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This is the **large** variant of the EoMT-DINOv3 model trained for **panoptic segmentation** on COCO at **640×640** resolution. **EoMT (Encoder-only Mask Transformer)** is a Vision Transformer (ViT) architecture designed for high-quality and efficient image segmentation. It was introduced in the CVPR 2025 highlight paper: **Your ViT is Secretly an Image Segmentation Model** > **Key Insight**: Given sufficient scale and pretraining, a plain ViT along with a few additional parameters can perform segmentation without the need for task-specific decoders or pixel fusion modules. The same model backbone supports semantic, instance, and panoptic segmentation with different post-processing.

Image segmentation
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This repository contains the Hugging Face Transformers conversion of the official VidEoMT checkpoint `yt_2019_vit_small_52.8.pth` from tue-mps/VidEoMT. Use `processor.post_process_instance_segmentation`, `processor.post_process_panoptic_segmentation`, or `processor.post_process_semantic_segmentation` depending on the target task.

Image segmentation
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flexray

VictorButoi

FleXray is a single 2D UNet that segments anatomy from standard radiographs across body regions, projections, and acquisition settings. It predicts 60 anatomical structures (plus background) as independent sigmoid channels at 256 x 256 resolution. This repository holds the **flagship model** and the four sibling models of the FleXray ensemble, one bundle per `members/` subfolder (see Repository layout). `flexify` and `FleXraySegmenter.from_pretrained` load the flagship by default. `flexify` writes ` _masks.npy`, ` _probabilities.npy`, and ` _logits.npy` per image, each shaped `CxHxW`. The Python API keeps the batch dimension (`B=1` for a single image). Channel order follows `label_schema.json`. Pass `--binary LABEL` (for example `--binary femurs`) to write one label. See docs/inference.md for the full CLI and Python API.

Image segmentation
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https://huggingface.co/facebook/detr-resnet-50-panoptic with ONNX weights to be compatible with Transformers.js. Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using 🤗 Optimum and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).

Image segmentation
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https://huggingface.co/jonathandinu/face-parsing with ONNX weights to be compatible with Transformers.js. Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using 🤗 Optimum and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).

Image segmentation
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modnet

Xenova

For more information, check out the official repository and example colab. If you haven't already, you can install the Transformers.js JavaScript library from NPM using: Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using 🤗 Optimum and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).

Image segmentation
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https://huggingface.co/mattmdjaga/segformer_b0_clothes with ONNX weights to be compatible with Transformers.js. Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using 🤗 Optimum and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).

Image segmentation
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https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512 with ONNX weights to be compatible with Transformers.js. If you haven't already, you can install the Transformers.js JavaScript library from NPM using: **Example:** Image segmentation with `Xenova/segformer-b0-finetuned-ade-512-512`.

Image segmentation
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https://huggingface.co/mattmdjaga/segformer_b2_clothes with ONNX weights to be compatible with Transformers.js. Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using 🤗 Optimum and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).

Image segmentation
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https://huggingface.co/nvidia/segformer-b2-finetuned-ade-512-512 with ONNX weights to be compatible with Transformers.js. Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using 🤗 Optimum and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).

Image segmentation
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ink-8um-v8in

YoussefMoNader

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

Basic profile indexed · More details to follow

Image segmentation
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