transformers tensorboard safetensors vit image-classification generated_from_trainer dataset:imagefolder base_model:google/vit-base-patch16-224-in21k license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: visual_emotion_classification_vit_base_finetunned
  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: 0.51875
---

<!-- 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. -->

# visual_emotion_classification_vit_base_finetunned

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: 1.2429
- Accuracy: 0.5188

## 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: 1e-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: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.026         | 1.25  | 100  | 2.0071          | 0.275    |
| 1.8882        | 2.5   | 200  | 1.8921          | 0.3625   |
| 1.7186        | 3.75  | 300  | 1.7326          | 0.4188   |
| 1.5892        | 5.0   | 400  | 1.6242          | 0.475    |
| 1.4942        | 6.25  | 500  | 1.5443          | 0.5125   |
| 1.3825        | 7.5   | 600  | 1.4763          | 0.5062   |
| 1.3084        | 8.75  | 700  | 1.4554          | 0.4938   |
| 1.2388        | 10.0  | 800  | 1.4057          | 0.525    |
| 1.1519        | 11.25 | 900  | 1.3756          | 0.4938   |
| 1.1054        | 12.5  | 1000 | 1.3604          | 0.4875   |
| 1.0605        | 13.75 | 1100 | 1.3597          | 0.4938   |
| 1.016         | 15.0  | 1200 | 1.3370          | 0.4938   |
| 0.9601        | 16.25 | 1300 | 1.2981          | 0.4938   |
| 0.8445        | 17.5  | 1400 | 1.2420          | 0.5563   |
| 0.8514        | 18.75 | 1500 | 1.2485          | 0.5625   |
| 0.7899        | 20.0  | 1600 | 1.2861          | 0.4875   |
| 0.7459        | 21.25 | 1700 | 1.2860          | 0.4875   |
| 0.6917        | 22.5  | 1800 | 1.2335          | 0.5813   |
| 0.6864        | 23.75 | 1900 | 1.2726          | 0.5437   |
| 0.6414        | 25.0  | 2000 | 1.2215          | 0.5375   |
| 0.5583        | 26.25 | 2100 | 1.2756          | 0.5312   |
| 0.597         | 27.5  | 2200 | 1.2314          | 0.5375   |
| 0.5654        | 28.75 | 2300 | 1.3791          | 0.5125   |
| 0.5798        | 30.0  | 2400 | 1.1890          | 0.5687   |
| 0.5247        | 31.25 | 2500 | 1.2440          | 0.5687   |
| 0.5099        | 32.5  | 2600 | 1.2787          | 0.5625   |
| 0.496         | 33.75 | 2700 | 1.2628          | 0.55     |
| 0.479         | 35.0  | 2800 | 1.3420          | 0.4875   |
| 0.4685        | 36.25 | 2900 | 1.2817          | 0.5563   |
| 0.4375        | 37.5  | 3000 | 1.3122          | 0.525    |
| 0.4314        | 38.75 | 3100 | 1.1791          | 0.5563   |
| 0.4174        | 40.0  | 3200 | 1.2322          | 0.55     |
| 0.4019        | 41.25 | 3300 | 1.3871          | 0.5125   |
| 0.3738        | 42.5  | 3400 | 1.2854          | 0.5312   |
| 0.3938        | 43.75 | 3500 | 1.3057          | 0.5375   |
| 0.369         | 45.0  | 3600 | 1.2792          | 0.5437   |
| 0.3768        | 46.25 | 3700 | 1.2761          | 0.5625   |
| 0.3202        | 47.5  | 3800 | 1.2704          | 0.5375   |
| 0.3859        | 48.75 | 3900 | 1.2746          | 0.5312   |
| 0.3689        | 50.0  | 4000 | 1.3306          | 0.5563   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
