and VQA (+1.6% in VQA score). BLIP also demonstrates strong generalization ability when directly transferred to videolanguage tasks in a zero-shot manner. Code, models, and datasets are released.*

Usage

You can use this model for conditional and un-conditional image captioning

Using the Pytorch model

#### Running the model on CPU

<details>
<summary> Click to expand </summary>

python
import requests
from PIL import Image
from transformers import BlipProcessor, BlipForConditionalGeneration

processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large")


img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg' 
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')


conditional image captioning

text = "a photography of"
inputs = processor(raw_image, text, return_tensors="pt")

out = model.generate(inputs)
print(processor.decode(out[0], skip_special_tokens=True))


unconditional image captioning

inputs = processor(raw_image, return_tensors="pt")

out = model.generate(inputs)
print(processor.decode(out[0], skip_special_tokens=True))

</details>

#### Running the model on GPU

##### In full precision

<details>
<summary> Click to expand </summary>

python
import requests
from PIL import Image
from transformers import BlipProcessor, BlipForConditionalGeneration

processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large").to("cuda")


img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg' 
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')


conditional image captioning

text = "a photography of"
inputs = processor(raw_image, text, return_tensors="pt").to("cuda")

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))

</details>

##### In half precision (float16)

<details>
<summary> Click to expand </summary>

python
import torch
import requests
from PIL import Image
from transformers import BlipProcessor, BlipForConditionalGeneration

processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large", torch_dtype=torch.float16).to("cuda")


img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg' 
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')


conditional image captioning

text = "a photography of"
inputs = processor(raw_image, text, return_tensors="pt").to("cuda", torch.float16)

out = model.generate(inputs)
print(processor.decode(out[0], skip_special_tokens=True))
>>> a photography of a woman and her dog


unconditional image captioning

inputs = processor(raw_image, return_tensors="pt").to("cuda", torch.float16)

out = model.generate(inputs)
print(processor.decode(out[0], skip_special_tokens=True))
>>> a woman sitting on the beach with her dog

</details>

Ethical Considerations

This release is for research purposes only in support of an academic paper. Our models, datasets, and code are not specifically designed or evaluated for all downstream purposes. We strongly recommend users evaluate and address potential concerns related to accuracy, safety, and fairness before deploying this model. We encourage users to consider the common limitations of AI, comply with applicable laws, and leverage best practices when selecting use cases, particularly for high-risk scenarios where errors or misuse could significantly impact people’s lives, rights, or safety. For further guidance on use cases, refer to our AUP and AI AUP.

BibTex and citation info

code
@misc{https://doi.org/10.48550/arxiv.2201.12086,
  doi = {10.48550/ARXIV.2201.12086},
  
  url = {https://arxiv.org/abs/2201.12086},
  
  author = {Li, Junnan and Li, Dongxu and Xiong, Caiming and Hoi, Steven},
  
  keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation},
  
  publisher = {arXiv},
  
  year = {2022},
  
  copyright = {Creative Commons Attribution 4.0 International}
}
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