Instructions to use joshjrreynaldo/vit_based_pets_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use joshjrreynaldo/vit_based_pets_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="joshjrreynaldo/vit_based_pets_classifier")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("joshjrreynaldo/vit_based_pets_classifier") model = AutoModel.from_pretrained("joshjrreynaldo/vit_based_pets_classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 549 Bytes
b590299 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | {
"_name_or_path": "google/vit-base-patch16-224-in21k",
"architectures": [
"ViTModel"
],
"attention_probs_dropout_prob": 0.0,
"encoder_stride": 16,
"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,
"model_type": "vit",
"num_attention_heads": 12,
"num_channels": 3,
"num_hidden_layers": 12,
"patch_size": 16,
"qkv_bias": true,
"torch_dtype": "float32",
"transformers_version": "4.38.2"
}
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