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Fix image segmentation example - don't reopen image (#30481)

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Fix image segmentation example - don't repoen image
This commit is contained in:
amyeroberts
2024-05-01 16:52:57 +01:00
committed by GitHub
parent 6e0cba3cec
commit d2feb54591
1 changed files with 5 additions and 5 deletions
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10
docs/source/en/tasks/semantic_segmentation.md
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@@ -60,7 +60,7 @@ image
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/segmentation_input.jpg" alt="Segmentation Input"/> <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/segmentation_input.jpg" alt="Segmentation Input"/>
</div> </div>
We will use [nvidia/segformer-b1-finetuned-cityscapes-1024-1024](https://huggingface.co/nvidia/segformer-b1-finetuned-cityscapes-1024-1024). We will use [nvidia/segformer-b1-finetuned-cityscapes-1024-1024](https://huggingface.co/nvidia/segformer-b1-finetuned-cityscapes-1024-1024).
```python ```python
semantic_segmentation = pipeline("image-segmentation", "nvidia/segformer-b1-finetuned-cityscapes-1024-1024") semantic_segmentation = pipeline("image-segmentation", "nvidia/segformer-b1-finetuned-cityscapes-1024-1024")
@@ -68,7 +68,7 @@ results = semantic_segmentation(image)
results results
``` ```
The segmentation pipeline output includes a mask for every predicted class. The segmentation pipeline output includes a mask for every predicted class.
```bash ```bash
[{'score': None, [{'score': None,
'label': 'road', 'label': 'road',
@@ -111,11 +111,11 @@ results[-1]["mask"]
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/semantic_segmentation_output.png" alt="Semantic Segmentation Output"/> <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/semantic_segmentation_output.png" alt="Semantic Segmentation Output"/>
</div> </div>
In instance segmentation, the goal is not to classify every pixel, but to predict a mask for **every instance of an object** in a given image. It works very similar to object detection, where there is a bounding box for every instance, there's a segmentation mask instead. We will use [facebook/mask2former-swin-large-cityscapes-instance](https://huggingface.co/facebook/mask2former-swin-large-cityscapes-instance) for this. In instance segmentation, the goal is not to classify every pixel, but to predict a mask for **every instance of an object** in a given image. It works very similar to object detection, where there is a bounding box for every instance, there's a segmentation mask instead. We will use [facebook/mask2former-swin-large-cityscapes-instance](https://huggingface.co/facebook/mask2former-swin-large-cityscapes-instance) for this.
```python ```python
instance_segmentation = pipeline("image-segmentation", "facebook/mask2former-swin-large-cityscapes-instance") instance_segmentation = pipeline("image-segmentation", "facebook/mask2former-swin-large-cityscapes-instance")
results = instance_segmentation(Image.open(image)) results = instance_segmentation(image)
results results
``` ```
@@ -148,7 +148,7 @@ Panoptic segmentation combines semantic segmentation and instance segmentation,
```python ```python
panoptic_segmentation = pipeline("image-segmentation", "facebook/mask2former-swin-large-cityscapes-panoptic") panoptic_segmentation = pipeline("image-segmentation", "facebook/mask2former-swin-large-cityscapes-panoptic")
results = panoptic_segmentation(Image.open(image)) results = panoptic_segmentation(image)
results results
``` ```
As you can see below, we have more classes. We will later illustrate to see that every pixel is classified into one of the classes. As you can see below, we have more classes. We will later illustrate to see that every pixel is classified into one of the classes.