Add ONNX model (fp32).

README.md

@@ -1,3 +1,56 @@
 ---
+library_name: transformers
+pipeline_tag: image-segmentation
+tags:
+  - isnet
+  - dis
+  - anime
+  - image-segmentation
+  - mask-generation
+  - transformers.js
 license: apache-2.0
+language:
+  - en
 ---
+# IS-Net-Anime
+
+## Model Description
+IS-Net-Anime is a deep learning model designed to provide interactive image segmentation capabilities. The model allows users to refine segmentation masks through user interactions, making it highly effective for tasks that require precise and detailed segmentation results.
+
+## Usage
+Perform mask generation with `BritishWerewolf/IS-Net-Anime`.
+
+### 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/IS-Net-Anime');
+const processed = await processor(image);
+
+const model = await AutoModel.from_pretrained('BritishWerewolf/IS-Net-Anime', {
+    dtype: 'fp32',
+});
+
+const output = await model({ input: processed.pixel_values });
+// {
+//   mask: Tensor {
+//     dims: [ 1, 1024, 1024 ],
+//     type: 'uint8',
+//     data: Uint8Array(1048576) [ ... ],
+//     size: 1048576
+//   }
+// }
+```
+
+### 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 IS-Net-Anime model can be credited at https://github.com/SkyTNT/anime-segmentation.
+
+## Licence
+This model is licensed under the Apache License 2.0 to match the original IS-Net-Anime model.

config.json

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+{
+  "_name_or_path": "BritishWerewolf/IS-Net-Anime",
+  "model_type": "u2net",
+  "architectures": [
+    "U2NetModel"
+  ],
+  "input_name": "img",
+  "input_shape": [1, 3, 1024, 1024],
+  "output_composite": "mask",
+  "output_names": [
+    "mask"
+  ],
+  "output_shape": [1, 1024, 1024]
+}

onnx/model.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f15622d853e8260172812b657053460e20806f04b9e05147d49af7bed31a6e99
+size 176069933

preprocessor_config.json

@@ -0,0 +1,27 @@
+{
+  "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": [
+    1.0,
+    1.0,
+    1.0
+  ],
+  "pad_size": {
+    "width": 1024,
+    "height": 1024
+  },
+  "size": {
+    "longest_edge": 1024
+  }
+}