Keras
English
remote-sensing
hyperspectral
uncertainty-quantification
conformal-prediction
mamba
foundation-models
Instructions to use DaneshSelwal/Classification-DIAS-65Bands with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use DaneshSelwal/Classification-DIAS-65Bands 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://DaneshSelwal/Classification-DIAS-65Bands") - Notebooks
- Google Colab
- Kaggle
Download spatialgcn/models/SpatialGCN_classification_report.json from DaneshSelwal/Classification-DIAS-65Bands: direct link, hf CLI and curl.
- Browser
- Download file 1.39 kB
-
https://huggingface.co/DaneshSelwal/Classification-DIAS-65Bands/resolve/main/spatialgcn/models/SpatialGCN_classification_report.json
- Command line
-
hf download hf://DaneshSelwal/Classification-DIAS-65Bands/spatialgcn/models/SpatialGCN_classification_report.json
-
curl -L -o SpatialGCN_classification_report.json https://huggingface.co/DaneshSelwal/Classification-DIAS-65Bands/resolve/main/spatialgcn/models/SpatialGCN_classification_report.json
1.39 kB
| { | |
| "0": { | |
| "precision": 1.0, | |
| "recall": 1.0, | |
| "f1-score": 1.0, | |
| "support": 2432.0 | |
| }, | |
| "1": { | |
| "precision": 1.0, | |
| "recall": 1.0, | |
| "f1-score": 1.0, | |
| "support": 1408.0 | |
| }, | |
| "2": { | |
| "precision": 0.903114186851211, | |
| "recall": 0.9222614840989399, | |
| "f1-score": 0.9125874125874126, | |
| "support": 566.0 | |
| }, | |
| "3": { | |
| "precision": 0.9686373467916366, | |
| "recall": 0.9846097471601319, | |
| "f1-score": 0.9765582409594766, | |
| "support": 2729.0 | |
| }, | |
| "4": { | |
| "precision": 0.9753476611883691, | |
| "recall": 0.970440251572327, | |
| "f1-score": 0.9728877679697352, | |
| "support": 3180.0 | |
| }, | |
| "5": { | |
| "precision": 0.9790675547098002, | |
| "recall": 0.9712128362435111, | |
| "f1-score": 0.9751243781094527, | |
| "support": 2119.0 | |
| }, | |
| "6": { | |
| "precision": 0.8122171945701357, | |
| "recall": 0.899749373433584, | |
| "f1-score": 0.8537455410225921, | |
| "support": 399.0 | |
| }, | |
| "7": { | |
| "precision": 0.9128919860627178, | |
| "recall": 0.8174726989079563, | |
| "f1-score": 0.8625514403292182, | |
| "support": 641.0 | |
| }, | |
| "accuracy": 0.9704616298055514, | |
| "macro avg": { | |
| "precision": 0.9439094912717338, | |
| "recall": 0.9457182989270563, | |
| "f1-score": 0.9441818476222359, | |
| "support": 13474.0 | |
| }, | |
| "weighted avg": { | |
| "precision": 0.9707631055262675, | |
| "recall": 0.9704616298055514, | |
| "f1-score": 0.9703995486551594, | |
| "support": 13474.0 | |
| } | |
| } |