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README.md
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- danbooru
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- deep-learning
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- computer-vision
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# ML-Danbooru ONNX Models
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print(tags_custom)
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```
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### Advanced Usage with Model Selection
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```python
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from huggingface_hub import hf_hub_download
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from imgutils.utils import open_onnx_model
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from PIL import Image
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import numpy as np
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# Load a specific model from this repository
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model_path = hf_hub_download('deepghs/ml-danbooru-onnx', 'ml_caformer_m36_dec-5-97527.onnx')
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model = open_onnx_model(model_path)
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# Manual preprocessing and inference
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def preprocess_image(image_path, size=448):
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image = Image.open(image_path).convert('RGB')
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# Resize with alignment for optimal performance
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min_edge = min(image.size)
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target_size = (
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int(image.size[0] / min_edge * size),
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int(image.size[1] / min_edge * size),
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)
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target_size = (
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(target_size[0] // 4) * 4,
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(target_size[1] // 4) * 4,
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)
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image = image.resize(target_size, resample=Image.BILINEAR)
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# Convert to tensor
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img_array = np.array(image, dtype=np.float32).transpose(2, 0, 1) / 255.0
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return img_array.reshape(1, *img_array.shape)
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# Run inference
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input_tensor = preprocess_image('your_image.jpg')
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output = model.run(['output'], {'input': input_tensor})[0]
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probabilities = 1 / (1 + np.exp(-output)).reshape(-1)
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# Process results (you would need to load the tag labels)
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# tags = process_probabilities(probabilities, threshold=0.7)
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```
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## Model Variants
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This repository contains multiple ML-Danbooru model variants:
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- **Architecture**: Caformer-M36 and TResnet variants
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- **Format**: ONNX for optimized inference
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## Original Content
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### Tag Database Structure
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The repository includes a comprehensive tag database with the following structure:
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```json
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// Sample from classes.json (simplified tags)
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[
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"1girl",
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"bangs",
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"blunt_bangs",
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"brown_hair",
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"hair_bun",
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"hime_cut",
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"long_hair",
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"mask",
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"ribbon",
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"solo",
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"yellow_eyes",
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// ... 1,527 tags total
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]
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```
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```csv
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# Sample from tags.csv
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tag,root,pos,count
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1girl,girl,NOUN,4317542
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bangs,bang,NOUN,1576060
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blunt_bangs,bang,NOUN,178797
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brown_hair,hair,NOUN,1092727
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hair_bun,bun,NOUN,157335
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```
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### Model Architecture Details
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The ML-Danbooru models utilize modern transformer-based architectures:
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- danbooru
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- deep-learning
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- computer-vision
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---
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# ML-Danbooru ONNX Models
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print(tags_custom)
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```
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## Model Variants
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This repository contains multiple ML-Danbooru model variants:
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- **Architecture**: Caformer-M36 and TResnet variants
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- **Format**: ONNX for optimized inference
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### Model Architecture Details
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The ML-Danbooru models utilize modern transformer-based architectures:
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