Instructions to use FelaKuti/Emotion-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use FelaKuti/Emotion-detection with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://FelaKuti/Emotion-detection") - Notebooks
- Google Colab
- Kaggle
Rename best_model.keras to emotion-detection.keras
Browse files
.gitattributes
CHANGED
|
@@ -34,3 +34,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
|
|
| 34 |
*.zst filter=lfs diff=lfs merge=lfs -text
|
| 35 |
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
| 36 |
best_model.keras filter=lfs diff=lfs merge=lfs -text
|
|
|
|
|
|
| 34 |
*.zst filter=lfs diff=lfs merge=lfs -text
|
| 35 |
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
| 36 |
best_model.keras filter=lfs diff=lfs merge=lfs -text
|
| 37 |
+
emotion-detection.keras filter=lfs diff=lfs merge=lfs -text
|
best_model.keras → emotion-detection.keras
RENAMED
|
File without changes
|