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---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: emotion_classification_v1.2
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train[:5000]
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.625
- name: Precision
type: precision
value: 0.620708259363687
- name: Recall
type: recall
value: 0.625
- name: F1
type: f1
value: 0.6034583857987293
---
<!-- 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_classification_v1.2
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: 1.2401
- Accuracy: 0.625
- Precision: 0.6207
- Recall: 0.625
- F1: 0.6035
## Model description
A slightly more accurate model compared to previous 1.1 version. More information needed
## Intended uses & limitations
This model is fined tune solely for face emotion recognition.
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log | 1.0 | 20 | 1.9487 | 0.3312 | 0.3554 | 0.3312 | 0.2830 |
| No log | 2.0 | 40 | 1.6735 | 0.4437 | 0.4238 | 0.4437 | 0.4232 |
| No log | 3.0 | 60 | 1.5359 | 0.4813 | 0.3990 | 0.4813 | 0.4272 |
| No log | 4.0 | 80 | 1.4249 | 0.5 | 0.4178 | 0.5 | 0.4443 |
| No log | 5.0 | 100 | 1.3733 | 0.5062 | 0.4753 | 0.5062 | 0.4653 |
| No log | 6.0 | 120 | 1.3513 | 0.5188 | 0.5076 | 0.5188 | 0.4908 |
| No log | 7.0 | 140 | 1.2377 | 0.6125 | 0.6163 | 0.6125 | 0.5976 |
| No log | 8.0 | 160 | 1.2354 | 0.6062 | 0.6131 | 0.6062 | 0.5961 |
| No log | 9.0 | 180 | 1.2574 | 0.575 | 0.5847 | 0.575 | 0.5728 |
| No log | 10.0 | 200 | 1.2493 | 0.5813 | 0.5912 | 0.5813 | 0.5776 |
| No log | 11.0 | 220 | 1.1954 | 0.5813 | 0.5795 | 0.5813 | 0.5730 |
| No log | 12.0 | 240 | 1.2283 | 0.5625 | 0.5651 | 0.5625 | 0.5598 |
| No log | 13.0 | 260 | 1.1984 | 0.5625 | 0.5800 | 0.5625 | 0.5643 |
| No log | 14.0 | 280 | 1.2308 | 0.5437 | 0.5523 | 0.5437 | 0.5414 |
| No log | 15.0 | 300 | 1.1665 | 0.5938 | 0.6005 | 0.5938 | 0.5935 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0
- Datasets 2.19.1
- Tokenizers 0.19.1
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