Create read.md

# Long-CLIP
This repository is the official implementation of Long-CLIP

**Long-CLIP: Unlocking the Long-Text Capability of CLIP**\
[Beichen Zhang](https://beichenzbc.github.io), [Pan Zhang](https://panzhang0212.github.io/), [Xiaoyi Dong](https://lightdxy.github.io/), [Yuhang Zang](https://yuhangzang.github.io/), [Jiaqi Wang](https://myownskyw7.github.io/)

## 💡 Highlights
- 🔥 **Long Input length** Increase the maximum input length of CLIP from **77** to **248**.
- 🔥 **Strong Performace** Improve the R@5 of long-caption text-image retrieval by **20%** and traditional text-image retrieval by **6%**.
- 🔥 **Plug-in and play** Can be directly applied in **any work** that requires long-text capability.


## 📜 News
🚀 [2024/7/3] Our paper has been accepted by ***ECCV2024***.

🚀 [2024/7/3] We release the code of using Long-CLIP in ***SDXL***. For detailed information, you may refer to `SDXL/SDXL.md`.

🚀 [2024/5/21] We update the paper and checkpoints after fixing the bug in DDP and add results in Urban-1k. Special thanks to

@MajorDavidZhang
for finding and refining this bug in DDP! Now the fine-tuning only takes ***0.5*** hours on *8 GPUs*!

🚀 [2024/5/21] Urban-1k: a scaling-up version of Urban-200 dataset in the paper has been released at this [page](https://huggingface.co/datasets/BeichenZhang/Urban1k).

🚀 [2024/4/1] The training code is released!

🚀 [2024/3/25] The Inference code and models ([LongCLIP-B](https://huggingface.co/BeichenZhang/LongCLIP-B) and [LongCLIP-L](https://huggingface.co/BeichenZhang/LongCLIP-L)) are released!

🚀 [2024/3/25] The [paper](https://arxiv.org/abs/2403.15378) is released!

## 👨‍💻 Todo
- [x] Training code for Long-CLIP based on OpenAI-CLIP
- [x] Evaluation code for Long-CLIP
- [x] evaluation code for zero-shot classification and text-image retrieval tasks.
- [x] Usage example of Long-CLIP
- [x] Checkpoints of Long-CLIP


## 🛠️ Usage

### Installation

Our model is based on [CLIP](https://github.com/openai/CLIP), please prepare environment for CLIP.


### how to use

Please first clone our [repo](https://github.com/beichenzbc/Long-CLIP) from github by running the following command.

```shell
git clone https://github.com/beichenzbc/Long-CLIP.git
cd Long-CLIP
```

Then, download the checkpoints of our model [LongCLIP-B](https://huggingface.co/BeichenZhang/LongCLIP-B) and/or [LongCLIP-L](https://huggingface.co/BeichenZhang/LongCLIP-L) and place it under `./checkpoints`

```python
from model import longclip
import torch
from PIL import Image

device = "cuda" if torch.cuda.is_available() else "cpu"
model, preprocess = longclip.load("./checkpoints/longclip-B.pt", device=device)

text = longclip.tokenize(["A man is crossing the street with a red car parked nearby.", "A man is driving a car in an urban scene."]).to(device)
image = preprocess(Image.open("./img/demo.png")).unsqueeze(0).to(device)

with torch.no_grad():
image_features = model.encode_image(image)
text_features = model.encode_text(text)

logits_per_image = image_features @ text_features.T
probs = logits_per_image.softmax(dim=-1).cpu().numpy()

print("Label probs:", probs)
```

### Evaluation
#### Zero-shot classification

To run zero-shot classification on imagenet dataset, run the following command after preparing the data
```shell
cd eval/classification/imagenet
python imagenet.py
```

Similarly, run the following command for cifar datset
```shell
cd eval/classification/cifar
python cifar10.py #cifar10
python cifar100.py #cifar100
```

#### Retrieval
To run text-image retrieval on COCO2017 or Flickr30k, run the following command after preparing the data
```shell
cd eval/retrieval
python coco.py #COCO2017
python flickr30k.py #Flickr30k
```
### Traning
Please refer to `train/train.md` for training details.

## ⭐ Demos
### Long-CLIP-SDXL
<p align="center"> <a>
<img src="./img/demo_SDXL.png" width="900" />
</a> </p>

### Long-caption text-image retrieval
<p align="center"> <a>
<img src="./img/retrieval.png" width="900" />
</a> </p>

### Plug-and-Play text to image generation
<p align="center"> <a>
<img src="./img/generation.png" width="900" />
</a> </p>


## Citation
If you find our work helpful for your research, please consider giving a citation:
```


@article
{zhang2024longclip,
title={Long-CLIP: Unlocking the Long-Text Capability of CLIP},
author={Beichen Zhang and Pan Zhang and Xiaoyi Dong and Yuhang Zang and Jiaqi Wang},
journal={arXiv preprint arXiv:2403.15378},
year={2024}
}
```

read.md

@@ -0,0 +1,127 @@
+# Long-CLIP
+This repository is the official implementation of Long-CLIP
+
+**Long-CLIP: Unlocking the Long-Text Capability of CLIP**\
+[Beichen Zhang](https://beichenzbc.github.io), [Pan Zhang](https://panzhang0212.github.io/), [Xiaoyi Dong](https://lightdxy.github.io/), [Yuhang Zang](https://yuhangzang.github.io/), [Jiaqi Wang](https://myownskyw7.github.io/)
+
+## 💡 Highlights
+- 🔥 **Long Input length** Increase the maximum input length of CLIP from **77** to **248**.
+- 🔥 **Strong Performace** Improve the R@5 of long-caption text-image retrieval by **20%** and traditional text-image retrieval by **6%**.
+- 🔥 **Plug-in and play** Can be directly applied in **any work** that requires long-text capability.
+
+
+## 📜 News
+🚀 [2024/7/3] Our paper has been accepted by ***ECCV2024***.
+
+🚀 [2024/7/3] We release the code of using Long-CLIP in ***SDXL***. For detailed information, you may refer to `SDXL/SDXL.md`.
+
+🚀 [2024/5/21] We update the paper and checkpoints after fixing the bug in DDP and add results in Urban-1k. Special thanks to @MajorDavidZhang for finding and refining this bug in DDP! Now the fine-tuning only takes ***0.5*** hours on *8 GPUs*!
+
+🚀 [2024/5/21] Urban-1k: a scaling-up version of Urban-200 dataset in the paper has been released at this [page](https://huggingface.co/datasets/BeichenZhang/Urban1k).
+
+🚀 [2024/4/1] The training code is released!
+
+🚀 [2024/3/25] The Inference code and models ([LongCLIP-B](https://huggingface.co/BeichenZhang/LongCLIP-B) and [LongCLIP-L](https://huggingface.co/BeichenZhang/LongCLIP-L)) are released!
+
+🚀 [2024/3/25] The [paper](https://arxiv.org/abs/2403.15378) is released!
+
+## 👨‍💻 Todo
+- [x] Training code for Long-CLIP based on OpenAI-CLIP
+- [x] Evaluation code for Long-CLIP
+- [x] evaluation code for zero-shot classification and text-image retrieval tasks.
+- [x] Usage example of Long-CLIP
+- [x] Checkpoints of Long-CLIP
+
+
+## 🛠️ Usage
+
+### Installation
+
+Our model is based on [CLIP](https://github.com/openai/CLIP), please prepare environment for CLIP.
+
+
+### how to use
+
+Please first clone our [repo](https://github.com/beichenzbc/Long-CLIP) from github by running the following command.
+
+```shell
+git clone https://github.com/beichenzbc/Long-CLIP.git
+cd Long-CLIP
+```
+
+Then, download the checkpoints of our model [LongCLIP-B](https://huggingface.co/BeichenZhang/LongCLIP-B) and/or [LongCLIP-L](https://huggingface.co/BeichenZhang/LongCLIP-L) and place it under `./checkpoints`
+
+```python
+from model import longclip
+import torch
+from PIL import Image
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model, preprocess = longclip.load("./checkpoints/longclip-B.pt", device=device)
+
+text = longclip.tokenize(["A man is crossing the street with a red car parked nearby.", "A man is driving a car in an urban scene."]).to(device)
+image = preprocess(Image.open("./img/demo.png")).unsqueeze(0).to(device)
+
+with torch.no_grad():
+    image_features = model.encode_image(image)
+    text_features = model.encode_text(text)
+    
+    logits_per_image = image_features @ text_features.T
+    probs = logits_per_image.softmax(dim=-1).cpu().numpy()
+
+print("Label probs:", probs) 
+```
+
+### Evaluation
+#### Zero-shot classification
+
+To run zero-shot classification on imagenet dataset, run the following command after preparing the data
+```shell
+cd eval/classification/imagenet
+python imagenet.py
+```
+
+Similarly, run the following command for cifar datset
+```shell
+cd eval/classification/cifar
+python cifar10.py               #cifar10
+python cifar100.py              #cifar100
+```
+
+#### Retrieval
+To run text-image retrieval on COCO2017 or Flickr30k, run the following command after preparing the data
+```shell
+cd eval/retrieval
+python coco.py                  #COCO2017
+python flickr30k.py             #Flickr30k
+```
+### Traning
+Please refer to `train/train.md` for training details.
+
+## ⭐ Demos
+### Long-CLIP-SDXL
+<p align="center"> <a>  
+<img src="./img/demo_SDXL.png"  width="900" />
+</a> </p>
+
+### Long-caption text-image retrieval 
+<p align="center"> <a>  
+<img src="./img/retrieval.png"  width="900" />
+</a> </p>
+
+### Plug-and-Play text to image generation 
+<p align="center"> <a>  
+<img src="./img/generation.png"  width="900" />
+</a> </p>
+
+
+## Citation
+If you find our work helpful for your research, please consider giving a citation:
+```
+@article{zhang2024longclip,
+        title={Long-CLIP: Unlocking the Long-Text Capability of CLIP},
+        author={Beichen Zhang and Pan Zhang and Xiaoyi Dong and Yuhang Zang and Jiaqi Wang},
+        journal={arXiv preprint arXiv:2403.15378},
+        year={2024}
+}
+```