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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: vit-emotions-fp16
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: 0.9859375
---
<!-- 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. -->
# vit-emotions-fp16
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: 0.0725
- Accuracy: 0.9859
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 40 | 1.3965 | 0.4938 |
| No log | 2.0 | 80 | 1.4154 | 0.425 |
| No log | 3.0 | 120 | 1.3729 | 0.4562 |
| No log | 4.0 | 160 | 1.3532 | 0.4562 |
| No log | 5.0 | 200 | 1.2993 | 0.5062 |
| No log | 6.0 | 240 | 1.3438 | 0.4938 |
| No log | 7.0 | 280 | 1.3741 | 0.5 |
| No log | 8.0 | 320 | 1.5267 | 0.4313 |
| No log | 9.0 | 360 | 1.2778 | 0.5375 |
| No log | 10.0 | 400 | 1.3864 | 0.5062 |
| No log | 11.0 | 440 | 1.4221 | 0.4875 |
| No log | 12.0 | 480 | 1.5059 | 0.5062 |
| 0.7596 | 13.0 | 520 | 1.5004 | 0.5188 |
| 0.7596 | 14.0 | 560 | 1.4539 | 0.5125 |
| 0.7596 | 15.0 | 600 | 1.5219 | 0.5375 |
| 0.7596 | 16.0 | 640 | 1.6179 | 0.4813 |
| 0.7596 | 17.0 | 680 | 1.4562 | 0.55 |
| 0.7596 | 18.0 | 720 | 1.5473 | 0.4875 |
| 0.7596 | 19.0 | 760 | 1.5820 | 0.5188 |
| 0.7596 | 20.0 | 800 | 1.5877 | 0.5125 |
| 0.7596 | 21.0 | 840 | 1.4965 | 0.55 |
| 0.7596 | 22.0 | 880 | 1.5947 | 0.5375 |
| 0.7596 | 23.0 | 920 | 1.4672 | 0.5437 |
| 0.7596 | 24.0 | 960 | 1.7930 | 0.5 |
| 0.2328 | 25.0 | 1000 | 1.8033 | 0.4875 |
| 0.2328 | 26.0 | 1040 | 1.7193 | 0.5312 |
| 0.2328 | 27.0 | 1080 | 1.8072 | 0.4813 |
| 0.2328 | 28.0 | 1120 | 1.6767 | 0.5437 |
| 0.2328 | 29.0 | 1160 | 1.6138 | 0.5625 |
| 0.2328 | 30.0 | 1200 | 1.8484 | 0.4938 |
| 0.2328 | 31.0 | 1240 | 1.7691 | 0.5062 |
| 0.2328 | 32.0 | 1280 | 1.7797 | 0.5062 |
| 0.2328 | 33.0 | 1320 | 1.7575 | 0.5375 |
| 0.2328 | 34.0 | 1360 | 1.7550 | 0.5062 |
| 0.2328 | 35.0 | 1400 | 1.7933 | 0.5 |
| 0.2328 | 36.0 | 1440 | 1.7056 | 0.5563 |
| 0.2328 | 37.0 | 1480 | 1.8739 | 0.4938 |
| 0.1517 | 38.0 | 1520 | 1.7637 | 0.5188 |
| 0.1517 | 39.0 | 1560 | 1.7178 | 0.5563 |
| 0.1517 | 40.0 | 1600 | 1.9114 | 0.5 |
| 0.1517 | 41.0 | 1640 | 1.8453 | 0.5188 |
| 0.1517 | 42.0 | 1680 | 1.7571 | 0.5625 |
| 0.1517 | 43.0 | 1720 | 1.7757 | 0.5437 |
| 0.1517 | 44.0 | 1760 | 1.8389 | 0.5125 |
| 0.1517 | 45.0 | 1800 | 1.8109 | 0.5375 |
| 0.1517 | 46.0 | 1840 | 1.8537 | 0.4688 |
| 0.1517 | 47.0 | 1880 | 1.7422 | 0.5563 |
| 0.1517 | 48.0 | 1920 | 1.7807 | 0.5687 |
| 0.1517 | 49.0 | 1960 | 1.8111 | 0.525 |
| 0.1045 | 50.0 | 2000 | 1.9057 | 0.5125 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
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