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: vit-emotions-fp16
  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: 0.94875
---

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# vit-emotions-fp16

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: 0.2406
- Accuracy: 0.9487

## 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: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 50   | 1.1462          | 0.605    |
| No log        | 2.0   | 100  | 0.9580          | 0.7175   |
| No log        | 3.0   | 150  | 0.9513          | 0.68     |
| No log        | 4.0   | 200  | 0.7491          | 0.7937   |
| No log        | 5.0   | 250  | 0.6535          | 0.8387   |
| No log        | 6.0   | 300  | 0.5758          | 0.87     |
| No log        | 7.0   | 350  | 0.5042          | 0.8688   |
| No log        | 8.0   | 400  | 0.4789          | 0.8775   |
| No log        | 9.0   | 450  | 0.3602          | 0.9337   |
| 0.7188        | 10.0  | 500  | 0.3826          | 0.905    |
| 0.7188        | 11.0  | 550  | 0.3889          | 0.8938   |
| 0.7188        | 12.0  | 600  | 0.3590          | 0.9137   |
| 0.7188        | 13.0  | 650  | 0.2929          | 0.92     |
| 0.7188        | 14.0  | 700  | 0.2881          | 0.9213   |
| 0.7188        | 15.0  | 750  | 0.2652          | 0.9363   |
| 0.7188        | 16.0  | 800  | 0.2611          | 0.9463   |
| 0.7188        | 17.0  | 850  | 0.2267          | 0.9537   |
| 0.7188        | 18.0  | 900  | 0.2394          | 0.95     |
| 0.7188        | 19.0  | 950  | 0.1969          | 0.9525   |
| 0.2754        | 20.0  | 1000 | 0.2419          | 0.9413   |


### Framework versions

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