Instructions to use isp-uv-es/superIX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- TF-Keras
How to use isp-uv-es/superIX with TF-Keras:
# Note: 'keras<3.x' or 'tf_keras' must be installed (legacy) # See https://github.com/keras-team/tf-keras for more details. from huggingface_hub import from_pretrained_keras model = from_pretrained_keras("isp-uv-es/superIX") - Notebooks
- Google Colab
- Kaggle
| # It work in my personal laptop but no in the Valencia server, IDK why :'( | |
| import torch | |
| import benchmark | |
| import opensr_test | |
| import matplotlib.pyplot as plt | |
| from sr4rs.utils import load_cesbio_sr, run_sr4rs | |
| # Load the model | |
| model = load_cesbio_sr() | |
| # Load the dataset | |
| dataset = opensr_test.load("naip") | |
| lr_dataset, hr_dataset = dataset["L2A"], dataset["HRharm"] | |
| # Predict a image | |
| results = run_sr4rs( | |
| model=model, | |
| lr=lr_dataset[2], | |
| hr=hr_dataset[2], | |
| ) | |
| # Display the results | |
| fig, ax = plt.subplots(1, 3, figsize=(10, 5)) | |
| ax[0].imshow(results["lr"].transpose(1, 2, 0)/3000) | |
| ax[0].set_title("LR") | |
| ax[0].axis("off") | |
| ax[1].imshow(results["sr"].transpose(1, 2, 0)/3000) | |
| ax[1].set_title("SR") | |
| ax[1].axis("off") | |
| ax[2].imshow(results["hr"].transpose(1, 2, 0) / 3000) | |
| ax[2].set_title("HR") | |
| plt.show() | |
| # Run the experiment | |
| #benchmark.create_geotiff(model, run_sr4rs, "all", "sr4rs/", force=True) |