Instructions to use TGrote11/Handwriting_Math_Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TGrote11/Handwriting_Math_Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="TGrote11/Handwriting_Math_Classification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("TGrote11/Handwriting_Math_Classification") model = AutoModelForImageClassification.from_pretrained("TGrote11/Handwriting_Math_Classification") - Notebooks
- Google Colab
- Kaggle
File size: 615 Bytes
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"_name_or_path": "google/vit-base-patch32-224-in21k",
"architectures": [
"ViTForImageClassification"
],
"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": 32,
"problem_type": "single_label_classification",
"qkv_bias": true,
"torch_dtype": "float32",
"transformers_version": "4.44.2"
}
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