Instructions to use MITCriticalData/Sentinel-2_ViT_Autoencoder_RGB with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use MITCriticalData/Sentinel-2_ViT_Autoencoder_RGB with Keras:
# !pip install -U keras tensorflow huggingface_hub # Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here; # "jax" and "torch" also work for computation once TensorFlow is installed. import os os.environ["KERAS_BACKEND"] = "tensorflow" import keras model = keras.saving.load_model("hf://MITCriticalData/Sentinel-2_ViT_Autoencoder_RGB") - Notebooks
- Google Colab
- Kaggle
Commit ·
68fbdd4
1
Parent(s): b287226
Update README.md
Browse files
README.md
CHANGED
|
@@ -4,7 +4,7 @@ library_name: keras
|
|
| 4 |
|
| 5 |
## Model description
|
| 6 |
|
| 7 |
-
Autoencoder model trained to compress information from sentinel-2 satellite images using Vision Transformer (
|
| 8 |
The latent space of the model is given by 1024 neurons which can be used to generate embeddings from the sentinel-2 satellite images.
|
| 9 |
|
| 10 |
The model was trained using bands RGB (2, 3 and 4) (Red, Green and Blue) of the Sentinel-2 satellites and using 10 municipalities of Colombia with most dengue cases.
|
|
|
|
| 4 |
|
| 5 |
## Model description
|
| 6 |
|
| 7 |
+
Autoencoder model trained to compress information from sentinel-2 satellite images using Vision Transformer (ViT) as encoder backbone to extract features.
|
| 8 |
The latent space of the model is given by 1024 neurons which can be used to generate embeddings from the sentinel-2 satellite images.
|
| 9 |
|
| 10 |
The model was trained using bands RGB (2, 3 and 4) (Red, Green and Blue) of the Sentinel-2 satellites and using 10 municipalities of Colombia with most dengue cases.
|