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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: hq_fer2013notestaugM
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: 0.6998319625011055
- name: Precision
type: precision
value: 0.7022095243425648
- name: Recall
type: recall
value: 0.6998319625011055
- name: F1
type: f1
value: 0.6999146124635052
---
<!-- 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_fer2013notestaugM
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: 0.8297
- Accuracy: 0.6998
- Precision: 0.7022
- Recall: 0.6998
- F1: 0.6999
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.2858 | 1.0 | 353 | 1.2814 | 0.5545 | 0.5432 | 0.5545 | 0.5122 |
| 1.0247 | 2.0 | 706 | 1.0343 | 0.6288 | 0.6235 | 0.6288 | 0.6136 |
| 0.9403 | 3.0 | 1059 | 0.9500 | 0.6607 | 0.6592 | 0.6607 | 0.6522 |
| 0.8501 | 4.0 | 1412 | 0.8971 | 0.6803 | 0.6761 | 0.6803 | 0.6760 |
| 0.8148 | 5.0 | 1765 | 0.8733 | 0.6857 | 0.6881 | 0.6857 | 0.6854 |
| 0.7898 | 6.0 | 2118 | 0.8526 | 0.6913 | 0.6911 | 0.6913 | 0.6888 |
| 0.7074 | 7.0 | 2471 | 0.8408 | 0.6959 | 0.6971 | 0.6959 | 0.6953 |
| 0.7273 | 8.0 | 2824 | 0.8361 | 0.6980 | 0.6971 | 0.6980 | 0.6949 |
| 0.6982 | 9.0 | 3177 | 0.8297 | 0.6998 | 0.7022 | 0.6998 | 0.6999 |
| 0.6994 | 10.0 | 3530 | 0.8287 | 0.6998 | 0.7002 | 0.6998 | 0.6991 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
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