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---
library_name: transformers
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: 0.475
---
<!-- 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: 1.5599
- Accuracy: 0.475
## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 5 | 2.0884 | 0.1125 |
| 2.08 | 2.0 | 10 | 2.0750 | 0.1437 |
| 2.08 | 3.0 | 15 | 2.0519 | 0.2125 |
| 2.0091 | 4.0 | 20 | 2.0177 | 0.225 |
| 2.0091 | 5.0 | 25 | 1.9777 | 0.2625 |
| 1.8779 | 6.0 | 30 | 1.9381 | 0.3125 |
| 1.8779 | 7.0 | 35 | 1.8990 | 0.3438 |
| 1.7355 | 8.0 | 40 | 1.8592 | 0.3688 |
| 1.7355 | 9.0 | 45 | 1.8217 | 0.3812 |
| 1.598 | 10.0 | 50 | 1.7844 | 0.4 |
| 1.598 | 11.0 | 55 | 1.7536 | 0.4062 |
| 1.4689 | 12.0 | 60 | 1.7217 | 0.4188 |
| 1.4689 | 13.0 | 65 | 1.7019 | 0.4188 |
| 1.3534 | 14.0 | 70 | 1.6773 | 0.4188 |
| 1.3534 | 15.0 | 75 | 1.6614 | 0.425 |
| 1.2526 | 16.0 | 80 | 1.6448 | 0.4562 |
| 1.2526 | 17.0 | 85 | 1.6306 | 0.45 |
| 1.1657 | 18.0 | 90 | 1.6201 | 0.4562 |
| 1.1657 | 19.0 | 95 | 1.6067 | 0.4562 |
| 1.0918 | 20.0 | 100 | 1.5992 | 0.45 |
| 1.0918 | 21.0 | 105 | 1.5889 | 0.4562 |
| 1.0311 | 22.0 | 110 | 1.5852 | 0.4562 |
| 1.0311 | 23.0 | 115 | 1.5767 | 0.4625 |
| 0.9814 | 24.0 | 120 | 1.5733 | 0.45 |
| 0.9814 | 25.0 | 125 | 1.5688 | 0.4625 |
| 0.9439 | 26.0 | 130 | 1.5643 | 0.4562 |
| 0.9439 | 27.0 | 135 | 1.5620 | 0.4625 |
| 0.918 | 28.0 | 140 | 1.5599 | 0.475 |
| 0.918 | 29.0 | 145 | 1.5586 | 0.4625 |
| 0.9044 | 30.0 | 150 | 1.5582 | 0.4562 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
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