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-Distilled 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-Distilled 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-Distilled", 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-Distilled", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 678 Bytes
ddcb7ea | 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 | {
"architectures": [
"SMSelectiveViTModelForClassification"
],
"atten_dim": 768,
"attention_scale": 8.0,
"auto_map": {
"AutoConfig": "configuration_selectivevit.SMSelectiveViTConfig",
"AutoModelForImageClassification": "modeling_selectivevit.SMSelectiveViTModelForClassification"
},
"channels": 3,
"depth": 12,
"drop_path": 0.2,
"dropout": 0.0,
"dtype": "float32",
"embed_dim": 768,
"image_size": 224,
"mask_threshold": 0.05,
"mlp_dim": 3072,
"model_type": "softmasked_selective_vit",
"num_classes": 1000,
"num_heads": 12,
"patch_drop": 0.05,
"patch_size": 16,
"transformers_version": "4.57.3",
"use_distil_token": true
}
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