Feature Extraction
Transformers
PyTorch
English
clip
vision-language
probabilistic
uncertainty
custom_code
Instructions to use aalto-ml/BayesVLM-Large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aalto-ml/BayesVLM-Large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="aalto-ml/BayesVLM-Large", trust_remote_code=True)# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("aalto-ml/BayesVLM-Large", trust_remote_code=True) model = AutoModel.from_pretrained("aalto-ml/BayesVLM-Large", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 777 Bytes
ad641ec | 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 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 | {
"_valid_processor_keys": [
"images",
"do_resize",
"size",
"resample",
"do_center_crop",
"crop_size",
"do_rescale",
"rescale_factor",
"do_normalize",
"image_mean",
"image_std",
"do_convert_rgb",
"return_tensors",
"data_format",
"input_data_format"
],
"crop_size": {
"height": 224,
"width": 224
},
"do_center_crop": true,
"do_convert_rgb": true,
"do_normalize": true,
"do_rescale": true,
"do_resize": true,
"image_mean": [
0.5,
0.5,
0.5
],
"image_processor_type": "CLIPImageProcessor",
"image_std": [
0.5,
0.5,
0.5
],
"processor_class": "CLIPProcessor",
"resample": 3,
"rescale_factor": 0.00392156862745098,
"size": {
"shortest_edge": 224
}
}
|