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---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: emotion_classification_v1.1
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train[:5000]
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.575
- name: Precision
type: precision
value: 0.6064414347689876
- name: Recall
type: recall
value: 0.575
- name: F1
type: f1
value: 0.5730570699748332
---
<!-- 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_v1.1
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: 1.2449
- Accuracy: 0.575
- Precision: 0.6064
- Recall: 0.575
- F1: 0.5731
## 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: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log | 1.0 | 40 | 1.8287 | 0.325 | 0.2995 | 0.325 | 0.2695 |
| No log | 2.0 | 80 | 1.5621 | 0.475 | 0.4171 | 0.475 | 0.4104 |
| No log | 3.0 | 120 | 1.4485 | 0.4188 | 0.3786 | 0.4188 | 0.3710 |
| No log | 4.0 | 160 | 1.4040 | 0.4313 | 0.5179 | 0.4313 | 0.3963 |
| No log | 5.0 | 200 | 1.3333 | 0.4938 | 0.5016 | 0.4938 | 0.4654 |
| No log | 6.0 | 240 | 1.3076 | 0.4688 | 0.4698 | 0.4688 | 0.4437 |
| No log | 7.0 | 280 | 1.3531 | 0.4813 | 0.5289 | 0.4813 | 0.4834 |
| No log | 8.0 | 320 | 1.3118 | 0.4688 | 0.4606 | 0.4688 | 0.4619 |
| No log | 9.0 | 360 | 1.3326 | 0.4938 | 0.5629 | 0.4938 | 0.4744 |
| No log | 10.0 | 400 | 1.2693 | 0.4938 | 0.4825 | 0.4938 | 0.4777 |
| No log | 11.0 | 440 | 1.2310 | 0.55 | 0.5747 | 0.55 | 0.5441 |
| No log | 12.0 | 480 | 1.2673 | 0.5375 | 0.5418 | 0.5375 | 0.5316 |
| 1.0804 | 13.0 | 520 | 1.3161 | 0.5125 | 0.5321 | 0.5125 | 0.5048 |
| 1.0804 | 14.0 | 560 | 1.2517 | 0.55 | 0.5550 | 0.55 | 0.5430 |
| 1.0804 | 15.0 | 600 | 1.3344 | 0.5 | 0.5023 | 0.5 | 0.4848 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0
- Datasets 2.19.1
- Tokenizers 0.19.1
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