Upload folder using huggingface_hub

README.md

@@ -0,0 +1,139 @@
+---
+license: apache-2.0
+---
+
+# PicoDet_layout_1x
+
+## Introduction
+
+A high-efficiency layout area localization model trained on a self-built dataset using PicoDet-1x, capable of detecting 5-Class english document area, including Text, Title, Table, Figure, and List. The key metrics are as follow:
+
+| Model| mAP(0.5) (%) | 
+|  --- | --- | 
+|PicoDet_layout_1x |  97.8 | 
+
+
+
+## Quick Start
+
+### Installation
+
+1. PaddlePaddle
+
+Please refer to the following commands to install PaddlePaddle using pip:
+
+```bash
+# for CUDA11.8
+python -m pip install paddlepaddle-gpu==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cu118/
+
+# for CUDA12.6
+python -m pip install paddlepaddle-gpu==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cu126/
+
+# for CPU
+python -m pip install paddlepaddle==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cpu/
+```
+
+For details about PaddlePaddle installation, please refer to the [PaddlePaddle official website](https://www.paddlepaddle.org.cn/en/install/quick).
+
+2. PaddleOCR
+
+Install the latest version of the PaddleOCR inference package from PyPI:
+
+```bash
+python -m pip install paddleocr
+```
+
+
+### Model Usage
+
+You can quickly experience the functionality with a single command:
+
+```bash
+paddleocr layout_detection \
+    --model_name PicoDet_layout_1x \
+    -i https://cdn-uploads.huggingface.co/production/uploads/63d7b8ee07cd1aa3c49a2026/-oU2IpNLcA0gTMJ34wDBR.png
+```
+
+You can also integrate the model inference of the layout detection module into your project. Before running the following code, please download the sample image to your local machine.
+
+```python
+from paddleocr import LayoutDetection
+
+model = LayoutDetection(model_name="PicoDet_layout_1x")
+output = model.predict("-oU2IpNLcA0gTMJ34wDBR.png", batch_size=1, layout_nms=True)
+for res in output:
+    res.print()
+    res.save_to_img(save_path="./output/")
+    res.save_to_json(save_path="./output/res.json")
+```
+
+After running, the obtained result is as follows:
+
+```json
+{'res': {'input_path': '-oU2IpNLcA0gTMJ34wDBR.png', 'page_index': None, 'boxes': [{'cls_id': 0, 'label': 'Text', 'score': 0.9922339916229248, 'coordinate': [84.18926, 1248.3407, 583.2205, 1582.3639]}, {'cls_id': 0, 'label': 'Text', 'score': 0.991148829460144, 'coordinate': [606.0175, 1209.0277, 1105.669, 1538.559]}, {'cls_id': 0, 'label': 'Text', 'score': 0.989486813545227, 'coordinate': [84.11199, 710.821, 582.4992, 1208.4802]}, {'cls_id': 0, 'label': 'Text', 'score': 0.9823781847953796, 'coordinate': [605.9509, 951.964, 1105.8907, 1089.925]}, {'cls_id': 3, 'label': 'Table', 'score': 0.98123699426651, 'coordinate': [608.91614, 301.1981, 1111.2635, 805.45233]}, {'cls_id': 4, 'label': 'Figure', 'score': 0.971710741519928, 'coordinate': [84.329, 200.35394, 577.5106, 681.766]}, {'cls_id': 0, 'label': 'Text', 'score': 0.950641930103302, 'coordinate': [607.27435, 826.2146, 1107.0017, 911.43726]}, {'cls_id': 0, 'label': 'Text', 'score': 0.9468774795532227, 'coordinate': [605.6758, 197.66481, 1105.3107, 265.3606]}, {'cls_id': 1, 'label': 'Title', 'score': 0.86313396692276, 'coordinate': [606.6188, 1170.0265, 827.1196, 1192.0656]}, {'cls_id': 1, 'label': 'Title', 'score': 0.8427881002426147, 'coordinate': [605.2791, 1131.709, 823.0802, 1152.363]}, {'cls_id': 0, 'label': 'Text', 'score': 0.5990690588951111, 'coordinate': [625.3928, 1558.7576, 849.12946, 1583.5391]}]}}
+```
+
+The visualized image is as follows:
+
+![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/63d7b8ee07cd1aa3c49a2026/syzCGwEeqaZ6nqM_u8Qs5.png)
+
+For details about usage command and descriptions of parameters, please refer to the [Document](https://paddlepaddle.github.io/PaddleOCR/latest/en/version3.x/module_usage/layout_detection.html#iii-quick-integration).
+
+### Pipeline Usage
+
+The ability of a single model is limited. But the pipeline consists of several models can provide more capacity to resolve difficult problems in real-world scenJust a few lines of code can experience the inference of the pipelinearios.
+
+#### PP-TableMagic (table_recognition_v2)
+
+The General Table Recognition v2 pipeline (PP-TableMagic) is designed to tackle table recognition tasks, identifying tables in images and outputting them in HTML format. PP-TableMagic includes the following 8 modules:
+
+* Table Structure Recognition Module
+* Table Classification Module
+* Table Cell Detection Module
+* Text Detection Module
+* Text Recognition Module
+* Layout Region Detection Module (optional)
+* Document Image Orientation Classification Module (optional)
+* Text Image Unwarping Module (optional)
+
+You can quickly experience the PP-TableMagic pipeline with a single command.
+
+```bash
+paddleocr table_recognition_v2 -i https://cdn-uploads.huggingface.co/production/uploads/63d7b8ee07cd1aa3c49a2026/-oU2IpNLcA0gTMJ34wDBR.png \
+    --layout_detection_model_name PicoDet_layout_1x \
+    --use_doc_orientation_classify False \
+    --use_doc_unwarping False \
+    --save_path ./output \
+    --device gpu:0 
+
+```
+
+You can also integrate the PP-TableMagic pipeline into your project. Before running the following code, please download the sample image to your local machine.
+
+```python
+from paddleocr import TableRecognitionPipelineV2
+
+pipeline = TableRecognitionPipelineV2(
+    layout_detection_model_name=PicoDet_layout_1x,
+    use_doc_orientation_classify=False,   # Use use_doc_orientation_classify to enable/disable document orientation classification model
+    use_doc_unwarping=False, # Use use_doc_unwarping to enable/disable document unwarping module
+    device="gpu:0", # Use device to specify GPU for model inference
+    )
+
+output = pipeline.predict("-oU2IpNLcA0gTMJ34wDBR.png")
+for res in output:
+    res.print() ## Print the predicted structured output
+    res.save_to_img("./output/")
+    res.save_to_xlsx("./output/")
+    res.save_to_html("./output/")
+    res.save_to_json("./output/")
+```
+
+The default model used in pipeline is `PP-DocLayout-L`, so it is needed that specifing to `PicoDet_layout_1x` by argument `layout_detection_model_name`. And you can also use the local model file by argument `layout_detection_model_dir`. For details about usage command and descriptions of parameters, please refer to the [Document](https://paddlepaddle.github.io/PaddleOCR/main/en/version3.x/pipeline_usage/table_recognition_v2.html#2-quick-start).
+
+## Links
+
+[PaddleOCR Repo](https://github.com/paddlepaddle/paddleocr)
+
+[PaddleOCR Documentation](https://paddlepaddle.github.io/PaddleOCR/latest/en/index.html)
+