Instructions to use hf-internal-testing/tiny-random-beit-pipeline with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-beit-pipeline with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="hf-internal-testing/tiny-random-beit-pipeline")# Load model directly from transformers import AutoImageProcessor, BeitForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-beit-pipeline") model = BeitForSemanticSegmentation.from_pretrained("hf-internal-testing/tiny-random-beit-pipeline", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 261 Bytes
60eb087 | 1 2 3 4 5 6 7 8 9 10 11 12 | {
"crop_size": 224,
"do_center_crop": false,
"do_normalize": true,
"do_resize": true,
"feature_extractor_type": "BeitFeatureExtractor",
"image_mean": [0.5, 0.5, 0.5],
"image_std": [0.5, 0.5, 0.5],
"resample": 2,
"size": 30
}
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