metadata
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
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: 0.4125
emotion_recognition
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:
- Loss: 1.7451
- Accuracy: 0.4125
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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 5 | 2.0629 | 0.1625 |
| 2.0494 | 2.0 | 10 | 2.0216 | 0.2375 |
| 2.0494 | 3.0 | 15 | 1.9567 | 0.3438 |
| 1.8758 | 4.0 | 20 | 1.8914 | 0.3937 |
| 1.8758 | 5.0 | 25 | 1.8314 | 0.3937 |
| 1.6857 | 6.0 | 30 | 1.7821 | 0.3812 |
| 1.6857 | 7.0 | 35 | 1.7451 | 0.4125 |
| 1.5477 | 8.0 | 40 | 1.7205 | 0.4125 |
| 1.5477 | 9.0 | 45 | 1.7058 | 0.4125 |
| 1.4739 | 10.0 | 50 | 1.7010 | 0.4125 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1