Instructions to use hf-internal-testing/tiny-random-MaskFormerForInstanceSegmentation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-MaskFormerForInstanceSegmentation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="hf-internal-testing/tiny-random-MaskFormerForInstanceSegmentation")# Load model directly from transformers import AutoImageProcessor, MaskFormerForInstanceSegmentation processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-MaskFormerForInstanceSegmentation") model = MaskFormerForInstanceSegmentation.from_pretrained("hf-internal-testing/tiny-random-MaskFormerForInstanceSegmentation") - Notebooks
- Google Colab
- Kaggle
File size: 423 Bytes
f844aaa | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | {
"do_normalize": true,
"do_resize": true,
"feature_extractor_type": "MaskFormerFeatureExtractor",
"ignore_index": 255,
"image_mean": [
0.48500001430511475,
0.4560000002384186,
0.4059999883174896
],
"image_std": [
0.2290000021457672,
0.2239999920129776,
0.22499999403953552
],
"max_size": 2560,
"reduce_labels": false,
"resample": 2,
"size": 640,
"size_divisibility": 32
}
|