Instructions to use evanrsl/vit_facial_emotion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use evanrsl/vit_facial_emotion with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="evanrsl/vit_facial_emotion") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("evanrsl/vit_facial_emotion") model = AutoModelForImageClassification.from_pretrained("evanrsl/vit_facial_emotion") - Notebooks
- Google Colab
- Kaggle
vit_facial_emotion
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set:
- eval_loss: 2.3959
- eval_accuracy: 0.5312
- eval_runtime: 0.8502
- eval_samples_per_second: 188.201
- eval_steps_per_second: 11.763
- epoch: 37.65
- step: 1506
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
Framework versions
- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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Model tree for evanrsl/vit_facial_emotion
Base model
google/vit-base-patch16-224-in21k