Image Feature Extraction
Transformers
Safetensors
siglip_vision_model
vision
image-encoder
siglip
siglip2
Instructions to use gwkrsrch/siglip2-so400m-patch16-384 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gwkrsrch/siglip2-so400m-patch16-384 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="gwkrsrch/siglip2-so400m-patch16-384")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("gwkrsrch/siglip2-so400m-patch16-384") model = AutoModel.from_pretrained("gwkrsrch/siglip2-so400m-patch16-384") - Notebooks
- Google Colab
- Kaggle
File size: 418 Bytes
cb27c26 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | {
"architectures": [
"SiglipVisionModel"
],
"attention_dropout": 0.0,
"hidden_act": "gelu_pytorch_tanh",
"hidden_size": 1152,
"image_size": 384,
"intermediate_size": 4304,
"layer_norm_eps": 1e-06,
"model_type": "siglip_vision_model",
"num_attention_heads": 16,
"num_channels": 3,
"num_hidden_layers": 27,
"patch_size": 16,
"torch_dtype": "float32",
"transformers_version": "4.50.0"
}
|