Instructions to use microsoft/beit-base-patch16-224-pt22k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/beit-base-patch16-224-pt22k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="microsoft/beit-base-patch16-224-pt22k") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, BeitForMaskedImageModeling processor = AutoImageProcessor.from_pretrained("microsoft/beit-base-patch16-224-pt22k") model = BeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
File size: 736 Bytes
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"architectures": [
"BeitForMaskedImageModeling"
],
"attention_probs_dropout_prob": 0.0,
"drop_path_rate": 0.1,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.0,
"hidden_size": 768,
"image_size": 224,
"initializer_range": 0.02,
"intermediate_size": 3072,
"layer_norm_eps": 1e-12,
"layer_scale_init_value": 0.1,
"model_type": "beit",
"num_attention_heads": 12,
"num_channels": 3,
"num_hidden_layers": 12,
"patch_size": 16,
"torch_dtype": "float32",
"transformers_version": "4.11.0.dev0",
"use_absolute_position_embeddings": false,
"use_mask_token": true,
"use_mean_pooling": true,
"use_relative_position_bias": false,
"use_shared_relative_position_bias": true,
"vocab_size": 8192
}
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