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semihdervis
/
cat-emotion-classifier

Image Classification
Transformers
Safetensors
vit
Generated from Trainer
Eval Results
Inference Endpoints
Model card Files Community
cat-emotion-classifier
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---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-cat-emotions
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: custom dataset
      type: imagefolder
      config: default
      split: validation
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.6352941176470588
---

<!-- 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. -->

# vit-base-cat-emotions

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 custom dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0160
- Accuracy: 0.6353

You can try out the model live [here](https://cat-emotion-classifier.streamlit.app/), and check out the [GitHub repository](https://github.com/semihdervis/cat-emotion-classifier) for more details.

## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3361        | 3.125 | 100  | 1.0125          | 0.6548   |
| 0.0723        | 6.25  | 200  | 0.9043          | 0.7381   |
| 0.0321        | 9.375 | 300  | 0.9268          | 0.7143   |


### Framework versions

- Transformers 4.44.1
- Pytorch 2.2.2+cu118
- Datasets 2.20.0
- Tokenizers 0.19.1