Image-to-Image
Transformers
PyTorch
TensorBoard
Spanish
English
conv_swin2sr
climate
super-resolution
Instructions to use predictia/convswin2sr_mediterranean with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use predictia/convswin2sr_mediterranean with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-to-image", model="predictia/convswin2sr_mediterranean")# Load model directly from transformers import ConvSwin2SR model = ConvSwin2SR.from_pretrained("predictia/convswin2sr_mediterranean", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "ConvSwin2SR" | |
| ], | |
| "attention_probs_dropout_prob": 0.0, | |
| "depths": [ | |
| 6, | |
| 6, | |
| 6, | |
| 6, | |
| 6, | |
| 6 | |
| ], | |
| "drop_path_rate": 0.1, | |
| "embed_dim": 180, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.0, | |
| "image_size": [ | |
| 32, | |
| 48 | |
| ], | |
| "img_range": 1, | |
| "initializer_range": 0.02, | |
| "input_shape": [ | |
| 44, | |
| 60 | |
| ], | |
| "interpolation_method": "bicubic", | |
| "layer_norm_eps": 1e-05, | |
| "mlp_ratio": 2.0, | |
| "model_type": "conv_swin2sr", | |
| "num_channels": 1, | |
| "num_heads": [ | |
| 6, | |
| 6, | |
| 6, | |
| 6, | |
| 6, | |
| 6 | |
| ], | |
| "num_high_res_covars": 0, | |
| "num_layers": 6, | |
| "patch_size": 1, | |
| "qkv_bias": true, | |
| "real_upscale": 5, | |
| "resi_connection": "1conv", | |
| "sample_size": [ | |
| 160, | |
| 240 | |
| ], | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.33.1", | |
| "upsampler": "pixelshuffle", | |
| "upscale": 8, | |
| "use_absolute_embeddings": false, | |
| "window_size": 5 | |
| } | |