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https://github.com/xinyu1205/Recognize_Anything-Tag2Text

Open-source and strong foundation image recognition models.
https://github.com/xinyu1205/Recognize_Anything-Tag2Text

recognize-anything tag2text-iclr2024

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Open-source and strong foundation image recognition models.

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README 

        
#  :label: Recognize Anything Model 



This project aims to develop a series of open-source and strong fundamental image recognition models.



[![Training Dataset](https://img.shields.io/badge/📦-Training%20Dataset-orange.svg)](#open_book-training-datasets)

[![Tag List](https://img.shields.io/badge/🏷️-4585%20Tags-green.svg)](ram/data/ram_tag_list.txt)

[![Web Demo](https://img.shields.io/badge/🤗-HuggingFace%20Space-cyan.svg)](https://huggingface.co/spaces/xinyu1205/Recognize_Anything-Tag2Text)

[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mhd-medfa/recognize-anything/blob/main/recognize_anything_demo.ipynb)

[![Open in Bohrium](https://cdn.dp.tech/bohrium/web/static/images/open-in-bohrium.svg)](https://bohrium.dp.tech/notebooks/63116114759)



- **Recognize Anything Plus Model (RAM++)** [[Paper](https://arxiv.org/abs/2310.15200)] 



  RAM++ is the next generation of RAM, which can **recognize any category with high accuracy**, including **both predefined common categories and diverse open-set categories**.



- **Recognize Anything Model (RAM)** [[Paper](https://arxiv.org/abs/2306.03514)][[Demo](https://huggingface.co/spaces/xinyu1205/recognize-anything)] 



  RAM is an image tagging model, which can **recognize any common category with high accuracy**.

  

  RAM is accepted at **CVPR 2024 Multimodal Foundation Models Workshop**.



- **Tag2Text (ICLR 2024)**  [[Paper](https://arxiv.org/abs/2303.05657)] [[Demo](https://huggingface.co/spaces/xinyu1205/recognize-anything)]



  Tag2Text is a vision-language model guided by tagging, which can **support tagging and comprehensive captioning simultaneously**.

  

  Tag2Text is accepted at **ICLR 2024!** See you in Vienna!



## :bulb: Highlight



### **Superior Image Recognition Capability** 



RAM++ outperforms existing SOTA image fundamental recognition models on common tag categories, uncommon tag categories, and  human-object interaction phrases.





 


  

    

  



   
Comparison of zero-shot image recognition performance.



### **Strong Visual Semantic Analysis** 



We have combined Tag2Text and RAM with localization models (Grounding-DINO and SAM) and developed a strong visual semantic analysis pipeline in the [Grounded-SAM](https://github.com/IDEA-Research/Grounded-Segment-Anything) project.



![](./images/ram_grounded_sam.jpg)



## :sunrise: Model Zoo



RAM++

 



RAM++ is the next generation of RAM, which can recognize any category with high accuracy, including both predefined common categories and diverse open-set categories.



- **For Common Predefined Categoies.** RAM++ exhibits exceptional image tagging capabilities with powerful zero-shot generalization, which maintains the same capabilities as RAM.

    

- **For Diverse Open-set Categoires.** RAM++ achieves notably enhancements beyond CLIP and RAM.

    





 


  

    

  



  
(Green color means fully supervised learning and others means zero-shot performance.)





 


  

    

  



  
RAM++ demonstrate a significant improvement in open-set category recognition.



RAM



RAM is a strong image tagging model, which can recognize any common category with high accuracy.

- **Strong and general.** RAM exhibits exceptional image tagging capabilities with powerful zero-shot generalization;

    - RAM showcases impressive zero-shot performance, significantly outperforming CLIP and BLIP.

    - RAM even surpasses the fully supervised manners (ML-Decoder).

    - RAM exhibits competitive performance with the Google tagging API.

- **Reproducible and affordable.** RAM requires Low reproduction cost with open-source and annotation-free dataset;

- **Flexible and versatile.** RAM offers remarkable flexibility, catering to various application scenarios.





 


  

    

  



  
(Green color means fully supervised learning and Blue color means zero-shot performance.)





 


  

    

  



RAM significantly improves the tagging ability based on the Tag2text framework.

- **Accuracy.** RAM utilizes a **data engine** to **generate** additional annotations and **clean** incorrect ones,  **higher accuracy** compared to Tag2Text.

- **Scope.** RAM upgrades the number of fixed tags from  3,400+ to **[6,400+](./ram/data/ram_tag_list.txt)** (synonymous reduction to 4,500+ different semantic tags), covering **more valuable categories**.

  Moreover, RAM is equipped with **open-set capability**, feasible to recognize tags not seen during training



Tag2text



Tag2Text is an efficient and controllable vision-language model with tagging guidance.

- **Tagging.** Tag2Text recognizes **[3,400+](./ram/data/tag2text_ori_tag_list.txt)** commonly human-used categories without manual annotations.

- **Captioning.** Tag2Text integrates **tags information** into text generation as the **guiding elements**, resulting in **more controllable and comprehensive descriptions**. 

- **Retrieval.** Tag2Text provides **tags** as **additional visible alignment indicators** for image-text retrieval. 





 


  

    

  



    
Tag2Text generate more comprehensive captions with tagging guidance.





 


  

    

  



    
Tag2Text provides tags as additional visible alignment indicators.



## :open_book: Training Datasets



### **Image Texts and Tags** 



These annotation files come from the [Tag2Text](https://arxiv.org/abs/2303.05657) and [RAM](https://recognize-anything.github.io/). Tag2Text automatically extracts image tags from image-text pairs. RAM further augments both tags and texts via an automatic data engine.



| DataSet  | Size    | Images | Texts | Tags  |

|----------|---------|--------|-------|-------|

| [COCO](https://huggingface.co/datasets/xinyu1205/recognize-anything-dataset/blob/main/coco_train_rmcocodev_ram.json)     | 168 MB  | 113K   | 680K  | 3.2M  |

| [VG](https://huggingface.co/datasets/xinyu1205/recognize-anything-dataset/blob/main/vg_ram.json)       | 55 MB   | 100K   | 923K  | 2.7M  |

| [SBU](https://huggingface.co/datasets/xinyu1205/recognize-anything-dataset/blob/main/sbu_ram.json)      | 234 MB  | 849K   | 1.7M  | 7.6M  |

| [CC3M](https://huggingface.co/datasets/xinyu1205/recognize-anything-dataset/blob/main/cc3m_train_ram.json)     | 766 MB  | 2.8M   | 5.6M  | 28.2M |

| [CC3M-val](https://huggingface.co/datasets/xinyu1205/recognize-anything-dataset/blob/main/cc3m_val_ram.json) | 3.5 MB  | 12K    | 26K   | 132K  |



CC12M to be released in the next update.



### **LLM Tag Descriptions** 



These tag descriptions files come from the [RAM++](https://arxiv.org/abs/2310.15200) by calling GPT api. You can also customize any tag categories by [generate_tag_des_llm.py](generate_tag_des_llm.py).



| Tag Descriptions    | Tag List |

|---------------------|----------|

| [RAM Tag List](https://huggingface.co/datasets/xinyu1205/recognize-anything-plus-model-tag-descriptions/blob/main/ram_tag_list_4585_llm_tag_descriptions.json)        | [4,585](ram/data/ram_tag_list.txt)    |

| [OpenImages Uncommon](./datasets/openimages_rare_200/openimages_rare_200_llm_tag_descriptions.json) | [200](datasets/openimages_rare_200/openimages_rare_200_ram_taglist.txt)      |



## :toolbox: Checkpoints

Note : you need to create 'pretrained' folder and download these checkpoints into this folder.



  

    

      

      Name

      Backbone

      Data

      Illustration

      Checkpoint

    

  

  

    

      1

      RAM++ (14M)

      Swin-Large

      COCO, VG, SBU, CC3M, CC3M-val, CC12M

      Provide strong image tagging ability for any category.

      Download  link

    

    

      2

      RAM (14M)

      Swin-Large

      COCO, VG, SBU, CC3M, CC3M-val, CC12M

      Provide strong image tagging ability for common category.

      Download  link

    

    

      3

      Tag2Text (14M)

      Swin-Base

      COCO, VG, SBU, CC3M, CC3M-val, CC12M

      Support comprehensive captioning and tagging.

      Download  link

    

  



## :running: Model Inference



### **Setting Up** ###



1. Create and activate a Conda environment:



```bash

conda create -n recognize-anything python=3.8 -y

conda activate recognize-anything

```



2. Install `recognize-anything` as a package:



```bash

pip install git+https://github.com/xinyu1205/recognize-anything.git

```



3. Or, for development, you may build from source:



```bash

git clone https://github.com/xinyu1205/recognize-anything.git

cd recognize-anything

pip install -e .

```



Then the RAM++, RAM, and Tag2Text models can be imported in other projects:



```python

from ram.models import ram_plus, ram, tag2text

```



### **RAM++ Inference** ###



Get the English and Chinese outputs of the images:



```bash

python inference_ram_plus.py --image images/demo/demo1.jpg --pretrained pretrained/ram_plus_swin_large_14m.pth

```



The output will look like the following:



```

Image Tags:  armchair | blanket | lamp | carpet | couch | dog | gray | green | hassock | home | lay | living room | picture frame | pillow | plant | room | wall lamp | sit | wood floor

图像标签:  扶手椅  | 毯子/覆盖层 | 灯  | 地毯  | 沙发 | 狗 | 灰色 | 绿色  | 坐垫/搁脚凳/草丛 | 家/住宅 | 躺  | 客厅  | 相框  | 枕头  | 植物  | 房间  | 壁灯  | 坐/放置/坐落 | 木地板

```



### **RAM++ Inference on Unseen Categories (Open-Set)** ##



1. Get the [OpenImages-Uncommon categories](./datasets/openimages_rare_200/openimages_rare_200_ram_taglist.txt) of the image:



We have released the LLM tag descriptions of OpenImages-Uncommon categories in [openimages_rare_200_llm_tag_descriptions](./datasets/openimages_rare_200/).





python inference_ram_plus_openset.py  --image images/openset_example.jpg \

--pretrained pretrained/ram_plus_swin_large_14m.pth \

--llm_tag_des datasets/openimages_rare_200/openimages_rare_200_llm_tag_descriptions.json



The output will look like the following:

```

Image Tags: Close-up | Compact car | Go-kart | Horse racing | Sport utility vehicle | Touring car

```



2. You can also customize any tag categories for recognition through tag descriptions:



Modify [categories](./generate_tag_des_llm.py#L56), and call GPT api to generate corresponding tag descriptions:





python generate_tag_des_llm.py \

--openai_api_key 'your openai api key' \

--output_file_path datasets/openimages_rare_200/openimages_rare_200_llm_tag_descriptions.json



RAM Inference



Get the English and Chinese outputs of the images:





python inference_ram.py  --image images/demo/demo1.jpg \

--pretrained pretrained/ram_swin_large_14m.pth



The output will look like the following:



```

Image Tags:  armchair | blanket | lamp | carpet | couch | dog | floor | furniture | gray | green | living room | picture frame | pillow | plant | room | sit | stool | wood floor

图像标签:  扶手椅  | 毯子/覆盖层 | 灯  | 地毯  | 沙发 | 狗 | 地板/地面 | 家具  | 灰色 | 绿色  | 客厅  | 相框  | 枕头  | 植物  | 房间  | 坐/放置/坐落 | 凳子  | 木地板 

```



RAM Inference on Unseen Categories (Open-Set)



Firstly, custom recognition categories in [build_openset_label_embedding](./ram/utils/openset_utils.py), then get the tags of the images:





python inference_ram_openset.py  --image images/openset_example.jpg \

--pretrained pretrained/ram_swin_large_14m.pth



The output will look like the following:

```

Image Tags: Black-and-white | Go-kart

```



Tag2Text Inference



Get the tagging and captioning results:



python inference_tag2text.py  --image images/demo/demo1.jpg \

--pretrained pretrained/tag2text_swin_14m.pth



Or get the tagging and sepcifed captioning results (optional):

python inference_tag2text.py  --image images/demo/demo1.jpg \

--pretrained pretrained/tag2text_swin_14m.pth \

--specified-tags "cloud,sky"



### **Batch Inference and Evaluation** ##

We release two datasets `OpenImages-common` (214 common tag classes) and `OpenImages-rare` (200 uncommon tag classes). Copy or sym-link test images of [OpenImages v6](https://storage.googleapis.com/openimages/web/download_v6.html) to `datasets/openimages_common_214/imgs/` and `datasets/openimages_rare_200/imgs`.



To evaluate RAM++ on `OpenImages-common`:



```bash

python batch_inference.py \

  --model-type ram_plus \

  --checkpoint pretrained/ram_plus_swin_large_14m.pth \

  --dataset openimages_common_214 \

  --output-dir outputs/ram_plus

```



To evaluate RAM++ open-set capability on `OpenImages-rare`:



```bash

python batch_inference.py \

  --model-type ram_plus \

  -- pretrained/ram_plus_swin_large_14m.pth \

  --open-set \

  --dataset openimages_rare_200 \

  --output-dir outputs/ram_plus_openset

```



To evaluate RAM on `OpenImages-common`:



```bash

python batch_inference.py \

  --model-type ram \

  -- pretrained/ram_swin_large_14m.pth \

  --dataset openimages_common_214 \

  --output-dir outputs/ram

```



To evaluate RAM open-set capability on `OpenImages-rare`:



```bash

python batch_inference.py \

  --model-type ram \

  -- pretrained/ram_swin_large_14m.pth \

  --open-set \

  --dataset openimages_rare_200 \

  --output-dir outputs/ram_openset

```



To evaluate Tag2Text on `OpenImages-common`:



```bash

python batch_inference.py \

  --model-type tag2text \

  -- pretrained/tag2text_swin_14m.pth \

  --dataset openimages_common_214 \

  --output-dir outputs/tag2text

```



Please refer to `batch_inference.py` for more options. To get P/R in table 3 of RAM paper, pass `--threshold=0.86` for RAM and `--threshold=0.68` for Tag2Text.



To batch inference custom images, you can set up you own datasets following the given two datasets.



## :golfing: Model Training/Finetuning



### **RAM++** ##



1. Download [RAM training datasets](#open_book-training-datasets) where each json file contains a list. Each item in the list is a dictonary with three key-value pairs: {'image_path': path_of_image, 'caption': text_of_image, 'union_label_id': image tags for tagging which including parsed tags and pseudo tags }.



2. In ram/configs/pretrain.yaml, set 'train_file' as the paths for the json files.



3. Prepare [pretained Swin-Transformer](https://github.com/microsoft/Swin-Transformer), and set 'ckpt' in ram/configs/swin.



4. Download RAM++ frozen tag embedding file "[ram_plus_tag_embedding_class_4585_des_51.pth](https://huggingface.co/xinyu1205/recognize-anything-plus-model/blob/main/ram_plus_tag_embedding_class_4585_des_51.pth)", and set file in "ram/data/frozen_tag_embedding/ram_plus_tag_embedding_class_4585_des_51.pth"



5. Pre-train the model using 8 A100 GPUs:



```bash

python -m torch.distributed.run --nproc_per_node=8 pretrain.py \

  --model-type ram_plus \

  --config ram/configs/pretrain.yaml  \

  --output-dir outputs/ram_plus

```



6. Fine-tune the pre-trained checkpoint using 8 A100 GPUs:



```bash

python -m torch.distributed.run --nproc_per_node=8 finetune.py \

  --model-type ram_plus \

  --config ram/configs/finetune.yaml  \

  --checkpoint outputs/ram_plus/checkpoint_04.pth \

  --output-dir outputs/ram_plus_ft

```



RAM 



1. Download [RAM training datasets](#open_book-training-datasets) where each json file contains a list. Each item in the list is a dictonary with four key-value pairs: {'image_path': path_of_image, 'caption': text_of_image, 'union_label_id': image tags for tagging which including parsed tags and pseudo tags, 'parse_label_id': image tags parsed from caption }.



2. In ram/configs/pretrain.yaml, set 'train_file' as the paths for the json files.



3. Prepare [pretained Swin-Transformer](https://github.com/microsoft/Swin-Transformer), and set 'ckpt' in ram/configs/swin.



4. Download RAM frozen tag embedding file "[ram_tag_embedding_class_4585.pth](https://huggingface.co/xinyu1205/recognize_anything_model/blob/main/ram_tag_embedding_class_4585.pth)", and set file in "ram/data/frozen_tag_embedding/ram_tag_embedding_class_4585.pth"



5. Pre-train the model using 8 A100 GPUs:



```bash

python -m torch.distributed.run --nproc_per_node=8 pretrain.py \

  --model-type ram \

  --config ram/configs/pretrain.yaml  \

  --output-dir outputs/ram

```



6. Fine-tune the pre-trained checkpoint using 8 A100 GPUs:



```bash

python -m torch.distributed.run --nproc_per_node=8 finetune.py \

  --model-type ram \

  --config ram/configs/finetune.yaml  \

  --checkpoint outputs/ram/checkpoint_04.pth \

  --output-dir outputs/ram_ft

```



Tag2Text 



1. Download [RAM training datasets](#open_book-training-datasets) where each json file contains a list. Each item in the list is a dictonary with three key-value pairs: {'image_path': path_of_image, 'caption': text_of_image, 'parse_label_id': image tags parsed from caption }.



2. In ram/configs/pretrain_tag2text.yaml, set 'train_file' as the paths for the json files.



3. Prepare [pretained Swin-Transformer](https://github.com/microsoft/Swin-Transformer), and set 'ckpt' in ram/configs/swin.



4. Pre-train the model using 8 A100 GPUs:



```bash

python -m torch.distributed.run --nproc_per_node=8 pretrain.py \

  --model-type tag2text \

  --config ram/configs/pretrain_tag2text.yaml  \

  --output-dir outputs/tag2text

```



5. Fine-tune the pre-trained checkpoint using 8 A100 GPUs:



```bash

python -m torch.distributed.run --nproc_per_node=8 finetune.py \

  --model-type tag2text \

  --config ram/configs/finetune_tag2text.yaml  \

  --checkpoint outputs/tag2text/checkpoint_04.pth \

  --output-dir outputs/tag2text_ft

```



## :black_nib: Citation

If you find our work to be useful for your research, please consider citing.



```

@article{huang2023open,

  title={Open-Set Image Tagging with Multi-Grained Text Supervision},

  author={Huang, Xinyu and Huang, Yi-Jie and Zhang, Youcai and Tian, Weiwei and Feng, Rui and Zhang, Yuejie and Xie, Yanchun and Li, Yaqian and Zhang, Lei},

  journal={arXiv e-prints},

  pages={arXiv--2310},

  year={2023}

}



@article{zhang2023recognize,

  title={Recognize Anything: A Strong Image Tagging Model},

  author={Zhang, Youcai and Huang, Xinyu and Ma, Jinyu and Li, Zhaoyang and Luo, Zhaochuan and Xie, Yanchun and Qin, Yuzhuo and Luo, Tong and Li, Yaqian and Liu, Shilong and others},

  journal={arXiv preprint arXiv:2306.03514},

  year={2023}

}



@article{huang2023tag2text,

  title={Tag2Text: Guiding Vision-Language Model via Image Tagging},

  author={Huang, Xinyu and Zhang, Youcai and Ma, Jinyu and Tian, Weiwei and Feng, Rui and Zhang, Yuejie and Li, Yaqian and Guo, Yandong and Zhang, Lei},

  journal={arXiv preprint arXiv:2303.05657},

  year={2023}

}

```



## :hearts: Acknowledgements

This work is done with the help of the amazing code base of [BLIP](https://github.com/salesforce/BLIP), thanks very much!



We want to thank @Cheng Rui @Shilong Liu @Ren Tianhe for their help in [marrying RAM/Tag2Text with Grounded-SAM](https://github.com/IDEA-Research/Grounded-Segment-Anything).



We also want to thank [Ask-Anything](https://github.com/OpenGVLab/Ask-Anything), [Prompt-can-anything](https://github.com/positive666/Prompt-Can-Anything) for  combining RAM/Tag2Text, which greatly expands the application boundaries of RAM/Tag2Text.