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| F1 on DIS-VD | 0.6461 |
| MAE | 0.1178 |
| Resolution | 512×512 |
| Parameters | 4.63M |
| Training | 1× T4, 40 epohs |
1import numpy as np, onnxruntime as ort
2from PIL import Image
3from huggingface_hub import hf_hub_download
4
5sess = ort.InferenceSession(hf_hub_download("Catniti/catrex-lite-image-segmentation", "model.onnx"))
6
7img = Image.open("photo.jpg").convert("RGB")
8x = np.array(img.resize((512, 512))).astype(np.float32) / 255.
9x = (x - [0.485, 0.456, 0.406]) / [0.229, 0.224, 0.225]
10x = x.transpose(2, 0, 1)[None].astype(np.float32)
11
12mask = sess.run(None, {"input": x})[0][0, 0]
13mask = Image.fromarray((mask * 255).astype(np.uint8)).resize(img.size)
14
15cutout = Image.fromarray(np.dstack([np.array(img), np.array(mask)]), "RGBA")
16cutout.save("no_background.png")1<script src="https://cdn.jsdelivr.net/npm/onnxruntime-web/dist/ort.min.js"></script>
2<script type="module">
3const url = "https://huggingface.co/Catniti/catrex-lite-image-segmentation/resolve/main/model.onnx";
4const session = await ort.InferenceSession.create(url);
5const { mask } = await session.run({ input: tensor });
6</script>npm i onnxruntime-nodehf download Catniti/catrex-lite-image-segmentation model.onnx --local-dir .