Image Classification
Transformers
TensorBoard
Safetensors
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use daniakartika/emotion-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use daniakartika/emotion-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="daniakartika/emotion-classifier") 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("daniakartika/emotion-classifier") model = AutoModelForImageClassification.from_pretrained("daniakartika/emotion-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from daniakartika/emotion-classifier: direct link, hf CLI and curl.
- Browser
- Download file 144 Bytes
-
https://huggingface.co/daniakartika/emotion-classifier/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://daniakartika/emotion-classifier/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/daniakartika/emotion-classifier/resolve/main/preprocessor_config.json
144 Bytes
| { | |
| "do_resize": true, | |
| "size": 224, | |
| "resample": 2, | |
| "do_normalize": true, | |
| "image_mean": [0.5, 0.5, 0.5], | |
| "image_std": [0.5, 0.5, 0.5] | |
| } |