Instructions to use nzs234/siglip2-so400m-aesthetic-scorer-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nzs234/siglip2-so400m-aesthetic-scorer-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="nzs234/siglip2-so400m-aesthetic-scorer-v1") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("nzs234/siglip2-so400m-aesthetic-scorer-v1", dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,038 Bytes
081eb5b | 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 29 30 31 32 33 34 35 36 37 38 39 40 41 | {
"architectures": [
"SiglipModel"
],
"dtype": "float32",
"initializer_factor": 1.0,
"model_type": "siglip",
"text_config": {
"attention_dropout": 0.0,
"bos_token_id": 49406,
"dtype": "float32",
"eos_token_id": 49407,
"hidden_act": "gelu_pytorch_tanh",
"hidden_size": 1152,
"intermediate_size": 4304,
"layer_norm_eps": 1e-06,
"max_position_embeddings": 64,
"model_type": "siglip_text_model",
"num_attention_heads": 16,
"num_hidden_layers": 27,
"pad_token_id": 1,
"projection_size": 1152,
"vocab_size": 256000
},
"transformers_version": "5.2.0",
"vision_config": {
"attention_dropout": 0.0,
"dtype": "float32",
"hidden_act": "gelu_pytorch_tanh",
"hidden_size": 1152,
"image_size": 512,
"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
}
}
|