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Posting about a very underrated model that tops paperswithcode across different segmentation benchmarks: OneFormer 👑
Posting about a very underrated model that tops paperswithcode across different segmentation benchmarks: OneFormer 👑
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OneFormer is a "truly universal" model for semantic, instance and panoptic segmentation tasks ⚔️ 
OneFormer is a "truly universal" model for semantic, instance and panoptic segmentation tasks ⚔️ 
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What makes is truly universal is that it's a single model that is trained only once and can be used across all tasks.
What makes is truly universal is that it's a single model that is trained only once and can be used across all tasks.
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The enabler here is the text conditioning, i.e. the model is given a text query that states task type along with the appropriate input, and using contrastive loss, the model learns the difference between different task types 👇 (see in the image below) 
The enabler here is the text conditioning, i.e. the model is given a text query that states task type along with the appropriate input, and using contrastive loss, the model learns the difference between different task types 👇 (see in the image below) 
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It's also super easy to use with transformers. 
It's also super easy to use with transformers. 
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```python
from transformers import OneFormerProcessor, OneFormerForUniversalSegmentation

processor = OneFormerProcessor.from_pretrained("shi-labs/oneformer_ade20k_swin_large")
model = OneFormerForUniversalSegmentation.from_pretrained("shi-labs/oneformer_ade20k_swin_large")

# swap the postprocessing and task_inputs for different types of segmentation
semantic_inputs = processor(images=image, task_inputs=["semantic"], return_tensors="pt")
semantic_outputs = model(**semantic_inputs)
predicted_semantic_map = processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]
```
from transformers import OneFormerProcessor, OneFormerForUniversalSegmentation

processor = OneFormerProcessor.from_pretrained("shi-labs/oneformer_ade20k_swin_large")
model = OneFormerForUniversalSegmentation.from_pretrained("shi-labs/oneformer_ade20k_swin_large")

# swap the postprocessing and task_inputs for different types of segmentation
semantic_inputs = processor(images=image, task_inputs=["semantic"], return_tensors="pt")
semantic_outputs = model(**semantic_inputs)
predicted_semantic_map = processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]
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I have drafted a notebook for you to try right away ✨ 
I have drafted a notebook for you to try right away ✨ 
link
https://colab.research.google.com/drive/1wfJhoTFqUqcTAYAOUc6TXUubBTmOYaVa?usp=sharing
https://colab.research.google.com/drive/1wfJhoTFqUqcTAYAOUc6TXUubBTmOYaVa?usp=sharing
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You can also check out the Space without checking out the code itself 👉 
You can also check out the Space without checking out the code itself 👉 
resource
https://huggingface.co/spaces/shi-labs/OneFormer
space
shi-labs/OneFormer
https://huggingface.co/spaces/shi-labs/OneFormer
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Posting about a very underrated model that tops paperswithcode across different segmentation benchmarks: OneFormer 👑

OneFormer is a "truly universal" model for semantic, instance and panoptic segmentation tasks ⚔️ 
What makes is truly universal is that it's a single model that is trained only once and can be used across all tasks.
The enabler here is the text conditioning, i.e. the model is given a text query that states task type along with the appropriate input, and using contrastive loss, the model learns the difference between different task types 👇 (see in the image below) 

It's also super easy to use with transformers. 
```python
from transformers import OneFormerProcessor, OneFormerForUniversalSegmentation

processor = OneFormerProcessor.from_pretrained("shi-labs/oneformer_ade20k_swin_large")
model = OneFormerForUniversalSegmentation.from_pretrained("shi-labs/oneformer_ade20k_swin_large")

# swap the postprocessing and task_inputs for different types of segmentation
semantic_inputs = processor(images=image, task_inputs=["semantic"], return_tensors="pt")
semantic_outputs = model(**semantic_inputs)
predicted_semantic_map = processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]
```
I have drafted a notebook for you to try right away ✨ https://colab.research.google.com/drive/1wfJhoTFqUqcTAYAOUc6TXUubBTmOYaVa?usp=sharing
You can also check out the Space without checking out the code itself 👉 https://huggingface.co/spaces/shi-labs/OneFormer

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