Instructions to use chenged118/dragonImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chenged118/dragonImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="chenged118/dragonImageClassification") 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("chenged118/dragonImageClassification") model = AutoModelForImageClassification.from_pretrained("chenged118/dragonImageClassification") - Notebooks
- Google Colab
- Kaggle
File size: 945 Bytes
b97e263 | 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 47 48 49 50 51 | {
"_name_or_path": "google/vit-base-patch16-224-in21k",
"architectures": [
"ViTForImageClassification"
],
"attention_probs_dropout_prob": 0.0,
"encoder_stride": 16,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.0,
"hidden_size": 768,
"id2label": {
"0": 5,
"1": 2,
"10": 4,
"2": 10,
"3": 9,
"4": 3,
"5": 1,
"6": 8,
"7": 7,
"8": 6,
"9": 0
},
"image_size": 224,
"initializer_range": 0.02,
"intermediate_size": 3072,
"label2id": {
"0": "9",
"1": "5",
"2": "1",
"3": "4",
"4": "10",
"5": "0",
"6": "8",
"7": "7",
"8": "6",
"9": "3",
"10": "2"
},
"layer_norm_eps": 1e-12,
"model_type": "vit",
"num_attention_heads": 12,
"num_channels": 3,
"num_hidden_layers": 12,
"patch_size": 16,
"problem_type": "single_label_classification",
"qkv_bias": true,
"torch_dtype": "float32",
"transformers_version": "4.40.2"
}
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