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: 0.59375
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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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: 1.2723
- Accuracy: 0.5938

## 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: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 40

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 20   | 2.0185          | 0.25     |
| No log        | 2.0   | 40   | 1.9216          | 0.35     |
| No log        | 3.0   | 60   | 1.8084          | 0.3875   |
| No log        | 4.0   | 80   | 1.6901          | 0.4375   |
| No log        | 5.0   | 100  | 1.6031          | 0.4562   |
| No log        | 6.0   | 120  | 1.5323          | 0.4688   |
| No log        | 7.0   | 140  | 1.4855          | 0.4813   |
| No log        | 8.0   | 160  | 1.4305          | 0.525    |
| No log        | 9.0   | 180  | 1.3853          | 0.4938   |
| No log        | 10.0  | 200  | 1.3556          | 0.5312   |
| No log        | 11.0  | 220  | 1.3141          | 0.5625   |
| No log        | 12.0  | 240  | 1.2958          | 0.5563   |
| No log        | 13.0  | 260  | 1.2810          | 0.5437   |
| No log        | 14.0  | 280  | 1.2629          | 0.6      |
| No log        | 15.0  | 300  | 1.2533          | 0.5938   |
| No log        | 16.0  | 320  | 1.2728          | 0.5813   |
| No log        | 17.0  | 340  | 1.2311          | 0.5437   |
| No log        | 18.0  | 360  | 1.2094          | 0.5938   |
| No log        | 19.0  | 380  | 1.2584          | 0.5687   |
| No log        | 20.0  | 400  | 1.2113          | 0.6125   |
| No log        | 21.0  | 420  | 1.2002          | 0.5938   |
| No log        | 22.0  | 440  | 1.2211          | 0.6062   |
| No log        | 23.0  | 460  | 1.2424          | 0.5875   |
| No log        | 24.0  | 480  | 1.2357          | 0.5813   |
| 0.9674        | 25.0  | 500  | 1.1765          | 0.5938   |
| 0.9674        | 26.0  | 520  | 1.2338          | 0.5875   |
| 0.9674        | 27.0  | 540  | 1.2333          | 0.5875   |
| 0.9674        | 28.0  | 560  | 1.2671          | 0.5563   |
| 0.9674        | 29.0  | 580  | 1.2011          | 0.6      |
| 0.9674        | 30.0  | 600  | 1.2008          | 0.6062   |
| 0.9674        | 31.0  | 620  | 1.2582          | 0.5687   |
| 0.9674        | 32.0  | 640  | 1.2820          | 0.5813   |
| 0.9674        | 33.0  | 660  | 1.2435          | 0.6      |
| 0.9674        | 34.0  | 680  | 1.2691          | 0.5875   |
| 0.9674        | 35.0  | 700  | 1.2324          | 0.6188   |
| 0.9674        | 36.0  | 720  | 1.2008          | 0.625    |
| 0.9674        | 37.0  | 740  | 1.2381          | 0.6125   |
| 0.9674        | 38.0  | 760  | 1.2494          | 0.5813   |
| 0.9674        | 39.0  | 780  | 1.2303          | 0.5938   |
| 0.9674        | 40.0  | 800  | 1.1828          | 0.6188   |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
