Instructions to use hf-tiny-model-private/tiny-random-Swin2SRModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-Swin2SRModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="hf-tiny-model-private/tiny-random-Swin2SRModel")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hf-tiny-model-private/tiny-random-Swin2SRModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-Swin2SRModel") - Notebooks
- Google Colab
- Kaggle
File size: 711 Bytes
cbc59f3 | 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 | {
"architectures": [
"Swin2SRModel"
],
"attention_probs_dropout_prob": 0.0,
"depths": [
1,
2,
1
],
"drop_path_rate": 0.1,
"embed_dim": 16,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.0,
"image_size": 32,
"img_range": 1.0,
"initializer_range": 0.02,
"layer_norm_eps": 1e-05,
"mlp_ratio": 2.0,
"model_type": "swin2sr",
"num_channels": 3,
"num_heads": [
2,
2,
4
],
"num_layers": 3,
"patch_size": 1,
"path_norm": true,
"qkv_bias": true,
"resi_connection": "1conv",
"torch_dtype": "float32",
"transformers_version": "4.28.0.dev0",
"upsampler": "pixelshuffle",
"upscale": 2,
"use_absolute_embeddings": false,
"window_size": 2
}
|