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: emotion_model_1
  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: 0.44375
---

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# emotion_model_1

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: 1.5356
- Accuracy: 0.4437

## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.0785        | 1.0   | 10   | 2.0617          | 0.125    |
| 2.0054        | 2.0   | 20   | 1.9826          | 0.275    |
| 1.8694        | 3.0   | 30   | 1.8516          | 0.325    |
| 1.7212        | 4.0   | 40   | 1.7082          | 0.3812   |
| 1.6101        | 5.0   | 50   | 1.6297          | 0.4375   |
| 1.5409        | 6.0   | 60   | 1.5981          | 0.4188   |
| 1.4801        | 7.0   | 70   | 1.5526          | 0.4437   |
| 1.433         | 8.0   | 80   | 1.5574          | 0.4813   |
| 1.4056        | 9.0   | 90   | 1.5094          | 0.5062   |
| 1.3797        | 10.0  | 100  | 1.5232          | 0.4688   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
