Instructions to use dima806/facial_emotions_image_detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dima806/facial_emotions_image_detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="dima806/facial_emotions_image_detection") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("dima806/facial_emotions_image_detection") model = AutoModelForImageClassification.from_pretrained("dima806/facial_emotions_image_detection", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -8,22 +8,22 @@ Returns facial emotion based on facial human image.
|
|
| 8 |
|
| 9 |
See https://www.kaggle.com/code/dima806/facial-emotions-image-detection-vit for more details.
|
| 10 |
|
| 11 |
-

|
| 12 |
|
| 13 |
```
|
| 14 |
Classification report:
|
| 15 |
|
| 16 |
precision recall f1-score support
|
| 17 |
|
| 18 |
+
sad 0.8394 0.8632 0.8511 3596
|
| 19 |
+
disgust 0.9909 1.0000 0.9954 3596
|
| 20 |
+
angry 0.9022 0.9035 0.9028 3595
|
| 21 |
+
neutral 0.8752 0.8626 0.8689 3595
|
| 22 |
+
fear 0.8788 0.8532 0.8658 3596
|
| 23 |
+
surprise 0.9476 0.9449 0.9463 3596
|
| 24 |
+
happy 0.9302 0.9372 0.9336 3596
|
| 25 |
|
| 26 |
+
accuracy 0.9092 25170
|
| 27 |
+
macro avg 0.9092 0.9092 0.9091 25170
|
| 28 |
+
weighted avg 0.9092 0.9092 0.9091 25170
|
| 29 |
```
|