metadata
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: 0.6
emotion-classification
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 1.1597
- Accuracy: 0.6
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: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 40 | 1.5881 | 0.4813 |
| No log | 2.0 | 80 | 1.4495 | 0.4188 |
| No log | 3.0 | 120 | 1.3173 | 0.525 |
| No log | 4.0 | 160 | 1.2644 | 0.5375 |
| No log | 5.0 | 200 | 1.1238 | 0.6125 |
| No log | 6.0 | 240 | 1.3448 | 0.5563 |
| No log | 7.0 | 280 | 1.3241 | 0.5938 |
| No log | 8.0 | 320 | 1.4283 | 0.5625 |
| No log | 9.0 | 360 | 1.3231 | 0.6062 |
| No log | 10.0 | 400 | 1.4146 | 0.5938 |
Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1