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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_classification
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: 0.58125
---
<!-- 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
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: 1.4116
- Accuracy: 0.5813
## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 10 | 1.3914 | 0.5312 |
| No log | 2.0 | 20 | 1.3253 | 0.4875 |
| No log | 3.0 | 30 | 1.4217 | 0.4813 |
| No log | 4.0 | 40 | 1.3711 | 0.5062 |
| No log | 5.0 | 50 | 1.3584 | 0.5 |
| No log | 6.0 | 60 | 1.3163 | 0.5 |
| No log | 7.0 | 70 | 1.3824 | 0.5188 |
| No log | 8.0 | 80 | 1.3882 | 0.525 |
| No log | 9.0 | 90 | 1.4126 | 0.5188 |
| No log | 10.0 | 100 | 1.3213 | 0.5625 |
| No log | 11.0 | 110 | 1.4385 | 0.5 |
| No log | 12.0 | 120 | 1.3729 | 0.525 |
| No log | 13.0 | 130 | 1.4603 | 0.4938 |
| No log | 14.0 | 140 | 1.5326 | 0.4688 |
| No log | 15.0 | 150 | 1.3687 | 0.5563 |
| No log | 16.0 | 160 | 1.4537 | 0.55 |
| No log | 17.0 | 170 | 1.5377 | 0.5188 |
| No log | 18.0 | 180 | 1.6417 | 0.4688 |
| No log | 19.0 | 190 | 1.5260 | 0.55 |
| No log | 20.0 | 200 | 1.6854 | 0.4938 |
| No log | 21.0 | 210 | 1.6457 | 0.5062 |
| No log | 22.0 | 220 | 1.5855 | 0.5125 |
| No log | 23.0 | 230 | 1.5083 | 0.5312 |
| No log | 24.0 | 240 | 1.5656 | 0.525 |
| No log | 25.0 | 250 | 1.5931 | 0.5125 |
| No log | 26.0 | 260 | 1.4351 | 0.5687 |
| No log | 27.0 | 270 | 1.5031 | 0.525 |
| No log | 28.0 | 280 | 1.4129 | 0.55 |
| No log | 29.0 | 290 | 1.5323 | 0.5125 |
| No log | 30.0 | 300 | 1.5217 | 0.5625 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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