Instructions to use Jiqing/patched_tiny_random_vit_for_image_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jiqing/patched_tiny_random_vit_for_image_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Jiqing/patched_tiny_random_vit_for_image_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("Jiqing/patched_tiny_random_vit_for_image_classification") model = AutoModelForImageClassification.from_pretrained("Jiqing/patched_tiny_random_vit_for_image_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 576 Bytes
cc5b1d9 | 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 | {
"_name_or_path": "hf-internal-testing/tiny-random-vit",
"architectures": [
"ViTForImageClassification"
],
"attention_probs_dropout_prob": 0.1,
"encoder_stride": 16,
"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,
"qkv_bias": true,
"torchscript": true,
"transformers_version": "4.41.2",
"vocab_size": {}
}
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