Duplicate from BritishWerewolf/U-2-Net-Human-Seg

Co-authored-by: Harry <BritishWerewolf@users.noreply.huggingface.co>

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README.md

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+---
+library_name: transformers
+pipeline_tag: image-segmentation
+tags:
+  - image-segmentation
+  - mask-generation
+  - transformers.js
+  - vision
+  - background-removal
+  - portrait-matting
+license: apache-2.0
+language:
+  - en
+---
+# U-2-Net-Human-Seg
+
+## Model Description
+U-2-Net-Human-Seg is a specialised version of the U-2-Net model designed specifically for human segmentation tasks. This model excels in distinguishing human figures from the background in images, making it particularly useful for applications such as background removal, virtual try-ons, and human-centric image editing. By leveraging a deep learning approach, U-2-Net-Human-Seg can accurately segment human subjects in various poses and environments, providing high-quality segmentation masks that can be utilized in different imaging tasks.
+
+## Usage
+Perform mask generation with `BritishWerewolf/U-2-Net-Human-Seg`.
+
+### Example
+```javascript
+import { AutoModel, AutoProcessor, RawImage } from '@huggingface/transformers';
+
+const img_url = 'https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png';
+const image = await RawImage.read(img_url);
+
+const processor = await AutoProcessor.from_pretrained('BritishWerewolf/U-2-Net-Human-Seg');
+const processed = await processor(image);
+
+const model = await AutoModel.from_pretrained('BritishWerewolf/U-2-Net-Human-Seg', {
+    dtype: 'fp32',
+});
+
+const output = await model({ input: processed.pixel_values });
+// {
+//   mask: Tensor {
+//     dims: [ 1, 320, 320 ],
+//     type: 'uint8',
+//     data: Uint8Array(102400) [ ... ],
+//     size: 102400
+//   }
+// }
+```
+
+## Model Architecture
+The U-2-Net-Human-Seg model is based on a simplified version of the original U-2-Net architecture, designed to be more lightweight while still achieving high performance in segmentation tasks. The model consists of several stages with down-sampling and up-sampling paths, using Residual U-blocks (RSU) for enhanced feature representation.
+
+### Inference
+To use the model for inference, you can follow the example provided above. The `AutoProcessor` and `AutoModel` classes from the `transformers` library make it easy to load the model and processor.
+
+## Credits
+* [`rembg`](https://github.com/danielgatis/rembg) for the ONNX model.
+* The authors of the original U-2-Net model can be credited at https://github.com/xuebinqin/U-2-Net.
+
+## Licence
+This model is licensed under the Apache License 2.0 to match the original U-2-Net model.

config.json

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+{
+  "_name_or_path": "u2net_human_seg",
+  "model_type": "u2net",
+  "architectures": [
+    "U2NetModel"
+  ],
+  "transformers.js_config": {
+    "dtype": "fp32"
+  },
+  "input_name": "input.1",
+  "input_shape": [1, 3, 320, 320],
+  "output_composite": "1959",
+  "output_names": [
+    "1959",
+    "1960",
+    "1961",
+    "1962",
+    "1963",
+    "1964",
+    "1965"
+  ],
+  "output_shape": [1, 320, 320]
+}

onnx/model.onnx

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+oid sha256:309c8469258dda742793dce0ebea8e6dd393174f89934733ecc8b14c76f4ddd8
+size 4574861

preprocessor_config.json

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+{
+  "processor_class": "U2NetProcessor",
+  "image_processor_type": "U2NetImageProcessor",
+  "do_convert_rgb": true,
+  "do_normalize": true,
+  "do_pad": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "keep_aspect_ratio": true,
+  "image_mean": [
+    0.485,
+    0.456,
+    0.406
+  ],
+  "image_std": [
+    0.229,
+    0.224,
+    0.225
+  ],
+  "pad_size": {
+    "width": 320,
+    "height": 320
+  },
+  "size": {
+    "longest_edge": 320
+  }
+}