Image Classification
Transformers
PyTorch
TensorBoard
Safetensors
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use akahana/vit-base-cats-vs-dogs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use akahana/vit-base-cats-vs-dogs with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="akahana/vit-base-cats-vs-dogs") 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("akahana/vit-base-cats-vs-dogs") model = AutoModelForImageClassification.from_pretrained("akahana/vit-base-cats-vs-dogs", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 228 Bytes
bd30b55 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | {
"do_normalize": true,
"do_resize": true,
"feature_extractor_type": "ViTFeatureExtractor",
"image_mean": [
0.5,
0.5,
0.5
],
"image_std": [
0.5,
0.5,
0.5
],
"resample": 2,
"size": 224
}
|