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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: ViT-Emotion-Classifier
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: 0.575
---
<!-- 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. -->
# ViT-Emotion-Classifier
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: 1.3652
- Accuracy: 0.575
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 40 | 1.8992 | 0.3312 |
| No log | 2.0 | 80 | 1.5939 | 0.4062 |
| No log | 3.0 | 120 | 1.4776 | 0.4688 |
| No log | 4.0 | 160 | 1.4012 | 0.4813 |
| No log | 5.0 | 200 | 1.3471 | 0.4875 |
| No log | 6.0 | 240 | 1.2877 | 0.5375 |
| No log | 7.0 | 280 | 1.2598 | 0.575 |
| No log | 8.0 | 320 | 1.3595 | 0.4938 |
| No log | 9.0 | 360 | 1.2825 | 0.5375 |
| No log | 10.0 | 400 | 1.3291 | 0.5062 |
| No log | 11.0 | 440 | 1.2422 | 0.5563 |
| No log | 12.0 | 480 | 1.2659 | 0.575 |
| 1.0646 | 13.0 | 520 | 1.3048 | 0.5062 |
| 1.0646 | 14.0 | 560 | 1.2993 | 0.5563 |
| 1.0646 | 15.0 | 600 | 1.2935 | 0.5563 |
| 1.0646 | 16.0 | 640 | 1.3589 | 0.5437 |
| 1.0646 | 17.0 | 680 | 1.2447 | 0.5938 |
| 1.0646 | 18.0 | 720 | 1.3298 | 0.5563 |
| 1.0646 | 19.0 | 760 | 1.2829 | 0.6 |
| 1.0646 | 20.0 | 800 | 1.3092 | 0.5813 |
| 1.0646 | 21.0 | 840 | 1.2895 | 0.5875 |
| 1.0646 | 22.0 | 880 | 1.3810 | 0.5625 |
| 1.0646 | 23.0 | 920 | 1.3833 | 0.5563 |
| 1.0646 | 24.0 | 960 | 1.4841 | 0.5312 |
| 0.3074 | 25.0 | 1000 | 1.3619 | 0.6062 |
| 0.3074 | 26.0 | 1040 | 1.3776 | 0.5563 |
| 0.3074 | 27.0 | 1080 | 1.3917 | 0.5875 |
| 0.3074 | 28.0 | 1120 | 1.3585 | 0.575 |
| 0.3074 | 29.0 | 1160 | 1.3455 | 0.5625 |
| 0.3074 | 30.0 | 1200 | 1.4409 | 0.5813 |
### Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
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