Instructions to use hf-internal-testing/tiny-random-vit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-vit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-vit") 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("hf-internal-testing/tiny-random-vit") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-vit", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
File size: 456 Bytes
b432acc 171c467 cf94a44 b432acc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | {
"attention_probs_dropout_prob": 0.1,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 32,
"image_size": 30,
"initializer_range": 0.02,
"intermediate_size": 37,
"layer_norm_eps": 1e-12,
"model_type": "vit",
"num_attention_heads": 4,
"num_channels": 3,
"num_hidden_layers": 5,
"patch_size": 2,
"transformers_version": "4.11.0.dev0",
"vocab_size": {},
"architectures": [
"ViTForImageClassification"
]
}
|