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---
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: 0.575
---
<!-- 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. -->
# emotion_classification
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 imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2677
- Accuracy: 0.575
## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 3
- total_train_batch_size: 48
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9379 | 0.97 | 13 | 1.2947 | 0.4875 |
| 0.9235 | 1.95 | 26 | 1.3397 | 0.475 |
| 0.8298 | 3.0 | 40 | 1.2971 | 0.5563 |
| 0.8883 | 3.98 | 53 | 1.3434 | 0.4875 |
| 0.8547 | 4.95 | 66 | 1.3226 | 0.475 |
| 0.8129 | 6.0 | 80 | 1.3077 | 0.5062 |
| 0.8095 | 6.97 | 93 | 1.2503 | 0.525 |
| 0.7764 | 7.95 | 106 | 1.2989 | 0.5312 |
| 0.7004 | 9.0 | 120 | 1.3383 | 0.4813 |
| 0.7013 | 9.97 | 133 | 1.3370 | 0.5125 |
| 0.6416 | 10.95 | 146 | 1.3073 | 0.5125 |
| 0.5831 | 12.0 | 160 | 1.3192 | 0.5 |
| 0.5968 | 12.97 | 173 | 1.2394 | 0.5375 |
| 0.5434 | 13.95 | 186 | 1.3389 | 0.5188 |
| 0.4605 | 15.0 | 200 | 1.2951 | 0.525 |
| 0.4674 | 15.97 | 213 | 1.2038 | 0.5687 |
| 0.3953 | 16.95 | 226 | 1.4019 | 0.5062 |
| 0.3595 | 18.0 | 240 | 1.4442 | 0.4813 |
| 0.3619 | 18.98 | 253 | 1.4213 | 0.525 |
| 0.3304 | 19.95 | 266 | 1.2937 | 0.5437 |
| 0.34 | 21.0 | 280 | 1.3024 | 0.5687 |
| 0.4215 | 21.98 | 293 | 1.4018 | 0.5375 |
| 0.3606 | 22.95 | 306 | 1.4221 | 0.5375 |
| 0.3402 | 24.0 | 320 | 1.4987 | 0.4313 |
| 0.3058 | 24.98 | 333 | 1.5120 | 0.5125 |
| 0.3047 | 25.95 | 346 | 1.5749 | 0.5 |
| 0.3616 | 27.0 | 360 | 1.4293 | 0.5188 |
| 0.3315 | 27.98 | 373 | 1.5326 | 0.5312 |
| 0.3535 | 28.95 | 386 | 1.5095 | 0.5188 |
| 0.3056 | 29.25 | 390 | 1.5366 | 0.5 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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