Image Classification
Transformers
PyTorch
Safetensors
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use dennisjooo/emotion_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dennisjooo/emotion_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="dennisjooo/emotion_classification") 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("dennisjooo/emotion_classification") model = AutoModelForImageClassification.from_pretrained("dennisjooo/emotion_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
a8e1506
1
Parent(s): 005c001
Update README.md
Browse files
README.md
CHANGED
|
@@ -55,6 +55,7 @@ Further details can be found on their [repo](https://huggingface.co/google/vit-b
|
|
| 55 |
|
| 56 |
### Data Split
|
| 57 |
|
|
|
|
| 58 |
Used a 4:1 ratio for training and development sets and a random seed of 42.
|
| 59 |
Also used a seed of 42 for batching the data, completely unrelated lol.
|
| 60 |
|
|
|
|
| 55 |
|
| 56 |
### Data Split
|
| 57 |
|
| 58 |
+
Trained on [FastJobs/Visual_Emotional_Analysis](https://huggingface.co/datasets/FastJobs/Visual_Emotional_Analysis) dataset.
|
| 59 |
Used a 4:1 ratio for training and development sets and a random seed of 42.
|
| 60 |
Also used a seed of 42 for batching the data, completely unrelated lol.
|
| 61 |
|