---
title: blip-itm-base-coco
canonical_url: "https://www.modelscope.cn/models/thomas/blip-itm-base-coco"
md_url: "https://www.modelscope.cn/models/thomas/blip-itm-base-coco.md"
repository: thomas/blip-itm-base-coco
last_updated: 2024-01-21
license: bsd-3-clause
model_type:
  - blip
architectures:
  - BlipForImageTextRetrieval
library_name:
  - pytorch
  - transformer
  - tensorflow
downloads: 238
stars: 1
tags:
  - image-text-matching
---

# blip-itm-base-coco

> blip-itm-base-coco - thomas 在 ModelScope 开源的模型。BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation

thomas/blip-itm-base-coco 是 ModelScope 魔搭社区上的机器学习模型，采用 bsd-3-clause 许可。

- **Repository**: thomas/blip-itm-base-coco
- **License**: bsd-3-clause
- **Tags**: image-text-matching
- **Downloads**: 238
- **Stars**: 1
- **Last updated**: 2024-01-21

Source: https://www.modelscope.cn/models/thomas/blip-itm-base-coco

---

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

Model card for BLIP trained on image-text matching - base architecture (with ViT base backbone) trained on COCO dataset.

| ![BLIP.gif](https://cdn-uploads.huggingface.co/production/uploads/1670928184033-62441d1d9fdefb55a0b7d12c.gif) |
|:--:|
| <b> Pull figure from BLIP official repo | Image source: https://github.com/salesforce/BLIP </b>|

## TL;DR

Authors from the [paper](https://arxiv.org/abs/2201.12086) 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, BlipForImageTextRetrieval

processor = BlipProcessor.from_pretrained("Salesforce/blip-itm-base-coco")
model = BlipForImageTextRetrieval.from_pretrained("Salesforce/blip-itm-base-coco")

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')

question = "A woman and a dog sitting together in a beach."
inputs = processor(raw_image, question, return_tensors="pt")

itm_scores = model(**inputs)[0]
cosine_score = model(**inputs, use_itm_head=False)[0]
```
</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, BlipForImageTextRetrieval

processor = BlipProcessor.from_pretrained("Salesforce/blip-itm-base-coco")
model = BlipForImageTextRetrieval.from_pretrained("Salesforce/blip-itm-base-coco").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')

question = "A woman and a dog sitting together in a beach."
inputs = processor(raw_image, question, return_tensors="pt").to("cuda")

itm_scores = model(**inputs)[0]
cosine_score = model(**inputs, use_itm_head=False)[0]
```
</details>

##### In half precision (`float16`)

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

```python
import torch
import requests
from PIL import Image
from transformers import BlipProcessor, BlipForImageTextRetrieval

processor = BlipProcessor.from_pretrained("Salesforce/blip-itm-base-coco")
model = BlipForImageTextRetrieval.from_pretrained("Salesforce/blip-itm-base-coco", 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')

question = "A woman and a dog sitting together in a beach."
inputs = processor(raw_image, question, return_tensors="pt").to("cuda", torch.float16)

itm_scores = model(**inputs)[0]
cosine_score = model(**inputs, use_itm_head=False)[0]
```
</details>

## BibTex and citation info

```
@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}
}
```