Instructions to use hf-tiny-model-private/tiny-random-Mask2FormerModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-Mask2FormerModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-tiny-model-private/tiny-random-Mask2FormerModel")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hf-tiny-model-private/tiny-random-Mask2FormerModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-Mask2FormerModel") - Notebooks
- Google Colab
- Kaggle
File size: 537 Bytes
3199005 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | {
"_max_size": 1333,
"do_normalize": true,
"do_rescale": true,
"do_resize": true,
"ignore_index": 255,
"image_mean": [
0.48500001430511475,
0.4560000002384186,
0.4059999883174896
],
"image_processor_type": "Mask2FormerImageProcessor",
"image_std": [
0.2290000021457672,
0.2239999920129776,
0.22499999403953552
],
"num_labels": 80,
"reduce_labels": false,
"resample": 2,
"rescale_factor": 0.00392156862745098,
"size": {
"height": 384,
"width": 384
},
"size_divisor": 32
}
|