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license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: hq_fer2013
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: 0.7022445455972375
- name: Precision
type: precision
value: 0.7038651811268685
- name: Recall
type: recall
value: 0.7022445455972375
- name: F1
type: f1
value: 0.702185081437324
---
<!-- 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_fer2013
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: 0.8438
- Accuracy: 0.7022
- Precision: 0.7039
- Recall: 0.7022
- F1: 0.7022
## 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: 13
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.3081 | 1.0 | 398 | 1.3132 | 0.5555 | 0.5079 | 0.5555 | 0.5137 |
| 0.991 | 2.0 | 796 | 1.0141 | 0.6332 | 0.6356 | 0.6332 | 0.6153 |
| 0.9099 | 3.0 | 1194 | 0.9257 | 0.6682 | 0.6677 | 0.6682 | 0.6631 |
| 0.8306 | 4.0 | 1592 | 0.8832 | 0.6765 | 0.6838 | 0.6765 | 0.6747 |
| 0.7755 | 5.0 | 1990 | 0.8583 | 0.6892 | 0.6896 | 0.6892 | 0.6876 |
| 0.7129 | 6.0 | 2388 | 0.8442 | 0.6931 | 0.6951 | 0.6931 | 0.6922 |
| 0.6549 | 7.0 | 2786 | 0.8494 | 0.6952 | 0.7054 | 0.6952 | 0.6978 |
| 0.6246 | 8.0 | 3184 | 0.8394 | 0.6963 | 0.7023 | 0.6963 | 0.6977 |
| 0.6138 | 9.0 | 3582 | 0.8421 | 0.6996 | 0.7080 | 0.6996 | 0.7013 |
| 0.5824 | 10.0 | 3980 | 0.8438 | 0.7022 | 0.7039 | 0.7022 | 0.7022 |
| 0.5517 | 11.0 | 4378 | 0.8497 | 0.7002 | 0.7034 | 0.7002 | 0.7005 |
| 0.5154 | 12.0 | 4776 | 0.8508 | 0.7021 | 0.7030 | 0.7021 | 0.7018 |
| 0.5318 | 13.0 | 5174 | 0.8540 | 0.7010 | 0.7029 | 0.7010 | 0.7013 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
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