Instructions to use mo-thecreator/vit-Facial-Expression-Recognition with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mo-thecreator/vit-Facial-Expression-Recognition with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="mo-thecreator/vit-Facial-Expression-Recognition") 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("mo-thecreator/vit-Facial-Expression-Recognition") model = AutoModelForImageClassification.from_pretrained("mo-thecreator/vit-Facial-Expression-Recognition", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Mohammed Abdeldayem commited on
Update README.md
Browse files
README.md
CHANGED
|
@@ -15,6 +15,7 @@ model-index:
|
|
| 15 |
- name: Accuracy
|
| 16 |
type: accuracy
|
| 17 |
value: 0.8571428571428571
|
|
|
|
| 18 |
---
|
| 19 |
|
| 20 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
|
|
@@ -102,4 +103,4 @@ The following hyperparameters were used during training:
|
|
| 102 |
- Transformers 4.36.0
|
| 103 |
- Pytorch 2.0.0
|
| 104 |
- Datasets 2.1.0
|
| 105 |
-
- Tokenizers 0.15.0
|
|
|
|
| 15 |
- name: Accuracy
|
| 16 |
type: accuracy
|
| 17 |
value: 0.8571428571428571
|
| 18 |
+
pipeline_tag: image-classification
|
| 19 |
---
|
| 20 |
|
| 21 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
|
|
|
|
| 103 |
- Transformers 4.36.0
|
| 104 |
- Pytorch 2.0.0
|
| 105 |
- Datasets 2.1.0
|
| 106 |
+
- Tokenizers 0.15.0
|