Instructions to use tue-mps/coco_instance_eomt_large_1280 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tue-mps/coco_instance_eomt_large_1280 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="tue-mps/coco_instance_eomt_large_1280")# Load model directly from transformers import AutoImageProcessor, EomtForUniversalSegmentation processor = AutoImageProcessor.from_pretrained("tue-mps/coco_instance_eomt_large_1280") model = EomtForUniversalSegmentation.from_pretrained("tue-mps/coco_instance_eomt_large_1280", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -16,7 +16,9 @@ by Tommie Kerssies, Niccolò Cavagnero, Alexander Hermans, Narges Norouzi, Giuse
|
|
| 16 |
|
| 17 |
> **Key Insight**: Given sufficient scale and pretraining, a plain ViT along with additional few params 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 🤗
|
| 18 |
|
| 19 |
-
The original implementation can be found in this [repository](https://github.com/tue-mps/eomt)
|
|
|
|
|
|
|
| 20 |
|
| 21 |
---
|
| 22 |
|
|
|
|
| 16 |
|
| 17 |
> **Key Insight**: Given sufficient scale and pretraining, a plain ViT along with additional few params 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 🤗
|
| 18 |
|
| 19 |
+
The original implementation can be found in this [repository](https://github.com/tue-mps/eomt).
|
| 20 |
+
|
| 21 |
+
The HuggingFace model page is available at this [link](https://huggingface.co/papers/2503.19108).
|
| 22 |
|
| 23 |
---
|
| 24 |
|