metadata
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: emotion_recognition
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.475
emotion_recognition
This model is a fine-tuned version of google/vit-base-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 1.4479
- Accuracy: 0.475
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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 80 | 1.7877 | 0.3 |
| No log | 2.0 | 160 | 1.5989 | 0.4062 |
| No log | 3.0 | 240 | 1.4993 | 0.4313 |
| No log | 4.0 | 320 | 1.4446 | 0.4437 |
| No log | 5.0 | 400 | 1.4479 | 0.475 |
| No log | 6.0 | 480 | 1.4549 | 0.4437 |
| 0.6433 | 7.0 | 560 | 1.4635 | 0.45 |
| 0.6433 | 8.0 | 640 | 1.4767 | 0.4562 |
| 0.6433 | 9.0 | 720 | 1.4850 | 0.4437 |
| 0.6433 | 10.0 | 800 | 1.4864 | 0.4437 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1