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---
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_I
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: 0.60625
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# emotion_recognition_I
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2755
- Accuracy: 0.6062
## 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: 0.0005
- 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: 0.3
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8344 | 1.0 | 5 | 1.1193 | 0.5813 |
| 0.7539 | 2.0 | 10 | 1.2210 | 0.5563 |
| 0.6334 | 3.0 | 15 | 1.2974 | 0.5188 |
| 0.6163 | 4.0 | 20 | 1.1309 | 0.6 |
| 0.4633 | 5.0 | 25 | 1.2804 | 0.5312 |
| 0.4066 | 6.0 | 30 | 1.1664 | 0.6 |
| 0.335 | 7.0 | 35 | 1.1741 | 0.6062 |
| 0.3484 | 8.0 | 40 | 1.1644 | 0.6125 |
| 0.3134 | 9.0 | 45 | 1.2799 | 0.55 |
| 0.2689 | 10.0 | 50 | 1.2276 | 0.6 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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