Image Classification
Transformers
Safetensors
softmasked_selective_vit
vision-transformer
efficient-transformer
selective-attention
knowledge-distillation
computer-vision
custom_code
Instructions to use XAFT/SM-Selective-ViT-Base-224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use XAFT/SM-Selective-ViT-Base-224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="XAFT/SM-Selective-ViT-Base-224", trust_remote_code=True) pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModelForImageClassification model = AutoModelForImageClassification.from_pretrained("XAFT/SM-Selective-ViT-Base-224", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 365 Bytes
e01e400 fc84eac e01e400 fc84eac e01e400 fc84eac e01e400 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | {
"crop_size": 224,
"do_center_crop": true,
"do_normalize": true,
"do_rescale": true,
"do_resize": true,
"image_mean": [
0.485,
0.456,
0.406
],
"image_processor_type": "CLIPImageProcessor",
"image_std": [
0.229,
0.224,
0.225
],
"resample": 3,
"rescale_factor": 0.00392156862745098,
"size": {"shortest_edge": 236}
}
|