|
--- |
|
license: other |
|
tags: |
|
- vision |
|
- image-segmentation |
|
datasets: |
|
- coco |
|
widget: |
|
- src: http://images.cocodataset.org/val2017/000000039769.jpg |
|
example_title: Cats |
|
- src: http://images.cocodataset.org/val2017/000000039770.jpg |
|
example_title: Castle |
|
--- |
|
|
|
# MaskFormer |
|
|
|
MaskFormer model trained on COCO panoptic segmentation (tiny-sized version, Swin backbone). It was introduced in the paper [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://arxiv.org/abs/2107.06278) and first released in [this repository](https://github.com/facebookresearch/MaskFormer/blob/da3e60d85fdeedcb31476b5edd7d328826ce56cc/mask_former/modeling/criterion.py#L169). |
|
|
|
Disclaimer: The team releasing MaskFormer did not write a model card for this model so this model card has been written by the Hugging Face team. |
|
|
|
## Model description |
|
|
|
MaskFormer addresses instance, semantic and panoptic segmentation with the same paradigm: by predicting a set of masks and corresponding labels. Hence, all 3 tasks are treated as if they were instance segmentation. |
|
|
|
 |
|
|
|
## Intended uses & limitations |
|
|
|
You can use this particular checkpoint for semantic segmentation. See the [model hub](https://huggingface.co/models?search=maskformer) to look for other |
|
fine-tuned versions on a task that interests you. |
|
|
|
### How to use |
|
|
|
Here is how to use this model: |
|
|
|
```python |
|
from transformers import MaskFormerFeatureExtractor, MaskFormerForInstanceSegmentation |
|
from PIL import Image |
|
import requests |
|
|
|
# load MaskFormer fine-tuned on COCO panoptic segmentation |
|
feature_extractor = MaskFormerFeatureExtractor.from_pretrained("facebook/maskformer-swin-tiny-coco") |
|
model = MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-tiny-coco") |
|
|
|
url = "http://images.cocodataset.org/val2017/000000039769.jpg" |
|
image = Image.open(requests.get(url, stream=True).raw) |
|
inputs = feature_extractor(images=image, return_tensors="pt") |
|
|
|
outputs = model(**inputs) |
|
# model predicts class_queries_logits of shape `(batch_size, num_queries)` |
|
# and masks_queries_logits of shape `(batch_size, num_queries, height, width)` |
|
class_queries_logits = outputs.class_queries_logits |
|
masks_queries_logits = outputs.masks_queries_logits |
|
|
|
# you can pass them to feature_extractor for postprocessing |
|
result = feature_extractor.post_process_panoptic_segmentation(outputs, target_sizes=[image.size[::-1]])[0] |
|
# we refer to the demo notebooks for visualization (see "Resources" section in the MaskFormer docs) |
|
predicted_panoptic_map = result["segmentation"] |
|
``` |
|
|
|
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/maskformer). |
