transformers tensorboard safetensors vit image-classification generated_from_trainer dataset:imagefolder base_model:dennisjooo/emotion_classification license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
license: apache-2.0
base_model: dennisjooo/emotion_classification
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: 0.7575
---

<!-- 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 [dennisjooo/emotion_classification](https://huggingface.co/dennisjooo/emotion_classification) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7891
- Accuracy: 0.7575

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7123        | 1.0   | 25   | 0.8681          | 0.735    |
| 0.6349        | 2.0   | 50   | 0.8721          | 0.73     |
| 0.6354        | 3.0   | 75   | 0.8732          | 0.725    |
| 0.6189        | 4.0   | 100  | 0.8406          | 0.735    |
| 0.6364        | 5.0   | 125  | 0.8456          | 0.74     |
| 0.5833        | 6.0   | 150  | 0.8503          | 0.725    |
| 0.5384        | 7.0   | 175  | 0.8023          | 0.755    |
| 0.5297        | 8.0   | 200  | 0.8002          | 0.7525   |
| 0.5487        | 9.0   | 225  | 0.8253          | 0.745    |
| 0.5068        | 10.0  | 250  | 0.7891          | 0.7575   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
