diff --git a/README.md b/README.md index e6bea46..2db1448 100644 --- a/README.md +++ b/README.md @@ -1,31 +1,46 @@
-TexTeller is a ViT-based model designed for end-to-end formula recognition. It can recognize formulas in natural images and convert them into LaTeX-style formulas. +TexTeller is an end-to-end formula recognition model based on ViT, capable of converting images into corresponding LaTeX formulas. -TexTeller is trained on a larger dataset of image-formula pairs (a 550K dataset available [here](https://huggingface.co/datasets/OleehyO/latex-formulas)), **exhibits superior generalization ability and higher accuracy compared to [LaTeX-OCR](https://github.com/lukas-blecher/LaTeX-OCR)**, which uses approximately 100K data points. This larger dataset enables TexTeller to cover most usage scenarios more effectively( **excluding scanned images and handwritten formulas** ). -> A TexTeller checkpoint trained on a 5.5M dataset will be released soon. +TexTeller was trained with ~~550K~~7.5M image-formula pairs (dataset available [here](https://huggingface.co/datasets/OleehyO/latex-formulas)), compared to [LaTeX-OCR](https://github.com/lukas-blecher/LaTeX-OCR) which used a 100K dataset, TexTeller has **stronger generalization abilities** and **higher accuracy**, covering most use cases (**except for scanned images and handwritten formulas**). -## Prerequisites +> ~~We will soon release a TexTeller checkpoint trained on a 7.5M dataset~~ + +## 🔄 Change Log + +* 📮[2024-03-24] TexTeller 2.0 released! The training data for TexTeller 2.0 has been increased to 7.5M (about **15 times more** than TexTeller 1.0 and also improved in data quality). The trained TexTeller 2.0 demonstrated **superior performance** in the test set, especially in recognizing rare symbols, complex multi-line formulas, and matrices. + +## 🔑 Prerequisites python=3.10 pytorch -> Note: Only CUDA version >= 12.0 have been fully tested, so we recommend using CUDA version>=12.0 +> Note: Only CUDA versions >= 12.0 have been fully tested, so it is recommended to use CUDA version >= 12.0 -## Getting Started +## 🖼 About Rendering LaTeX as Images + +* **Install XeLaTex** and ensure `xelatex` can be called directly from the command line. + +* To ensure correct rendering of the predicted formulas, **include the following packages** in your `.tex` file: + + ```tex + \usepackage{multirow,multicol,amsmath,amsfonts,amssymb,mathtools,bm,mathrsfs,wasysym,amsbsy,upgreek,mathalfa,stmaryrd,mathrsfs,dsfont,amsthm,amsmath,multirow} + ``` + +## 🚀 Getting Started 1. Clone the repository: @@ -33,13 +48,13 @@ pytorch git clone https://github.com/OleehyO/TexTeller ``` -2. After [pytorch installation](https://pytorch.org/get-started/locally/#start-locally), install the required packages: +2. After [installing pytorch](https://pytorch.org/get-started/locally/#start-locally), install the project's dependencies: ```bash pip install -r requirements.txt ``` -3. Navigate to the `TexTeller/src` directory and run the following command to perform inference: +3. Enter the `TexTeller/src` directory and run the following command in the terminal to start inference: ```bash python inference.py -img "/path/to/image.{jpg,png}" @@ -47,87 +62,104 @@ pytorch #+e.g. python inference.py -img "./img.jpg" -cuda ``` - > Checkpoints will be downloaded in your first run. + > The first time you run it, the required checkpoints will be downloaded from Hugging Face -## Web Demo +## 🌐 Web Demo -You can also run the web demo by navigating to the `TexTeller/src` directory and running the following command: +To start the web demo, you need to first enter the `TexTeller/src` directory, then run the following command ```bash ./start_web.sh ``` -Then go to `http://localhost:8501` in your browser to run TexTeller in the web. +Then, enter `http://localhost:8501` in your browser to see the web demo -> You can change the default settings in `start_web.sh`, such as inference with GPU(e.g. `USE_CUDA=True`) or increase the number of beams(e.g. `NUM_BEAM=3`) for higher accuracy. +> You can change the default configuration of `start_web.sh`, for example, to use GPU for inference (e.g. `USE_CUDA=True`) or to increase the number of beams (e.g. `NUM_BEAM=3`) to achieve higher accuracy -## API +**NOTE:** If you want to directly render the prediction results as images on the web (for example, to check if the prediction is correct), you need to ensure [xelatex is correctly installed](https://github.com/OleehyO/TexTeller?tab=readme-ov-file#Rendering-Predicted-Results) -We use [ray serve](https://github.com/ray-project/ray) to provide a simple API for using TexTeller in your own projects. To start the server, navigate to the `TexTeller/src` directory and run the following command: +## 📡 API Usage + +We use [ray serve](https://github.com/ray-project/ray) to provide an API interface for TexTeller, allowing you to integrate TexTeller into your own projects. To start the server, you first need to enter the `TexTeller/src` directory and then run the following command: ```bash python server.py # default settings ``` -You can pass the following arguments to the `server.py` script to get custom inference settings(e.g. `python server.py --use_gpu` to enable GPU inference): +You can pass the following arguments to `server.py` to change the server's inference settings (e.g. `python server.py --use_gpu` to enable GPU inference): -| Argument | Description | +| Parameter | Description | | --- | --- | -| `-ckpt` | Path to the checkpoint file to load, default is TexTeller pretrained model. | -| `-tknz` | Path to the tokenizer, default is TexTeller tokenizer. | -| `-port` | Port number to run the server on, *default is 8000*. | -| `--use_gpu` | Whether to use GPU for inference. | -| `--num_beams` | Number of beams to use for beam search decoding, *default is 1*. | -| `--num_replicas` | Number of replicas to run the server on, *default is 1*. You can use this to get higher throughput. | -| `--ncpu_per_replica` | Number of CPU cores to use per replica, *default is 1*. | -| `--ngpu_per_replica` | Number of GPUs to use per replica, *default is 1*. You can set this to 0~1 to run multiple replicas on a single GPU(if --num_replicas 2, --ngpu_per_replica 0.7, then 2 gpus are required) | +| `-ckpt` | The path to the weights file, *default is TexTeller's pretrained weights*.| +| `-tknz` | The path to the tokenizer, *default is TexTeller's tokenizer*.| +| `-port` | The server's service port, *default is 8000*. | +| `--use_gpu` | Whether to use GPU for inference, *default is CPU*. | +| `--num_beams` | The number of beams for beam search, *default is 1*. | +| `--num_replicas` | The number of service replicas to run on the server, *default is 1 replica*. You can use more replicas to achieve greater throughput.| +| `--ncpu_per_replica` | The number of CPU cores used per service replica, *default is 1*. | +| `--ngpu_per_replica` | The number of GPUs used per service replica, *default is 1*. You can set this value between 0 and 1 to run multiple service replicas on one GPU to share the GPU, thereby improving GPU utilization. (Note, if --num_replicas is 2, --ngpu_per_replica is 0.7, then 2 GPUs must be available) | -> Client demo can be found in `TexTeller/client/demo.py`, you can refer to `demo.py` to send requests to the server. +> A client demo can be found at `TexTeller/client/demo.py`, you can refer to `demo.py` to send requests to the server -## Training +## 🏋️♂️ Training ### Dataset -We provide a dataset example in `TexTeller/src/models/ocr_model/train/dataset`, and you can place your own images in the `images` directory and annotate the corresponding formula for each image in `formulas.jsonl`. +We provide an example dataset in the `TexTeller/src/models/ocr_model/train/dataset` directory, you can place your own images in the `images` directory and annotate each image with its corresponding formula in `formulas.jsonl`. -After the dataset is ready, you should **change the `DIR_URL` variable** in `.../dataset/loader.py` to the path of your dataset. +After preparing your dataset, you need to **change the `DIR_URL` variable to your own dataset's path** in `.../dataset/loader.py` -### Retrain the tokenizer +### Retraining the Tokenizer -If you are using a different dataset, you may need to retrain the tokenizer to match your specific vocabulary. After setting up the dataset, you can do this by: +If you are using a different dataset, you might need to retrain the tokenizer to obtain a different dictionary. After configuring your dataset, you can train your own tokenizer with the following command: -1. Change the line `new_tokenizer.save_pretrained('./your_dir_name')` in `TexTeller/src/models/tokenizer/train.py` to your desired output directory name. - > To use a different vocabulary size, you should modify the `VOCAB_SIZE` parameter in the `TexTeller/src/models/globals.py`. +1. In `TexTeller/src/models/tokenizer/train.py`, change `new_tokenizer.save_pretrained('./your_dir_name')` to your custom output directory + > If you want to use a different dictionary size (default is 10k tokens), you need to change the `VOCAB_SIZE` variable in `TexTeller/src/models/globals.py` -2. Running the following command **under `TexTeller/src` directory**: +2. **In the `TexTeller/src` directory**, run the following command: ```bash python -m models.tokenizer.train ``` -### Train the model +### Training the Model -To train the model, you can run the following command **under `TexTeller/src` directory**: +To train the model, you need to run the following command in the `TexTeller/src` directory: ```bash python -m models.ocr_model.train.train ``` -You can set your own tokenizer and checkpoint path(or fine-tune the default model checkpoint if you don't use your own tokenizer while keeping the same model architecture) in `TexTeller/src/models/ocr_model/train/train.py`. -> Please refer to `train.py` for more details. +You can set your own tokenizer and checkpoint paths in `TexTeller/src/models/ocr_model/train/train.py` (refer to `train.py` for more information). If you are using the same architecture and dictionary as TexTeller, you can also fine-tune TexTeller's default weights with your own dataset. -Model architecture and training hyperparameters can be adjusted in `TexTeller/src/globals.py` and `TexTeller/src/models/ocr_model/train/train_args.py`. +In `TexTeller/src/globals.py` and `TexTeller/src/models/ocr_model/train/train_args.py`, you can change the model's architecture and training hyperparameters. -> We use the [Hugging Face Transformers](https://github.com/huggingface/transformers) library for model training, so you can find more details about the training hyperparameters in their [documentation](https://huggingface.co/docs/transformers/v4.32.1/main_classes/trainer#transformers.TrainingArguments). +> Our training scripts use the [Hugging Face Transformers](https://github.com/huggingface/transformers) library, so you can refer to their [documentation](https://huggingface.co/docs/transformers/v4.32.1/main_classes/trainer#transformers.TrainingArguments) for more details and configurations on training parameters. -## To-Do +## 🚧 Limitations -- [ ] Train our model with a larger amount of data(5.5M samples, and soon to be released). +* Some complex multi-line scenarios are not well handled (e.g., long formulas mixed with matrices) -- [ ] Inference acceleration. +* Does not support scanned images and PDF document recognition + +* Does not support handwritten formulas + +## 📅 Plans + +- [x] ~~Train the model with a larger dataset (7.5M samples, coming soon)~~ + +- [ ] Recognition of scanned images + +- [ ] PDF document recognition + Support for English and Chinese scenarios + +- [ ] Inference acceleration - [ ] ... -## Acknowledgements +## 💖 Acknowledgments Thanks to [LaTeX-OCR](https://github.com/lukas-blecher/LaTeX-OCR) which has brought me a lot of inspiration, and [im2latex-100K](https://zenodo.org/records/56198#.V2px0jXT6eA) which enriches our dataset. + +## ⭐️ Stargazers over time + +[](https://starchart.cc/OleehyO/TexTeller) diff --git a/assets/README_zh.md b/assets/README_zh.md index 260915e..e2b301d 100644 --- a/assets/README_zh.md +++ b/assets/README_zh.md @@ -1,24 +1,28 @@ TexTeller是一个基于ViT的端到端公式识别模型,可以把图片转换为对应的latex公式 -TexTeller用了550K的图片-公式对进行训练(数据集可以在[这里](https://huggingface.co/datasets/OleehyO/latex-formulas)获取),相比于[LaTeX-OCR](https://github.com/lukas-blecher/LaTeX-OCR)(使用了一个100K的数据集),TexTeller具有**更强的泛化能力**以及**更高的准确率**,可以覆盖大部分的使用场景(**扫描图片,手写公式除外**)。 +TexTeller用了~~550K~~7.5M的图片-公式对进行训练(数据集可以在[这里](https://huggingface.co/datasets/OleehyO/latex-formulas)获取),相比于[LaTeX-OCR](https://github.com/lukas-blecher/LaTeX-OCR)(使用了一个100K的数据集),TexTeller具有**更强的泛化能力**以及**更高的准确率**,可以覆盖大部分的使用场景(**扫描图片,手写公式除外**)。 -> 我们马上就会发布一个使用5.5M数据集进行训练的TexTeller checkpoint +> ~~我们马上就会发布一个使用7.5M数据集进行训练的TexTeller checkpoint~~ -## 前置条件 +## 🔄 变更信息 + +* 📮[2024-03-24] TexTeller2.0发布!TexTeller2.0的训练数据增大到了7.5M(相较于TexTeller1.0**增加了~15倍**并且数据质量也有所改善)。训练后的TexTeller2.0在测试集中展现出了**更加优越的性能**,尤其在生僻符号、复杂多行、矩阵的识别场景中。 + +## 🔑 前置条件 python=3.10 @@ -26,7 +30,17 @@ pytorch > 注意: 只有CUDA版本>= 12.0被完全测试过,所以最好使用>= 12.0的CUDA版本 -## Getting Started +## 🖼 关于把latex渲染成图片 + +* **安装XeLaTex** 并确保`xelatex`可以直接被命令行调用。 + +* 为了确保正确渲染预测出的公式, 需要在`.tex`文件中**引入以下宏包**: + + ```tex + \usepackage{multirow,multicol,amsmath,amsfonts,amssymb,mathtools,bm,mathrsfs,wasysym,amsbsy,upgreek,mathalfa,stmaryrd,mathrsfs,dsfont,amsthm,amsmath,multirow} + ``` + +## 🚀 开搞 1. 克隆本仓库: @@ -50,7 +64,7 @@ pytorch > 第一次运行时会在hugging face上下载所需要的checkpoints -## FAQ:无法连接到Hugging Face +## ❓ 常见问题:无法连接到Hugging Face 默认情况下,会在Hugging Face中下载模型权重,**如果你的远端服务器无法连接到Hugging Face**,你可以通过以下命令进行加载: @@ -78,7 +92,7 @@ pytorch 2. 把这个目录上传远端服务器,并在`TexTeller/src/models/ocr_model/utils/metrics.py`中把`evaluate.load('google_bleu')`改为`evaluate.load('your/dir/path/google_bleu.py')` -## Web Demo +## 🌐 网页演示 要想启动web demo,你需要先进入 `TexTeller/src` 目录,然后运行以下命令 @@ -90,7 +104,9 @@ pytorch > 你可以改变`start_web.sh`的默认配置, 例如使用GPU进行推理(e.g. `USE_CUDA=True`) 或者增加beams的数量(e.g. `NUM_BEAM=3`)来获得更高的精确度 -## API +**NOTE:** 如果你想直接把预测结果在网页上渲染成图片(比如为了检查预测结果是否正确)你需要确保[xelatex被正确安装](https://github.com/OleehyO/TexTeller?tab=readme-ov-file#Rendering-Predicted-Results) + +## 📡 API调用 我们使用[ray serve](https://github.com/ray-project/ray)来对外提供一个TexTeller的API接口,通过使用这个接口,你可以把TexTeller整合到自己的项目里。要想启动server,你需要先进入`TexTeller/src`目录然后运行以下命令: @@ -100,28 +116,28 @@ python server.py # default settings 你可以给`server.py`传递以下参数来改变server的推理设置(e.g. `python server.py --use_gpu` 来启动GPU推理): -| Argument | Description | +| 参数 | 描述 | | --- | --- | -| `-ckpt` | Path to the checkpoint file to load, default is TexTeller pretrained model. | -| `-tknz` | Path to the tokenizer, default is TexTeller tokenizer. | -| `-port` | Port number to run the server on, *default is 8000*. | -| `--use_gpu` | Whether to use GPU for inference. | -| `--num_beams` | Number of beams to use for beam search decoding, *default is 1*. | -| `--num_replicas` | Number of replicas to run the server on, *default is 1*. You can use this to get higher throughput. | -| `--ncpu_per_replica` | Number of CPU cores to use per replica, *default is 1*. | -| `--ngpu_per_replica` | Number of GPUs to use per replica, *default is 1*. You can set this to 0~1 to run multiple replicas on a single GPU(if --num_replicas 2, --ngpu_per_replica 0.7, then 2 gpus are required) | +| `-ckpt` | 权重文件的路径,*默认为TexTeller的预训练权重*。| +| `-tknz` | 分词器的路径, *默认为TexTeller的分词器*。| +| `-port` | 服务器的服务端口, *默认是8000*。 | +| `--use_gpu` | 是否使用GPU推理,*默认为CPU*。 | +| `--num_beams` | beam search的beam数量, *默认是1*。 | +| `--num_replicas` | 在服务器上运行的服务副本数量, *默认1个副本*。你可以使用更多的副本来获取更大的吞吐量。| +| `--ncpu_per_replica` | 每个服务副本所用的CPU核心数,*默认为1*。 | +| `--ngpu_per_replica` | 每个服务副本所用的GPU数量,*默认为1*。你可以把这个值设置成 0~1之间的数,这样会在一个GPU上运行多个服务副本来共享GPU,从而提高GPU的利用率。(注意,如果 --num_replicas 2, --ngpu_per_replica 0.7, 那么就必须要有2个GPU可用) | > 一个客户端demo可以在`TexTeller/client/demo.py`找到,你可以参考`demo.py`来给server发送请求 -## Training +## 🏋️♂️ 训练 -### Dataset +### 数据集 我们在`TexTeller/src/models/ocr_model/train/dataset`目录中提供了一个数据集的例子,你可以把自己的图片放在`images`目录然后在`formulas.jsonl`中为每张图片标注对应的公式。 准备好数据集后,你需要在`.../dataset/loader.py`中把 **`DIR_URL`变量改成你自己数据集的路径** -### Retrain the tokenizer +### 重新训练分词器 如果你使用了不一样的数据集,你可能需要重新训练tokenizer来得到一个不一样的字典。配置好数据集后,可以通过以下命令来训练自己的tokenizer: @@ -134,7 +150,7 @@ python server.py # default settings python -m models.tokenizer.train ``` -### Train the model +### 训练模型 要想训练模型, 你需要在`TexTeller/src`目录下运行以下命令: @@ -148,14 +164,38 @@ python -m models.ocr_model.train.train > 我们的训练脚本使用了[Hugging Face Transformers](https://github.com/huggingface/transformers)库, 所以你可以参考他们提供的[文档](https://huggingface.co/docs/transformers/v4.32.1/main_classes/trainer#transformers.TrainingArguments)来获取更多训练参数的细节以及配置。 -## To-Do +## 🚧 不足 -- [ ] 使用更大的数据集来训练模型(5.5M样本,即将发布) +* 部分细节很多的公式无法做到100%的准确率 + +