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: 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: 0.55
---

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# 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: 1.3694
- Accuracy: 0.55

## 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: 4e-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: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 40   | 1.9385          | 0.35     |
| No log        | 2.0   | 80   | 1.6433          | 0.3875   |
| No log        | 3.0   | 120  | 1.4689          | 0.5375   |
| No log        | 4.0   | 160  | 1.3533          | 0.55     |
| No log        | 5.0   | 200  | 1.3162          | 0.5813   |
| No log        | 6.0   | 240  | 1.3131          | 0.5437   |
| No log        | 7.0   | 280  | 1.2160          | 0.6      |
| No log        | 8.0   | 320  | 1.2660          | 0.5437   |
| No log        | 9.0   | 360  | 1.2594          | 0.55     |
| No log        | 10.0  | 400  | 1.1873          | 0.5687   |
| No log        | 11.0  | 440  | 1.1169          | 0.5875   |
| No log        | 12.0  | 480  | 1.2015          | 0.5687   |
| 1.125         | 13.0  | 520  | 1.2653          | 0.5375   |
| 1.125         | 14.0  | 560  | 1.2801          | 0.5563   |
| 1.125         | 15.0  | 600  | 1.2304          | 0.5563   |
| 1.125         | 16.0  | 640  | 1.2341          | 0.5437   |
| 1.125         | 17.0  | 680  | 1.2981          | 0.5312   |
| 1.125         | 18.0  | 720  | 1.3277          | 0.5687   |
| 1.125         | 19.0  | 760  | 1.2174          | 0.5875   |
| 1.125         | 20.0  | 800  | 1.1810          | 0.6      |
| 1.125         | 21.0  | 840  | 1.2280          | 0.5687   |
| 1.125         | 22.0  | 880  | 1.3576          | 0.525    |
| 1.125         | 23.0  | 920  | 1.3897          | 0.5375   |
| 1.125         | 24.0  | 960  | 1.3216          | 0.5625   |
| 0.3612        | 25.0  | 1000 | 1.3033          | 0.6062   |
| 0.3612        | 26.0  | 1040 | 1.3501          | 0.5625   |
| 0.3612        | 27.0  | 1080 | 1.2310          | 0.575    |
| 0.3612        | 28.0  | 1120 | 1.2495          | 0.6062   |
| 0.3612        | 29.0  | 1160 | 1.2974          | 0.5875   |
| 0.3612        | 30.0  | 1200 | 1.2985          | 0.5813   |


### Framework versions

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