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: 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: 0.5125
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: 1.3578
- Accuracy: 0.5125
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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 2.0796 | 1.0 | 10 | 2.0709 | 0.1562 |
| 2.0631 | 2.0 | 20 | 2.0496 | 0.225 |
| 2.0242 | 3.0 | 30 | 2.0148 | 0.2875 |
| 1.9387 | 4.0 | 40 | 1.9268 | 0.325 |
| 1.789 | 5.0 | 50 | 1.7454 | 0.3812 |
| 1.6216 | 6.0 | 60 | 1.5996 | 0.3937 |
| 1.4795 | 7.0 | 70 | 1.5577 | 0.375 |
| 1.3735 | 8.0 | 80 | 1.5090 | 0.4062 |
| 1.2889 | 9.0 | 90 | 1.4418 | 0.4313 |
| 1.2092 | 10.0 | 100 | 1.4209 | 0.425 |
| 1.1127 | 11.0 | 110 | 1.3828 | 0.4437 |
| 1.032 | 12.0 | 120 | 1.3507 | 0.4562 |
| 0.9616 | 13.0 | 130 | 1.3556 | 0.4875 |
| 0.9099 | 14.0 | 140 | 1.3204 | 0.5188 |
| 0.8425 | 15.0 | 150 | 1.3490 | 0.4688 |
| 0.806 | 16.0 | 160 | 1.3690 | 0.5062 |
| 0.7377 | 17.0 | 170 | 1.3344 | 0.5563 |
| 0.677 | 18.0 | 180 | 1.4178 | 0.4625 |
| 0.6071 | 19.0 | 190 | 1.3305 | 0.4875 |
| 0.5581 | 20.0 | 200 | 1.3070 | 0.5 |
| 0.5599 | 21.0 | 210 | 1.3245 | 0.4938 |
| 0.5222 | 22.0 | 220 | 1.3765 | 0.4562 |
| 0.4856 | 23.0 | 230 | 1.3345 | 0.5 |
| 0.458 | 24.0 | 240 | 1.2938 | 0.5188 |
| 0.4393 | 25.0 | 250 | 1.3380 | 0.5062 |
| 0.4239 | 26.0 | 260 | 1.3756 | 0.525 |
| 0.4443 | 27.0 | 270 | 1.4586 | 0.4813 |
| 0.4374 | 28.0 | 280 | 1.2996 | 0.55 |
| 0.3917 | 29.0 | 290 | 1.3222 | 0.5062 |
| 0.3986 | 30.0 | 300 | 1.4486 | 0.4813 |
| 0.353 | 31.0 | 310 | 1.5204 | 0.4562 |
| 0.3598 | 32.0 | 320 | 1.3027 | 0.5625 |
| 0.3538 | 33.0 | 330 | 1.6122 | 0.4313 |
| 0.3246 | 34.0 | 340 | 1.5237 | 0.4437 |
| 0.3089 | 35.0 | 350 | 1.4717 | 0.5125 |
| 0.3278 | 36.0 | 360 | 1.5666 | 0.45 |
| 0.2865 | 37.0 | 370 | 1.4377 | 0.5 |
| 0.2958 | 38.0 | 380 | 1.4766 | 0.4938 |
| 0.3036 | 39.0 | 390 | 1.5345 | 0.4375 |
| 0.286 | 40.0 | 400 | 1.4174 | 0.5062 |
| 0.3099 | 41.0 | 410 | 1.4087 | 0.4625 |
| 0.2801 | 42.0 | 420 | 1.4439 | 0.4813 |
| 0.2973 | 43.0 | 430 | 1.4712 | 0.4938 |
| 0.2892 | 44.0 | 440 | 1.4099 | 0.5188 |
| 0.2835 | 45.0 | 450 | 1.3011 | 0.5563 |
| 0.261 | 46.0 | 460 | 1.6512 | 0.4188 |
| 0.2589 | 47.0 | 470 | 1.5651 | 0.4375 |
| 0.2806 | 48.0 | 480 | 1.5194 | 0.4938 |
| 0.2749 | 49.0 | 490 | 1.4519 | 0.525 |
| 0.2482 | 50.0 | 500 | 1.4127 | 0.5188 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1