kosmos 2 patch14 224

提供商microsoft
分类image-to-text
许可证mit
下载量190
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简介

Kosmos-2 是微软推出的多模态模型，专注于图像到文本的理解与生成。它通过独特的 Patch 机制将视觉信息转化为类似 Token 的形式，实现了图像与文本在同一个语义空间中的统一处理。对于开发者而言，它不仅能完成基础的图像描述，在视觉问答（VQA）和复杂场景分析上也有不错表现。由于采用 MIT 协议且结构清晰，它非常适合作为构建自定义多模态应用的基座模型，上手难度中等，是研究视觉语言模型（VLM）落地的一个极佳切入点。

核心亮点

  • 统一视觉文本 Token 空间，增强图文对齐能力
  • 擅长图像描述与复杂视觉问答场景
  • MIT 开源协议，企业级应用部署无压力
  • 适合作为多模态任务的预训练基座模型

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("microsoft/kosmos-2-patch14-224")
tokenizer = AutoTokenizer.from_pretrained("microsoft/kosmos-2-patch14-224")

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 microsoft/kosmos-2-patch14-224

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 microsoft/kosmos-2-patch14-224 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('microsoft/kosmos-2-patch14-224')

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/microsoft/kosmos-2-patch14-224

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/microsoft/kosmos-2-patch14-224

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('microsoft/kosmos-2-patch14-224')
tokenizer = AutoTokenizer.from_pretrained('microsoft/kosmos-2-patch14-224')

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 microsoft/kosmos-2-patch14-224

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 microsoft/kosmos-2-patch14-224 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('microsoft/kosmos-2-patch14-224')

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/microsoft/kosmos-2-patch14-224.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/microsoft/kosmos-2-patch14-224.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', 'microsoft/kosmos-2-patch14-224')

完整文档

来源: HuggingFace

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

  • image-captioning

languages:
  • en

license: mit
---

Kosmos-2: Grounding Multimodal Large Language Models to the World

<a href="https://huggingface.co/microsoft/kosmos-2-patch14-224/resolve/main/annotated_snowman.jpg" target="_blank"><figure><img src="https://huggingface.co/microsoft/kosmos-2-patch14-224/resolve/main/annotated_snowman.jpg" width="384"><figcaption><b>[An image of a snowman warming himself by a fire.]</b></figcaption></figure></a>

This Hub repository contains a HuggingFace's transformers implementation of the original Kosmos-2 model from Microsoft.

How to Get Started with the Model

Use the code below to get started with the model.

python
import requests

from PIL import Image
from transformers import AutoProcessor, AutoModelForVision2Seq


model = AutoModelForVision2Seq.from_pretrained("microsoft/kosmos-2-patch14-224")
processor = AutoProcessor.from_pretrained("microsoft/kosmos-2-patch14-224")


prompt = "<grounding>An image of"


url = "https://huggingface.co/microsoft/kosmos-2-patch14-224/resolve/main/snowman.png"
image = Image.open(requests.get(url, stream=True).raw)


The original Kosmos-2 demo saves the image first then reload it. For some images, this will give slightly different image input and change the generation outputs.

image.save("new_image.jpg")
image = Image.open("new_image.jpg")

inputs = processor(text=prompt, images=image, return_tensors="pt")


generated_ids = model.generate(
    pixel_values=inputs["pixel_values"],
    input_ids=inputs["input_ids"],
    attention_mask=inputs["attention_mask"],
    image_embeds=None,
    image_embeds_position_mask=inputs["image_embeds_position_mask"],
    use_cache=True,
    max_new_tokens=128,
)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]


Specify cleanup_and_extract=False in order to see the raw model generation.

processed_text = processor.post_process_generation(generated_text, cleanup_and_extract=False)

print(processed_text)
<grounding> An image of<phrase> a snowman</phrase><object><patch_index_0044><patch_index_0863></object> warming himself by<phrase> a fire</phrase><object><patch_index_0005><patch_index_0911></object>.


By default, the generated  text is cleanup and the entities are extracted.

processed_text, entities = processor.post_process_generation(generated_text)

print(processed_text)
An image of a snowman warming himself by a fire.


print(entities)
[('a snowman', (12, 21), [(0.390625, 0.046875, 0.984375, 0.828125)]), ('a fire', (41, 47), [(0.171875, 0.015625, 0.484375, 0.890625)])]

Tasks

This model is capable of performing different tasks through changing the prompts.

First, let's define a function to run a prompt.

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

python
import requests

from PIL import Image
from transformers import AutoProcessor, AutoModelForVision2Seq


model = AutoModelForVision2Seq.from_pretrained("microsoft/kosmos-2-patch14-224")
processor = AutoProcessor.from_pretrained("microsoft/kosmos-2-patch14-224")


url = "https://huggingface.co/microsoft/kosmos-2-patch14-224/resolve/main/snowman.png"
image = Image.open(requests.get(url, stream=True).raw)


def run_example(prompt):


inputs = processor(text=prompt, images=image, return_tensors="pt")
    generated_ids = model.generate(
      pixel_values=inputs["pixel_values"],
      input_ids=inputs["input_ids"],
      attention_mask=inputs["attention_mask"],
      image_embeds=None,
      image_embeds_position_mask=inputs["image_embeds_position_mask"],
      use_cache=True,
      max_new_tokens=128,
    )
    generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
    _processed_text = processor.post_process_generation(generated_text, cleanup_and_extract=False)
    processed_text, entities = processor.post_process_generation(generated_text)


print(processed_text)
    print(entities)
    print(_processed_text)

</details>

Here are the tasks Kosmos-2 could perform:

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

Multimodal Grounding

#### • Phrase Grounding

python
prompt = "<grounding><phrase> a snowman</phrase>"
run_example(prompt)


a snowman is warming himself by the fire


[('a snowman', (0, 9), [(0.390625, 0.046875, 0.984375, 0.828125)]), ('the fire', (32, 40), [(0.203125, 0.015625, 0.453125, 0.859375)])]


<grounding><phrase> a snowman</phrase><object><patch_index_0044><patch_index_0863></object> is warming himself by<phrase> the fire</phrase><object><patch_index_0006><patch_index_0878></object>

#### • Referring Expression Comprehension

python
prompt = "<grounding><phrase> a snowman next to a fire</phrase>"
run_example(prompt)


a snowman next to a fire


[('a snowman next to a fire', (0, 24), [(0.390625, 0.046875, 0.984375, 0.828125)])]


<grounding><phrase> a snowman next to a fire</phrase><object><patch_index_0044><patch_index_0863></object>

Multimodal Referring

#### • Referring expression generation

python
prompt = "<grounding><phrase> It</phrase><object><patch_index_0044><patch_index_0863></object> is"
run_example(prompt)


It is snowman in a hat and scarf


[('It', (0, 2), [(0.390625, 0.046875, 0.984375, 0.828125)])]


<grounding><phrase> It</phrase><object><patch_index_0044><patch_index_0863></object> is snowman in a hat and scarf

Perception-Language Tasks

#### • Grounded VQA

python
prompt = "<grounding> Question: What is special about this image? Answer:"
run_example(prompt)


Question: What is special about this image? Answer: The image features a snowman sitting by a campfire in the snow.


[('a snowman', (71, 80), [(0.390625, 0.046875, 0.984375, 0.828125)]), ('a campfire', (92, 102), [(0.109375, 0.640625, 0.546875, 0.984375)])]


<grounding> Question: What is special about this image? Answer: The image features<phrase> a snowman</phrase><object><patch_index_0044><patch_index_0863></object> sitting by<phrase> a campfire</phrase><object><patch_index_0643><patch_index_1009></object> in the snow.

#### • Grounded VQA with multimodal referring via bounding boxes

python
prompt = "<grounding> Question: Where is<phrase> the fire</phrase><object><patch_index_0005><patch_index_0911></object> next to? Answer:"
run_example(prompt)


Question: Where is the fire next to? Answer: Near the snowman.


[('the fire', (19, 27), [(0.171875, 0.015625, 0.484375, 0.890625)]), ('the snowman', (50, 61), [(0.390625, 0.046875, 0.984375, 0.828125)])]


<grounding> Question: Where is<phrase> the fire</phrase><object><patch_index_0005><patch_index_0911></object> next to? Answer: Near<phrase> the snowman</phrase><object><patch_index_0044><patch_index_0863></object>.

Grounded Image captioning

#### • Brief

python
prompt = "<grounding> An image of"
run_example(prompt)

An image of a snowman warming himself by a campfire.


[('a snowman', (12, 21), [(0.390625, 0.046875, 0.984375, 0.828125)]), ('a campfire', (41, 51), [(0.109375, 0.640625, 0.546875, 0.984375)])]


<grounding> An image of<phrase> a snowman</phrase><object><patch_index_0044><patch_index_0863></object> warming himself by<phrase> a campfire</phrase><object><patch_index_0643><patch_index_1009></object>.

#### • Detailed

```python
prompt = "<grounding> Describe this image in detail:"
run_example(prompt)

Describe this image in detail: The image features a snowman sitting by a campfire in the snow. He is wearing a hat, scarf, and gloves, with a pot nearby and a cup nearby. The snowman appears to be enjoying the warmth of the fire, and it appears to have a warm and cozy atmosphere.

[('a campfire', (71, 81), [(0.171875, 0.015625, 0.484375, 0.984375)]), ('a hat', (109, 114), [(0.515625, 0.046875, 0.828125, 0.234375)]), ('scarf', (116, 121), [(0.515625, 0.234375, 0.890625, 0.578125)]), ('gloves', (127, 133), [(0.515625, 0.390625, 0.640625, 0.515625)]), ('a pot', (140,

查看官方来源