Image Classification
Keras
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Keras
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Python
Instructions to use me-aas/EinsteinNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use me-aas/EinsteinNet with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://me-aas/EinsteinNet") - Notebooks
- Google Colab
- Kaggle
| { | |
| "Validation Accuracy": 0.996, | |
| "Test Accuracy": 0.996, | |
| "Precision (Macro)": 0.996, | |
| "Recall (Macro)": 0.996, | |
| "F1 Score (Macro)": 0.996, | |
| "Training Time (seconds)": 13033.01, | |
| "Model Size (MB)": 2.54, | |
| "Trainable Parameters": 207109, | |
| "Confusion Matrix": [ | |
| [ | |
| 449, | |
| 1, | |
| 0, | |
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| ], | |
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| 1, | |
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| "Classification Report": { | |
| "0": { | |
| "precision": 0.9933628318584071, | |
| "recall": 0.9977777777777778, | |
| "f1-score": 0.9955654101995566, | |
| "support": 450 | |
| }, | |
| "1": { | |
| "precision": 0.9977728285077951, | |
| "recall": 0.9955555555555555, | |
| "f1-score": 0.996662958843159, | |
| "support": 450 | |
| }, | |
| "2": { | |
| "precision": 0.9933481152993349, | |
| "recall": 0.9955555555555555, | |
| "f1-score": 0.9944506104328523, | |
| "support": 450 | |
| }, | |
| "3": { | |
| "precision": 1.0, | |
| "recall": 1.0, | |
| "f1-score": 1.0, | |
| "support": 450 | |
| }, | |
| "4": { | |
| "precision": 0.9955357142857143, | |
| "recall": 0.9911111111111112, | |
| "f1-score": 0.9933184855233853, | |
| "support": 450 | |
| }, | |
| "accuracy": 0.996, | |
| "macro avg": { | |
| "precision": 0.9960038979902504, | |
| "recall": 0.9960000000000001, | |
| "f1-score": 0.9959994929997906, | |
| "support": 2250 | |
| }, | |
| "weighted avg": { | |
| "precision": 0.9960038979902502, | |
| "recall": 0.996, | |
| "f1-score": 0.9959994929997905, | |
| "support": 2250 | |
| } | |
| } | |
| } |