Instructions to use mattmdjaga/segformer_b2_clothes with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mattmdjaga/segformer_b2_clothes with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="mattmdjaga/segformer_b2_clothes")# Load model directly from transformers import AutoImageProcessor, SegformerForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("mattmdjaga/segformer_b2_clothes") model = SegformerForSemanticSegmentation.from_pretrained("mattmdjaga/segformer_b2_clothes") - Inference
- Notebooks
- Google Colab
- Kaggle
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SegFormer model fine-tuned on [ATR dataset](https://github.com/lemondan/HumanParsing-Dataset) for clothes segmentation but can also be used for human segmentation.
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The dataset on hugging face is called "mattmdjaga/human_parsing_dataset".
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**[Training code](https://github.com/mattmdjaga/segformer_b2_clothes)**.
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```python
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from transformers import SegformerImageProcessor, AutoModelForSemanticSegmentation
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SegFormer model fine-tuned on [ATR dataset](https://github.com/lemondan/HumanParsing-Dataset) for clothes segmentation but can also be used for human segmentation.
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The dataset on hugging face is called "mattmdjaga/human_parsing_dataset".
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**[Training code](https://github.com/mattmdjaga/segformer_b2_clothes)**.
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```python
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from transformers import SegformerImageProcessor, AutoModelForSemanticSegmentation
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