metadata
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-emotion_classifier
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: 0.525
vit-emotion_classifier
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: 1.4782
- Accuracy: 0.525
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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 2.0776 | 1.0 | 10 | 2.0731 | 0.1437 |
| 2.0526 | 2.0 | 20 | 2.0567 | 0.1688 |
| 1.9975 | 3.0 | 30 | 2.0160 | 0.2 |
| 1.8977 | 4.0 | 40 | 1.9550 | 0.3 |
| 1.778 | 5.0 | 50 | 1.8805 | 0.3625 |
| 1.6549 | 6.0 | 60 | 1.8073 | 0.375 |
| 1.5379 | 7.0 | 70 | 1.7428 | 0.4125 |
| 1.4241 | 8.0 | 80 | 1.6957 | 0.4062 |
| 1.3212 | 9.0 | 90 | 1.6550 | 0.45 |
| 1.2245 | 10.0 | 100 | 1.6271 | 0.4437 |
| 1.1336 | 11.0 | 110 | 1.5928 | 0.4562 |
| 1.0483 | 12.0 | 120 | 1.5695 | 0.4688 |
| 0.9669 | 13.0 | 130 | 1.5452 | 0.4875 |
| 0.8889 | 14.0 | 140 | 1.5248 | 0.4875 |
| 0.815 | 15.0 | 150 | 1.5063 | 0.5062 |
| 0.7466 | 16.0 | 160 | 1.4909 | 0.4938 |
| 0.6852 | 17.0 | 170 | 1.4782 | 0.525 |
| 0.6308 | 18.0 | 180 | 1.4615 | 0.5 |
| 0.5819 | 19.0 | 190 | 1.4541 | 0.5 |
| 0.5392 | 20.0 | 200 | 1.4458 | 0.5125 |
| 0.503 | 21.0 | 210 | 1.4393 | 0.5 |
| 0.4718 | 22.0 | 220 | 1.4289 | 0.5188 |
| 0.4458 | 23.0 | 230 | 1.4238 | 0.5188 |
| 0.4234 | 24.0 | 240 | 1.4211 | 0.5125 |
| 0.405 | 25.0 | 250 | 1.4182 | 0.5 |
| 0.3905 | 26.0 | 260 | 1.4157 | 0.5062 |
| 0.379 | 27.0 | 270 | 1.4125 | 0.5062 |
| 0.3706 | 28.0 | 280 | 1.4119 | 0.5062 |
| 0.3649 | 29.0 | 290 | 1.4115 | 0.5062 |
| 0.3618 | 30.0 | 300 | 1.4111 | 0.5062 |
Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3