| --- |
| library_name: onnx |
| tags: |
| - computer-vision |
| - image-enhancement |
| - photo-retouching |
| - computational-photography |
| - color-grading |
| - onnx |
| - mobile |
| - lightweight |
| - multi-task-learning |
| datasets: |
| - Phitran21/adaptive-photo-retouching-6style |
| --- |
| |
| # AdaptivePhotoNet |
|
|
| **AdaptivePhotoNet** is a lightweight, scene-aware neural network for |
| automatic photo retouching. |
|
|
| Instead of generating a new image pixel-by-pixel, the model analyzes a |
| low-resolution preview of the photograph and predicts a compact |
| **21-dimensional retouching recipe** that can be applied to the original |
| full-resolution image by a deterministic image-processing pipeline. |
|
|
| The model also predicts **10 scene attributes** to provide auxiliary |
| scene understanding. |
|
|
| AdaptivePhotoNet contains **5,876,943 parameters** and supports six |
| retouching styles: |
|
|
| - Natural |
| - Vivid |
| - Cinema |
| - Portrait |
| - Film |
| - Moody |
|
|
| The model is exported to **ONNX** and designed with lightweight desktop, |
| mobile, and edge inference in mind. |
|
|
| --- |
|
|
| ## Model Concept |
|
|
| AdaptivePhotoNet separates **visual understanding** from |
| **full-resolution image processing**. |
|
|
| ```text |
| ┌─────────────────┐ |
| │ Original Image │ |
| └────────┬────────┘ |
| │ |
| resize / preview |
| │ |
| ▼ |
| ┌──────────────────┐ |
| │ RGB 224 × 224 │ |
| └────────┬─────────┘ |
| │ |
| ┌───────────────┴───────────────┐ |
| │ │ |
| Image Features Style ID |
| │ 0 ... 5 |
| └───────────────┬───────────────┘ |
| ▼ |
| ┌──────────────────┐ |
| │ AdaptivePhotoNet │ |
| │ 5.88M params │ |
| └────────┬─────────┘ |
| │ |
| ┌──────────┴──────────┐ |
| ▼ ▼ |
| 21D Retouch Recipe 10 Scene Scores |
| │ |
| ▼ |
| Deterministic Retouching |
| Pipeline |
| │ |
| ▼ |
| Full-Resolution Output Image |
| |
| The neural network therefore does not need to reconstruct the full-resolution photograph. |
| |
| It predicts how the photograph should be adjusted, while the final rendering is performed by conventional image-processing operations. |
| |
| This design has several practical advantages: |
| |
| low neural-network inference cost; |
| |
| processing is independent of the original image resolution at the model stage; |
| |
| deterministic full-resolution rendering; |
| |
| compact ONNX deployment; |
| |
| interpretable adjustment parameters; |
| |
| selectable photographic styles; |
| |
| suitable for mobile and edge applications. |
| |
| |
| |
| --- |
| |
| Model Specifications |
| |
| Property Value |
| |
| Model AdaptivePhotoNet |
| Parameters 5,876,943 |
| Model input resolution 224 × 224 |
| Image format RGB |
| Tensor layout NCHW |
| Image dtype float32 |
| Image range [0.0, 1.0] |
| Style input int64 |
| Number of styles 6 |
| Recipe output 21 dimensions |
| Scene output 10 dimensions |
| Runtime format ONNX |
| |
| |
| |
| --- |
| |
| Inputs |
| |
| Image |
| |
| name: image |
| shape: [1, 3, 224, 224] |
| dtype: float32 |
| layout: NCHW |
| color: RGB |
| range: 0.0 - 1.0 |
| |
| The original photograph should be converted to RGB, resized to 224 × 224, converted to float32, normalized to [0, 1], and arranged in NCHW format. |
| |
| The 224 × 224 image is used for analysis only. |
| |
| The final retouching operations can be applied separately to the original full-resolution photograph. |
| |
| Style |
| |
| name: style_id |
| shape: [1] |
| dtype: int64 |
| |
| ID Style |
| |
| 0 Natural |
| 1 Vivid |
| 2 Cinema |
| 3 Portrait |
| 4 Film |
| 5 Moody |
| |
| |
| Changing style_id instructs the same model to predict a different retouching direction for the input photograph. |
| |
| |
| --- |
| |
| Outputs |
| |
| AdaptivePhotoNet produces two outputs. |
| |
| 1. Retouching Recipe |
| |
| shape: [1, 21] |
| |
| The 21-dimensional vector describes the photographic adjustments that should be applied by the retouching engine. |
| |
| Linear Parameters |
| |
| Dimensions 0–16 represent: |
| |
| Dim Parameter Range |
| |
| 0 Exposure EV -2.0 → 2.0 |
| 1 Temperature -1.0 → 1.0 |
| 2 Tint -1.0 → 1.0 |
| 3 Shadows -1.0 → 1.0 |
| 4 Highlights -1.0 → 1.0 |
| 5 Contrast -1.0 → 1.0 |
| 6 Tone Curve 0 0.0 → 1.0 |
| 7 Tone Curve 1 0.0 → 1.0 |
| 8 Tone Curve 2 0.0 → 1.0 |
| 9 Tone Curve 3 0.0 → 1.0 |
| 10 Tone Curve 4 0.0 → 1.0 |
| 11 Shadow Tone Strength 0.0 → 0.3 |
| 12 Highlight Tone Strength 0.0 → 0.3 |
| 13 Saturation -1.0 → 1.0 |
| 14 Vibrance -1.0 → 1.0 |
| 15 Fade 0.0 → 1.0 |
| 16 Vignette 0.0 → 1.0 |
| |
| |
| Hue Parameters |
| |
| Hue is represented circularly using sine/cosine pairs rather than a single scalar value. |
| |
| 17, 18 → shadow_tone_hue [sin, cos] |
| 19, 20 → highlight_tone_hue [sin, cos] |
| |
| This avoids the discontinuity that occurs when representing circular hue values directly near the angle boundary. |
| |
| The complete interpretation is defined in: |
| |
| recipe_schema.json |
| |
| |
| --- |
| |
| 2. Scene Probabilities |
| |
| shape: [1, 10] |
| |
| The auxiliary scene head predicts ten visual attributes: |
| |
| Index Scene Attribute |
| |
| 0 Human |
| 1 Face Visible |
| 2 Skin Visible |
| 3 Portrait |
| 4 Indoor |
| 5 Outdoor |
| 6 Night |
| 7 Low Light |
| 8 Backlit |
| 9 High Dynamic Range |
| |
| |
| These attributes provide additional scene understanding alongside the retouching prediction. |
| |
| They can also be useful for debugging, analysis, UI features, or future scene-aware processing logic. |
| |
| |
| --- |
| |
| Why Predict a Recipe Instead of Pixels? |
| |
| Many neural photo-enhancement systems directly generate a complete output image. |
| |
| AdaptivePhotoNet takes a different approach. |
| |
| Pixel-to-pixel model: |
| |
| Full Image → Neural Network → Full Image |
| |
| |
| AdaptivePhotoNet: |
| |
| Small Preview → Neural Network → 21 Parameters |
| ↓ |
| Original Full-Resolution Image → Retouching Engine → Output |
| |
| For photographic retouching, much of the desired transformation can be expressed through global or structured photographic controls. |
| |
| Predicting these controls instead of millions of output pixels allows the neural network to remain relatively small. |
| |
| It also keeps the transformation interpretable. |
| |
| For example, an application can inspect whether the network requested: |
| |
| Exposure +0.32 EV |
| Temperature -0.08 |
| Highlights -0.21 |
| Contrast +0.14 |
| Saturation +0.07 |
| Vignette 0.11 |
| ... |
| |
| rather than receiving only an opaque generated image. |
| |
| |
| --- |
| |
| Multi-Style Retouching |
| |
| AdaptivePhotoNet uses a separate style_id input rather than requiring six independent models. |
| |
| The same photograph can therefore be analyzed under different retouching directions: |
| |
| ┌─ Natural |
| ├─ Vivid |
| Input Photograph ───├─ Cinema |
| ├─ Portrait |
| ├─ Film |
| └─ Moody |
| |
| The style determines the intended aesthetic direction while the image content determines the actual adjustment recipe. |
| |
| This means that Film, for example, is not intended to represent one fixed preset applied identically to every photograph. |
| |
| Two photographs using the same style may receive different exposure, tone, color, curve, and other adjustments according to their visual characteristics. |
| |
| |
| --- |
| |
| Training Dataset |
| |
| AdaptivePhotoNet was developed together with: |
| |
| Adaptive Photo Retouching 6-Style Dataset |
| |
| https://huggingface.co/datasets/Phitran21/adaptive-photo-retouching-6style |
| |
| The dataset contains original photographs paired with six adaptively retouched variants: |
| |
| Original |
| ├── Natural |
| ├── Vivid |
| ├── Cinema |
| ├── Portrait |
| ├── Film |
| └── Moody |
| |
| The target transformations were generated adaptively for individual images rather than by applying six globally fixed presets. |
| |
| See the dataset card for details about dataset generation, source data, licensing, and limitations. |
| |
| |
| --- |
| |
| Inference Pipeline |
| |
| A typical application pipeline is: |
| |
| 1. Load the original image |
| ↓ |
| 2. Create 224 × 224 RGB preview |
| ↓ |
| 3. Normalize to float32 [0, 1] |
| ↓ |
| 4. Convert HWC → NCHW |
| ↓ |
| 5. Select style_id |
| ↓ |
| 6. Run AdaptivePhotoNet |
| ↓ |
| 7. Decode the 21D recipe |
| ↓ |
| 8. Apply recipe to original-resolution image |
| ↓ |
| 9. Produce final retouched photograph |
| |
| The original full-resolution image does not need to pass through the neural network. |
| |
| |
| --- |
| |
| Minimal ONNX Runtime Example |
| |
| import numpy as np |
| import onnxruntime as ort |
| from PIL import Image |
| |
| STYLE = { |
| "natural": 0, |
| "vivid": 1, |
| "cinema": 2, |
| "portrait": 3, |
| "film": 4, |
| "moody": 5, |
| } |
| |
| image = Image.open("photo.jpg").convert("RGB") |
| preview = image.resize((224, 224)) |
| |
| x = np.asarray(preview, dtype=np.float32) / 255.0 |
| x = np.transpose(x, (2, 0, 1)) |
| x = np.expand_dims(x, axis=0) |
| |
| style_id = np.asarray([STYLE["film"]], dtype=np.int64) |
| |
| session = ort.InferenceSession("AdaptivePhotoNet.onnx") |
| |
| recipe_vector, scene_probs = session.run( |
| None, |
| { |
| "image": x, |
| "style_id": style_id, |
| }, |
| ) |
| |
| print("Recipe:", recipe_vector) |
| print("Scene probabilities:", scene_probs) |
| |
| The resulting recipe_vector must then be interpreted according to recipe_schema.json and applied by the corresponding image-retouching pipeline. |
| |
| |
| --- |
| |
| Android / ONNX Runtime |
| |
| Recommended execution-provider configuration: |
| |
| FP32 |
| |
| XNNPACKExecutionProvider |
| ↓ fallback |
| CPUExecutionProvider |
| |
| INT8 |
| |
| CPUExecutionProvider |
| |
| Actual performance depends on device hardware, ONNX Runtime version, thread configuration, quantization method, and preprocessing pipeline. |
| |
| |
| --- |
| |
| Intended Use |
| |
| AdaptivePhotoNet is intended for experimentation and development in: |
| |
| automatic photo retouching; |
| |
| computational photography; |
| |
| adaptive color grading; |
| |
| scene-aware image enhancement; |
| |
| mobile photo editing; |
| |
| lightweight computer vision; |
| |
| ONNX Runtime applications; |
| |
| edge inference; |
| |
| non-destructive image adjustment prediction. |
| |
| |
| |
| --- |
| |
| Limitations |
| |
| AdaptivePhotoNet predicts photographic adjustments from a 224 × 224 representation of the image. |
| |
| Fine details that disappear during resizing may therefore not influence the predicted recipe. |
| |
| The model may also perform less reliably on images significantly outside its training distribution, including unusual lighting, extreme exposure, uncommon photographic styles, or heavily degraded images. |
| |
| Retouching quality is inherently subjective. Different users may prefer different photographic interpretations of the same image. |
| |
| The six supported styles represent only six predefined aesthetic directions and should not be interpreted as exhaustive photographic styles. |
| |
| The model predicts retouching parameters rather than reconstructing or generating image content. It therefore cannot perform tasks such as object removal, image inpainting, semantic image editing, or generative relighting. |
| |
| |
| --- |
| |
| Related Resources |
| |
| Training Dataset |
| |
| Adaptive Photo Retouching 6-Style Dataset |
| |
| https://huggingface.co/datasets/Phitran21/adaptive-photo-retouching-6style |
| |
| Source Code and Demo |
| |
| https://github.com/phiiggfdg/adaptive-retouch-6m-onnx |
| |
| |
| --- |
| |
| Author |
| |
| Trần Phi |
| |
| Hugging Face: |
| https://huggingface.co/Phitran21 |
| |
| GitHub: |
| https://github.com/phiiggfdg |
| |
| Website: |
| https://toren.io.vn |
| |
| |
| --- |
