transformers 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-base-patch16-224-in21k-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: 0.4375
---

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# vit-base-patch16-224-in21k-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: 1.6098
- Accuracy: 0.4375

## 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: 32
- eval_batch_size: 32
- seed: 101010
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 20   | 2.0782          | 0.1938   |
| No log        | 2.0   | 40   | 2.0771          | 0.1938   |
| No log        | 3.0   | 60   | 2.0752          | 0.1875   |
| No log        | 4.0   | 80   | 2.0725          | 0.1875   |
| No log        | 5.0   | 100  | 2.0691          | 0.1812   |
| No log        | 6.0   | 120  | 2.0646          | 0.1875   |
| No log        | 7.0   | 140  | 2.0591          | 0.1875   |
| No log        | 8.0   | 160  | 2.0517          | 0.2062   |
| No log        | 9.0   | 180  | 2.0423          | 0.2062   |
| No log        | 10.0  | 200  | 2.0301          | 0.2437   |
| No log        | 11.0  | 220  | 2.0148          | 0.275    |
| No log        | 12.0  | 240  | 1.9941          | 0.2687   |
| No log        | 13.0  | 260  | 1.9721          | 0.325    |
| No log        | 14.0  | 280  | 1.9464          | 0.3375   |
| No log        | 15.0  | 300  | 1.9138          | 0.3312   |
| No log        | 16.0  | 320  | 1.8832          | 0.3438   |
| No log        | 17.0  | 340  | 1.8495          | 0.3625   |
| No log        | 18.0  | 360  | 1.8153          | 0.3688   |
| No log        | 19.0  | 380  | 1.7807          | 0.3625   |
| No log        | 20.0  | 400  | 1.7487          | 0.3812   |
| No log        | 21.0  | 420  | 1.7179          | 0.3875   |
| No log        | 22.0  | 440  | 1.6897          | 0.4125   |
| No log        | 23.0  | 460  | 1.6649          | 0.4062   |
| No log        | 24.0  | 480  | 1.6409          | 0.3937   |
| 1.7227        | 25.0  | 500  | 1.6235          | 0.4188   |
| 1.7227        | 26.0  | 520  | 1.5990          | 0.4      |
| 1.7227        | 27.0  | 540  | 1.5816          | 0.425    |
| 1.7227        | 28.0  | 560  | 1.5664          | 0.45     |
| 1.7227        | 29.0  | 580  | 1.5497          | 0.4313   |
| 1.7227        | 30.0  | 600  | 1.5323          | 0.4125   |
| 1.7227        | 31.0  | 620  | 1.5209          | 0.425    |
| 1.7227        | 32.0  | 640  | 1.5059          | 0.4      |
| 1.7227        | 33.0  | 660  | 1.5029          | 0.4188   |
| 1.7227        | 34.0  | 680  | 1.4970          | 0.4313   |
| 1.7227        | 35.0  | 700  | 1.4944          | 0.4062   |
| 1.7227        | 36.0  | 720  | 1.4992          | 0.425    |
| 1.7227        | 37.0  | 740  | 1.5060          | 0.425    |
| 1.7227        | 38.0  | 760  | 1.4960          | 0.4313   |
| 1.7227        | 39.0  | 780  | 1.5080          | 0.4313   |
| 1.7227        | 40.0  | 800  | 1.5175          | 0.425    |
| 1.7227        | 41.0  | 820  | 1.5219          | 0.4188   |
| 1.7227        | 42.0  | 840  | 1.5273          | 0.4313   |
| 1.7227        | 43.0  | 860  | 1.5318          | 0.425    |
| 1.7227        | 44.0  | 880  | 1.5446          | 0.4313   |
| 1.7227        | 45.0  | 900  | 1.5519          | 0.4375   |
| 1.7227        | 46.0  | 920  | 1.5678          | 0.4188   |
| 1.7227        | 47.0  | 940  | 1.5747          | 0.4375   |
| 1.7227        | 48.0  | 960  | 1.5843          | 0.4375   |
| 1.7227        | 49.0  | 980  | 1.5968          | 0.425    |
| 0.3221        | 50.0  | 1000 | 1.6098          | 0.4375   |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
