Instructions to use nateraw/test_model_a with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nateraw/test_model_a with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="nateraw/test_model_a") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("nateraw/test_model_a") model = AutoModelForImageClassification.from_pretrained("nateraw/test_model_a", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 583 Bytes
25e3208 | 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 | {
"_name_or_path": "lysandre/tiny-vit-random",
"architectures": [
"ViTForImageClassification"
],
"attention_probs_dropout_prob": 0.0,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.0,
"hidden_size": 32,
"id2label": {
"0": "cat",
"1": "dog"
},
"image_size": 224,
"initializer_range": 0.02,
"intermediate_size": 32,
"label2id": {
"cat": "0",
"dog": "1"
},
"layer_norm_eps": 1e-12,
"model_type": "vit",
"num_attention_heads": 2,
"num_channels": 3,
"num_hidden_layers": 2,
"patch_size": 16,
"transformers_version": "4.8.2"
}
|