Instructions to use cloudwoowoo/zoomodelclassproject with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cloudwoowoo/zoomodelclassproject with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="cloudwoowoo/zoomodelclassproject") 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("cloudwoowoo/zoomodelclassproject") model = AutoModelForImageClassification.from_pretrained("cloudwoowoo/zoomodelclassproject") - Notebooks
- Google Colab
- Kaggle
File size: 1,021 Bytes
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"architectures": [
"ViTForImageClassification"
],
"attention_probs_dropout_prob": 0.0,
"dtype": "float32",
"encoder_stride": 16,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.0,
"hidden_size": 768,
"id2label": {
"0": "butterfly",
"1": "cat",
"2": "chicken",
"3": "cow",
"4": "dog",
"5": "elephant",
"6": "horse",
"7": "sheep",
"8": "spider",
"9": "squirrel"
},
"image_size": 224,
"initializer_range": 0.02,
"intermediate_size": 3072,
"label2id": {
"butterfly": 0,
"cat": 1,
"chicken": 2,
"cow": 3,
"dog": 4,
"elephant": 5,
"horse": 6,
"sheep": 7,
"spider": 8,
"squirrel": 9
},
"layer_norm_eps": 1e-12,
"model_type": "vit",
"num_attention_heads": 12,
"num_channels": 3,
"num_hidden_layers": 12,
"patch_size": 16,
"pooler_act": "tanh",
"pooler_output_size": 768,
"problem_type": "single_label_classification",
"qkv_bias": true,
"transformers_version": "5.0.0",
"use_cache": false
}
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