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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: hq_fer2013notest
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: 0.7052268506235075
- name: Precision
type: precision
value: 0.7048074435355876
- name: Recall
type: recall
value: 0.7052268506235075
- name: F1
type: f1
value: 0.7036260157126459
---
<!-- 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. -->
# hq_fer2013notest
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: 0.8294
- Accuracy: 0.7052
- Precision: 0.7048
- Recall: 0.7052
- F1: 0.7036
## 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: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 17
- 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 | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.2982 | 1.0 | 353 | 1.2708 | 0.5635 | 0.5107 | 0.5635 | 0.5168 |
| 1.0218 | 2.0 | 706 | 1.0159 | 0.6411 | 0.6397 | 0.6411 | 0.6301 |
| 0.9437 | 3.0 | 1059 | 0.9452 | 0.6631 | 0.6698 | 0.6631 | 0.6556 |
| 0.8282 | 4.0 | 1412 | 0.8873 | 0.6829 | 0.6798 | 0.6829 | 0.6743 |
| 0.7717 | 5.0 | 1765 | 0.8612 | 0.6884 | 0.6888 | 0.6884 | 0.6835 |
| 0.7678 | 6.0 | 2118 | 0.8473 | 0.6985 | 0.6989 | 0.6985 | 0.6966 |
| 0.7096 | 7.0 | 2471 | 0.8363 | 0.7018 | 0.7001 | 0.7018 | 0.6989 |
| 0.6803 | 8.0 | 2824 | 0.8333 | 0.7036 | 0.7036 | 0.7036 | 0.7019 |
| 0.6521 | 9.0 | 3177 | 0.8309 | 0.7050 | 0.7039 | 0.7050 | 0.7028 |
| 0.6671 | 10.0 | 3530 | 0.8294 | 0.7052 | 0.7048 | 0.7052 | 0.7036 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
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