Instructions to use kolkela/simple-cnn-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use kolkela/simple-cnn-classifier with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://kolkela/simple-cnn-classifier") - Notebooks
- Google Colab
- Kaggle
File size: 1,591 Bytes
ab0aeff | 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 | {
"classification_report": {
"glass": {
"precision": 0.6923076923076923,
"recall": 0.6428571428571429,
"f1-score": 0.6666666666666666,
"support": 42.0
},
"metal": {
"precision": 0.6195652173913043,
"recall": 0.7215189873417721,
"f1-score": 0.6666666666666666,
"support": 79.0
},
"organic": {
"precision": 0.7613636363636364,
"recall": 0.7790697674418605,
"f1-score": 0.7701149425287356,
"support": 86.0
},
"paper": {
"precision": 0.7586206896551724,
"recall": 0.6804123711340206,
"f1-score": 0.717391304347826,
"support": 97.0
},
"plastic": {
"precision": 0.6483516483516484,
"recall": 0.6344086021505376,
"f1-score": 0.6413043478260869,
"support": 93.0
},
"accuracy": 0.6952141057934509,
"macro avg": {
"precision": 0.6960417768138908,
"recall": 0.6916533741850668,
"f1-score": 0.6924287856071965,
"support": 397.0
},
"weighted avg": {
"precision": 0.6986971238573397,
"recall": 0.6952141057934509,
"f1-score": 0.6955284951982446,
"support": 397.0
}
},
"confusion_matrix": [
[
27,
5,
2,
4,
4
],
[
3,
57,
7,
7,
5
],
[
0,
5,
67,
1,
13
],
[
5,
10,
6,
66,
10
],
[
4,
15,
6,
9,
59
]
]
} |