metadata
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
vit-base-cat-emotions
This model is a fine-tuned version of 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
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