Instructions to use Master-AI-Lab/Lumi-Transformer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Master-AI-Lab/Lumi-Transformer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Master-AI-Lab/Lumi-Transformer") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Master-AI-Lab/Lumi-Transformer", dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 645 Bytes
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"attention_probs_dropout_prob": 0.0,
"depths": [
2,
2,
18,
2
],
"drop_path_rate": 0.3,
"embed_dim": 192,
"encoder_stride": 32,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.0,
"hidden_size": 1536,
"image_size": 224,
"initializer_range": 0.02,
"layer_norm_eps": 1e-05,
"mlp_ratio": 4.0,
"model_type": "swinv2",
"num_channels": 3,
"num_heads": [
6,
12,
24,
48
],
"num_layers": 4,
"patch_size": 4,
"pretrained_window_sizes": [
0,
0,
0,
0
],
"qkv_bias": true,
"transformers_version": "4.28.1",
"use_absolute_embeddings": false,
"window_size": 24
}
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