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--- |
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language: en |
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library_name: transformers |
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tags: |
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- vision |
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- image-segmentation |
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- nvidia/mit-b5 |
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- transformers.js |
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- onnx |
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datasets: |
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- celebamaskhq |
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--- |
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# Face Parsing |
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[Semantic segmentation](https://huggingface.co/docs/transformers/tasks/semantic_segmentation) model fine-tuned from [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) with [CelebAMask-HQ](https://github.com/switchablenorms/CelebAMask-HQ) for face parsing. For additional options, see the Transformers [Segformer docs](https://huggingface.co/docs/transformers/model_doc/segformer). |
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> ONNX model for web inference contributed by [Xenova](https://huggingface.co/Xenova). |
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## Usage in Python |
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Exhaustive list of labels can be extracted from [config.json](https://huggingface.co/jonathandinu/face-parsing/blob/65972ac96180b397f86fda0980bbe68e6ee01b8f/config.json#L30). |
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| id | label | note | |
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| :-: | :--------- | :---------------- | |
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| 0 | background | | |
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| 1 | skin | | |
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| 2 | nose | | |
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| 3 | eye_g | eyeglasses | |
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| 4 | l_eye | left eye | |
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| 5 | r_eye | right eye | |
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| 6 | l_brow | left eyebrow | |
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| 7 | r_brow | right eyebrow | |
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| 8 | l_ear | left ear | |
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| 9 | r_ear | right ear | |
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| 10 | mouth | area between lips | |
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| 11 | u_lip | upper lip | |
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| 12 | l_lip | lower lip | |
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| 13 | hair | | |
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| 14 | hat | | |
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| 15 | ear_r | earring | |
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| 16 | neck_l | necklace | |
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| 17 | neck | | |
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| 18 | cloth | clothing | |
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```python |
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import torch |
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from torch import nn |
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from transformers import SegformerImageProcessor, SegformerForSemanticSegmentation |
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from PIL import Image |
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import matplotlib.pyplot as plt |
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import requests |
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# convenience expression for automatically determining device |
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device = ( |
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"cuda" |
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# Device for NVIDIA or AMD GPUs |
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if torch.cuda.is_available() |
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else "mps" |
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# Device for Apple Silicon (Metal Performance Shaders) |
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if torch.backends.mps.is_available() |
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else "cpu" |
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) |
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# load models |
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image_processor = SegformerImageProcessor.from_pretrained("jonathandinu/face-parsing") |
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model = SegformerForSemanticSegmentation.from_pretrained("jonathandinu/face-parsing") |
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model.to(device) |
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# expects a PIL.Image or torch.Tensor |
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url = "https://images.unsplash.com/photo-1539571696357-5a69c17a67c6" |
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image = Image.open(requests.get(url, stream=True).raw) |
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# run inference on image |
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inputs = image_processor(images=image, return_tensors="pt").to(device) |
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outputs = model(**inputs) |
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logits = outputs.logits # shape (batch_size, num_labels, ~height/4, ~width/4) |
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# resize output to match input image dimensions |
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upsampled_logits = nn.functional.interpolate(logits, |
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size=image.size[::-1], # H x W |
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mode='bilinear', |
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align_corners=False) |
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# get label masks |
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labels = upsampled_logits.argmax(dim=1)[0] |
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# move to CPU to visualize in matplotlib |
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labels_viz = labels.cpu().numpy() |
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plt.imshow(labels_viz) |
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plt.show() |
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``` |
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## Usage in the browser (Transformers.js) |
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```js |
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import { |
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pipeline, |
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env, |
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} from "https://cdn.jsdelivr.net/npm/@xenova/[email protected]"; |
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// important to prevent errors since the model files are likely remote on HF hub |
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env.allowLocalModels = false; |
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// instantiate image segmentation pipeline with pretrained face parsing model |
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model = await pipeline("image-segmentation", "jonathandinu/face-parsing"); |
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// async inference since it could take a few seconds |
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const output = await model(url); |
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// each label is a separate mask object |
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// [ |
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// { score: null, label: 'background', mask: transformers.js RawImage { ... }} |
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// { score: null, label: 'hair', mask: transformers.js RawImage { ... }} |
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// ... |
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// ] |
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for (const m of output) { |
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print(`Found ${m.label}`); |
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m.mask.save(`${m.label}.png`); |
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} |
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``` |
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### p5.js |
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Since [p5.js](https://p5js.org/) uses an animation loop abstraction, we need to take care loading the model and making predictions. |
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```js |
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// ... |
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// asynchronously load transformers.js and instantiate model |
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async function preload() { |
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// load transformers.js library with a dynamic import |
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const { pipeline, env } = await import( |
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"https://cdn.jsdelivr.net/npm/@xenova/[email protected]" |
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); |
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// important to prevent errors since the model files are remote on HF hub |
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env.allowLocalModels = false; |
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// instantiate image segmentation pipeline with pretrained face parsing model |
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model = await pipeline("image-segmentation", "jonathandinu/face-parsing"); |
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print("face-parsing model loaded"); |
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} |
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// ... |
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``` |
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[full p5.js example](https://editor.p5js.org/jonathan.ai/sketches/wZn15Dvgh) |
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### Model Description |
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- **Developed by:** [Jonathan Dinu](https://twitter.com/jonathandinu) |
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- **Model type:** Transformer-based semantic segmentation image model |
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- **License:** non-commercial research and educational purposes |
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- **Resources for more information:** Transformers docs on [Segformer](https://huggingface.co/docs/transformers/model_doc/segformer) and/or the [original research paper](https://arxiv.org/abs/2105.15203). |
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## Limitations and Bias |
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### Bias |
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While the capabilities of computer vision models are impressive, they can also reinforce or exacerbate social biases. The [CelebAMask-HQ](https://github.com/switchablenorms/CelebAMask-HQ) dataset used for fine-tuning is large but not necessarily perfectly diverse or representative. Also, they are images of.... just celebrities. |
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