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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Login to HuggingFace (just login once)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from huggingface_hub import interpreter_login\n",
"interpreter_login()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Collect Menu Image Datasets\n",
"- Use `metadata.jsonl` to label the images's ground truth. You can visit [here](https://github.com/ryanlinjui/menu-text-detection/tree/main/examples) to see the examples.\n",
"- After finishing, push to HuggingFace Datasets.\n",
"- For labeling:\n",
" - [Google AI Studio](https://aistudio.google.com) or [OpenAI ChatGPT](https://chatgpt.com).\n",
" - Use function calling by API. Start the gradio app locally or visit [here](https://huggingface.co/spaces/ryanlinjui/menu-text-detection).\n",
"\n",
"### Menu Type\n",
"- **h**: horizontal menu\n",
"- **v**: vertical menu\n",
"- **d**: document-style menu\n",
"- **s**: in-scene menu (non-document style)\n",
"- **i**: irregular menu (menu with irregular text layout)\n",
"\n",
"> Please see the [examples](https://github.com/ryanlinjui/menu-text-detection/tree/main/examples) for more details."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import json\n",
"\n",
"import numpy as np\n",
"from PIL import Image\n",
"from pillow_heif import register_heif_opener\n",
"\n",
"from menu.llm import (\n",
" GeminiAPI,\n",
" OpenAIAPI\n",
")\n",
"\n",
"IMAGE_DIR = \"datasets/images\" # set your image directory here\n",
"SELECTED_MODEL = \"gemini-2.5-flash\" # set model name here, refer MODEL_LIST from app.py for more\n",
"API_TOKEN = \"\" # set your API token here\n",
"SELECTED_FUNCTION = GeminiAPI # set \"GeminiAPI\" or \"OpenAIAPI\"\n",
"\n",
"register_heif_opener()\n",
"\n",
"for file in os.listdir(IMAGE_DIR):\n",
" print(f\"Processing image: {file}\")\n",
" try:\n",
" image = np.array(Image.open(os.path.join(IMAGE_DIR, file)))\n",
" data = {\n",
" \"file_name\": file,\n",
" \"menu\": SELECTED_FUNCTION.call(image, SELECTED_MODEL, API_TOKEN)\n",
" }\n",
" with open(os.path.join(IMAGE_DIR, \"metadata.jsonl\"), \"a\", encoding=\"utf-8\") as metaf:\n",
" metaf.write(json.dumps(data, ensure_ascii=False, sort_keys=True) + \"\\n\")\n",
" except Exception as e:\n",
" print(f\"Skipping invalid image '{file}': {e}\")\n",
" continue"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Push Datasets to HuggingFace"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from datasets import load_dataset\n",
"\n",
"dataset = load_dataset(path=\"datasets/menu-zh-TW\") # load dataset from the local directory including the metadata.jsonl, images files.\n",
"dataset.push_to_hub(repo_id=\"ryanlinjui/menu-zh-TW\") # push to the huggingface dataset hub"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Prepare the dataset for training"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from menu.utils import split_dataset\n",
"from datasets import load_dataset\n",
"\n",
"dataset = load_dataset(path=\"ryanlinjui/menu-zh-TW\") # set your dataset repo id for training\n",
"dataset = split_dataset(dataset[\"train\"], train=0.8, validation=0.1, test=0.1, seed=42) # (optional) use it if your dataset is not split into train/validation/test\n",
"print(f\"Dataset split: {len(dataset['train'])} train, {len(dataset['validation'])} validation, {len(dataset['test'])} test\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Fine-tune Donut Model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import logging\n",
"from menu.donut import DonutTrainer\n",
"\n",
"logging.getLogger(\"transformers\").setLevel(logging.ERROR) # filter output message from transformers\n",
"\n",
"DonutTrainer.train(\n",
" dataset=dataset,\n",
" pretrained_model_repo_id=\"naver-clova-ix/donut-base\", # set your pretrained model repo id for fine-tuning\n",
" ground_truth_key=\"menu\", # set your ground truth key for training\n",
" huggingface_model_id=\"ryanlinjui/donut-base-finetuned-menu\", # set your huggingface model repo id for saving / pushing to the hub\n",
" epochs=15, # set your training epochs\n",
" train_batch_size=8, # set your training batch size\n",
" val_batch_size=1, # set your validation batch size\n",
" learning_rate=3e-5, # set your learning rate\n",
" val_check_interval=0.5, # how many times we want to validate during an epoch\n",
" check_val_every_n_epoch=1, # how many epochs we want to validate\n",
" gradient_clip_val=1.0, # gradient clipping value for training stability\n",
" num_training_samples_per_epoch=198, # set num_training_samples_per_epoch = training set size\n",
" num_nodes=1, # number of nodes for distributed training\n",
" warmup_steps=75 # number of warmup steps for learning rate scheduler, 198/8*30/10, 10%\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Evaluate Donut Model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"from datasets import load_dataset\n",
"\n",
"from menu.utils import split_dataset\n",
"from menu.donut import DonutFinetuned\n",
"\n",
"dataset = load_dataset(\"ryanlinjui/menu-zh-TW\")\n",
"dataset = split_dataset(dataset[\"train\"], train=0.8, validation=0.1, test=0.1, seed=42) # (optional) use it if your dataset is not split into train/validation/test\n",
"donut_finetuned = DonutFinetuned(pretrained_model_repo_id=\"ryanlinjui/donut-base-finetuned-menu\")\n",
"scores, output_list = donut_finetuned.evaluate(dataset=dataset[\"test\"], ground_truth_key=\"menu\")\n",
"\n",
"print(\"Evaluation scores:\")\n",
"for key, value in scores.items():\n",
" print(f\"{key}: {value}\")\n",
"\n",
"print(\"\\nSample outputs:\")\n",
"for output in output_list[:5]:\n",
" print(json.dumps(output, ensure_ascii=False, indent=4))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Test Donut Model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from PIL import Image\n",
"from menu.donut import DonutFinetuned\n",
"\n",
"image = Image.open(\"./examples/menu-hd.jpg\")\n",
"\n",
"donut_finetuned = DonutFinetuned(pretrained_model_repo_id=\"ryanlinjui/donut-base-finetuned-menu\")\n",
"outputs = donut_finetuned.predict(image=image)\n",
"print(outputs)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "menu-text-detection",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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"nbformat_minor": 2
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