Image Classification
Transformers
TensorBoard
Safetensors
beit
Generated from Trainer
Eval Results (legacy)
Instructions to use 3una/finetuned-FER2013 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 3una/finetuned-FER2013 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="3una/finetuned-FER2013") 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("3una/finetuned-FER2013") model = AutoModelForImageClassification.from_pretrained("3una/finetuned-FER2013", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from 3una/finetuned-FER2013: direct link, hf CLI and curl.
- Browser
- Download file 494 Bytes
-
https://huggingface.co/3una/finetuned-FER2013/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://3una/finetuned-FER2013/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/3una/finetuned-FER2013/resolve/main/preprocessor_config.json
494 Bytes
| { | |
| "crop_size": { | |
| "height": 224, | |
| "width": 224 | |
| }, | |
| "do_center_crop": false, | |
| "do_normalize": true, | |
| "do_reduce_labels": false, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "feature_extractor_type": "BeitFeatureExtractor", | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_processor_type": "BeitFeatureExtractor", | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "resample": 2, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "height": 224, | |
| "width": 224 | |
| } | |
| } | |