---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: dog-emotion-classifier
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# dog-emotion-classifier

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7653
- Accuracy: 0.8612

## 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: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.5   | 100  | 0.5477          | 0.795    |
| No log        | 1.0   | 200  | 0.4834          | 0.83     |
| No log        | 1.5   | 300  | 0.4944          | 0.8263   |
| No log        | 2.0   | 400  | 0.5020          | 0.8187   |
| 0.4298        | 2.5   | 500  | 0.5745          | 0.83     |
| 0.4298        | 3.0   | 600  | 0.6207          | 0.8187   |
| 0.4298        | 3.5   | 700  | 0.5745          | 0.85     |
| 0.4298        | 4.0   | 800  | 0.7309          | 0.84     |
| 0.4298        | 4.5   | 900  | 0.7073          | 0.835    |
| 0.0585        | 5.0   | 1000 | 0.6339          | 0.8538   |
| 0.0585        | 5.5   | 1100 | 0.7294          | 0.8413   |
| 0.0585        | 6.0   | 1200 | 0.7083          | 0.8562   |
| 0.0585        | 6.5   | 1300 | 0.7272          | 0.8588   |
| 0.0585        | 7.0   | 1400 | 0.7358          | 0.8588   |
| 0.0042        | 7.5   | 1500 | 0.7447          | 0.86     |
| 0.0042        | 8.0   | 1600 | 0.7517          | 0.86     |
| 0.0042        | 8.5   | 1700 | 0.7581          | 0.8612   |
| 0.0042        | 9.0   | 1800 | 0.7620          | 0.8612   |
| 0.0042        | 9.5   | 1900 | 0.7645          | 0.8612   |
| 0.0012        | 10.0  | 2000 | 0.7653          | 0.8612   |


### Framework versions

- Transformers 4.57.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1