Instructions to use basant18/Smoking-detection-MobileNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use basant18/Smoking-detection-MobileNet 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://basant18/Smoking-detection-MobileNet") - Notebooks
- Google Colab
- Kaggle
Upload training_stats.json with huggingface_hub
Browse files- training_stats.json +8 -4
training_stats.json
CHANGED
|
@@ -1,6 +1,6 @@
|
|
| 1 |
{
|
| 2 |
"phase": 2,
|
| 3 |
-
"epoch_global":
|
| 4 |
"best_val_acc": 0.9777777791023254,
|
| 5 |
"best_val_loss": 0.11201294511556625,
|
| 6 |
"history": {
|
|
@@ -38,7 +38,8 @@
|
|
| 38 |
0.2125702202320099,
|
| 39 |
0.2097943127155304,
|
| 40 |
0.2046484798192978,
|
| 41 |
-
0.21061262488365173
|
|
|
|
| 42 |
],
|
| 43 |
"accuracy": [
|
| 44 |
0.7721806168556213,
|
|
@@ -74,7 +75,8 @@
|
|
| 74 |
0.9116557240486145,
|
| 75 |
0.916042685508728,
|
| 76 |
0.9162696003913879,
|
| 77 |
-
0.9115044474601746
|
|
|
|
| 78 |
],
|
| 79 |
"val_loss": [
|
| 80 |
0.23263464868068695,
|
|
@@ -110,7 +112,8 @@
|
|
| 110 |
0.09001041203737259,
|
| 111 |
0.09129680693149567,
|
| 112 |
0.09052718430757523,
|
| 113 |
-
0.0898459255695343
|
|
|
|
| 114 |
],
|
| 115 |
"val_accuracy": [
|
| 116 |
0.9222221970558167,
|
|
@@ -146,6 +149,7 @@
|
|
| 146 |
0.9722222089767456,
|
| 147 |
0.9722222089767456,
|
| 148 |
0.9722222089767456,
|
|
|
|
| 149 |
0.9722222089767456
|
| 150 |
]
|
| 151 |
}
|
|
|
|
| 1 |
{
|
| 2 |
"phase": 2,
|
| 3 |
+
"epoch_global": 35,
|
| 4 |
"best_val_acc": 0.9777777791023254,
|
| 5 |
"best_val_loss": 0.11201294511556625,
|
| 6 |
"history": {
|
|
|
|
| 38 |
0.2125702202320099,
|
| 39 |
0.2097943127155304,
|
| 40 |
0.2046484798192978,
|
| 41 |
+
0.21061262488365173,
|
| 42 |
+
0.20049527287483215
|
| 43 |
],
|
| 44 |
"accuracy": [
|
| 45 |
0.7721806168556213,
|
|
|
|
| 75 |
0.9116557240486145,
|
| 76 |
0.916042685508728,
|
| 77 |
0.9162696003913879,
|
| 78 |
+
0.9115044474601746,
|
| 79 |
+
0.9178579449653625
|
| 80 |
],
|
| 81 |
"val_loss": [
|
| 82 |
0.23263464868068695,
|
|
|
|
| 112 |
0.09001041203737259,
|
| 113 |
0.09129680693149567,
|
| 114 |
0.09052718430757523,
|
| 115 |
+
0.0898459255695343,
|
| 116 |
+
0.09193039685487747
|
| 117 |
],
|
| 118 |
"val_accuracy": [
|
| 119 |
0.9222221970558167,
|
|
|
|
| 149 |
0.9722222089767456,
|
| 150 |
0.9722222089767456,
|
| 151 |
0.9722222089767456,
|
| 152 |
+
0.9722222089767456,
|
| 153 |
0.9722222089767456
|
| 154 |
]
|
| 155 |
}
|