Image Classification
Keras
LiteRT
English
Research
Education
Science and Technology
Artificial Intelligence
Computer Science
Computer Vision
CNN
Image
Keras
TensorFlow
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
File size: 2,093 Bytes
202f867 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 | {
"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,
0,
0
],
[
1,
448,
1,
0,
0
],
[
0,
0,
448,
0,
2
],
[
0,
0,
0,
450,
0
],
[
2,
0,
2,
0,
446
]
],
"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
}
}
} |