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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
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: 0.59375
- name: Precision
type: precision
value: 0.6599395444120348
- name: Recall
type: recall
value: 0.59375
- name: F1
type: f1
value: 0.5919790409999833
---
<!-- 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
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: 1.1926
- Accuracy: 0.5938
- Precision: 0.6599
- Recall: 0.5938
- F1: 0.5920
## 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: 8
- eval_batch_size: 8
- 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 | 80 | 1.6474 | 0.3375 | 0.3120 | 0.3375 | 0.2259 |
| No log | 2.0 | 160 | 1.4434 | 0.4625 | 0.5606 | 0.4625 | 0.4112 |
| No log | 3.0 | 240 | 1.3266 | 0.4875 | 0.5296 | 0.4875 | 0.4516 |
| No log | 4.0 | 320 | 1.2547 | 0.5375 | 0.5836 | 0.5375 | 0.5342 |
| No log | 5.0 | 400 | 1.2195 | 0.5875 | 0.6815 | 0.5875 | 0.5900 |
| No log | 6.0 | 480 | 1.1895 | 0.5563 | 0.5709 | 0.5563 | 0.5424 |
| 1.2914 | 7.0 | 560 | 1.1572 | 0.5437 | 0.5607 | 0.5437 | 0.5431 |
| 1.2914 | 8.0 | 640 | 1.1822 | 0.5563 | 0.5602 | 0.5563 | 0.5515 |
| 1.2914 | 9.0 | 720 | 1.2712 | 0.55 | 0.5695 | 0.55 | 0.5530 |
| 1.2914 | 10.0 | 800 | 1.2196 | 0.5625 | 0.5701 | 0.5625 | 0.5559 |
| 1.2914 | 11.0 | 880 | 1.2460 | 0.5312 | 0.5584 | 0.5312 | 0.5357 |
| 1.2914 | 12.0 | 960 | 1.2473 | 0.5563 | 0.5710 | 0.5563 | 0.5553 |
| 0.5247 | 13.0 | 1040 | 1.2438 | 0.575 | 0.5908 | 0.575 | 0.5761 |
| 0.5247 | 14.0 | 1120 | 1.3033 | 0.5312 | 0.5391 | 0.5312 | 0.5305 |
| 0.5247 | 15.0 | 1200 | 1.2928 | 0.5625 | 0.5861 | 0.5625 | 0.5673 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0
- Datasets 2.19.1
- Tokenizers 0.19.1
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