Instructions to use XYZ9843/GOOSE-M2F with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use XYZ9843/GOOSE-M2F with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="XYZ9843/GOOSE-M2F")# Load model directly from transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation processor = AutoImageProcessor.from_pretrained("XYZ9843/GOOSE-M2F") model = Mask2FormerForUniversalSegmentation.from_pretrained("XYZ9843/GOOSE-M2F") - Notebooks
- Google Colab
- Kaggle
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by nielsr HF Staff - opened
README.md
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## GOOSE-M2F: Adapting Mask2Former for High-Fidelity, Long-Tailed Fine-Grained Semantic Segmentation in Unstructured Outdoor Terrain
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**Jyothiraditya Lingam, Nikhileswara Rao Sulake, Sai Manikanta Eswar Machara**
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If you use this work, please cite:
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```bibtex
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@techreport{
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title = {GOOSE-M2F: Adapting Mask2Former for High-Fidelity, Long-Tailed Fine-Grained Semantic Segmentation in Unstructured Outdoor Terrain},
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author = {
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year = {2026},
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institution = {
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}
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```
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---
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pipeline_tag: image-segmentation
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library_name: transformers
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---
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## GOOSE-M2F: Adapting Mask2Former for High-Fidelity, Long-Tailed Fine-Grained Semantic Segmentation in Unstructured Outdoor Terrain
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**Jyothiraditya Lingam, Nikhileswara Rao Sulake, Sai Manikanta Eswar Machara**
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If you use this work, please cite:
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```bibtex
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@techreport{lingam2026goosem2f,
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title = {GOOSE-M2F: Adapting Mask2Former for High-Fidelity, Long-Tailed Fine-Grained Semantic Segmentation in Unstructured Outdoor Terrain},
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author = {Jyothiraditya Lingam and Nikhileswara Rao Sulake and Sai Manikanta Eswar Machara},
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year = {2026},
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institution = {Rajiv Gandhi University of Knowledge Technologies (RGUKT)}
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}
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```
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