Image Classification
timm
PyTorch
Safetensors
Transformers
variance_pad_eva
feature-extraction
custom_code
Instructions to use bn22/vit_medium_patch28_rope_reg4_gap_224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use bn22/vit_medium_patch28_rope_reg4_gap_224 with timm:
import timm model = timm.create_model("hf_hub:bn22/vit_medium_patch28_rope_reg4_gap_224", pretrained=True) - Transformers
How to use bn22/vit_medium_patch28_rope_reg4_gap_224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="bn22/vit_medium_patch28_rope_reg4_gap_224", trust_remote_code=True) pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("bn22/vit_medium_patch28_rope_reg4_gap_224", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 745 Bytes
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"architectures": [
"VariancePadEvaModel"
],
"attn_pool_num_heads": 16,
"auto_map": {
"AutoConfig": "variance_pad_eva.VariancePadEvaConfig",
"AutoModel": "variance_pad_eva.VariancePadEvaModel"
},
"class_token": false,
"depth": 20,
"dtype": "float32",
"dynamic_img_size": true,
"embed_dim": 512,
"global_pool": "map",
"img_size": 224,
"init_values": 1e-05,
"mlp_ratio": 2.6667,
"model_type": "variance_pad_eva",
"num_classes": 0,
"num_heads": 16,
"num_reg_tokens": 4,
"pad_variance_threshold": 0.01,
"patch_size": 28,
"qkv_bias": false,
"qkv_fused": true,
"ref_feat_shape": [
16,
16
],
"transformers_version": "5.0.0",
"use_abs_pos_emb": false,
"use_rot_pos_emb": true
}
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