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---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- f1
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: 0.64375
- name: Precision
type: precision
value: 0.650616883116883
- name: F1
type: f1
value: 0.6344950707077283
---
<!-- 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: 1.1553
- Accuracy: 0.6438
- Precision: 0.6506
- F1: 0.6345
## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 150
- num_epochs: 300
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|
| 2.0799 | 1.0 | 10 | 2.0707 | 0.1313 | 0.1740 | 0.1156 |
| 2.0811 | 2.0 | 20 | 2.0681 | 0.1437 | 0.1617 | 0.1245 |
| 2.0709 | 3.0 | 30 | 2.0640 | 0.1562 | 0.1544 | 0.1330 |
| 2.0701 | 4.0 | 40 | 2.0590 | 0.1688 | 0.1463 | 0.1431 |
| 2.0639 | 5.0 | 50 | 2.0529 | 0.1812 | 0.1676 | 0.1613 |
| 2.0499 | 6.0 | 60 | 2.0439 | 0.2 | 0.2050 | 0.1871 |
| 2.0387 | 7.0 | 70 | 2.0322 | 0.25 | 0.2679 | 0.2373 |
| 2.0235 | 8.0 | 80 | 2.0141 | 0.3312 | 0.3638 | 0.3331 |
| 1.9933 | 9.0 | 90 | 1.9883 | 0.3375 | 0.3752 | 0.3392 |
| 1.9573 | 10.0 | 100 | 1.9473 | 0.3563 | 0.3940 | 0.3535 |
| 1.912 | 11.0 | 110 | 1.8863 | 0.3875 | 0.4352 | 0.3759 |
| 1.8306 | 12.0 | 120 | 1.8102 | 0.3875 | 0.4062 | 0.3586 |
| 1.7479 | 13.0 | 130 | 1.7158 | 0.4062 | 0.4056 | 0.3689 |
| 1.665 | 14.0 | 140 | 1.6250 | 0.475 | 0.4543 | 0.4248 |
| 1.6115 | 15.0 | 150 | 1.5597 | 0.4875 | 0.4646 | 0.4414 |
| 1.5716 | 16.0 | 160 | 1.5112 | 0.5125 | 0.4846 | 0.4575 |
| 1.5062 | 17.0 | 170 | 1.4672 | 0.525 | 0.4932 | 0.4925 |
| 1.4655 | 18.0 | 180 | 1.4262 | 0.5312 | 0.5018 | 0.4876 |
| 1.413 | 19.0 | 190 | 1.3851 | 0.575 | 0.5253 | 0.5317 |
| 1.3758 | 20.0 | 200 | 1.3421 | 0.5625 | 0.5900 | 0.5113 |
| 1.317 | 21.0 | 210 | 1.3156 | 0.55 | 0.5835 | 0.4996 |
| 1.291 | 22.0 | 220 | 1.2712 | 0.5938 | 0.6374 | 0.5601 |
| 1.2369 | 23.0 | 230 | 1.2697 | 0.5563 | 0.5681 | 0.5250 |
| 1.2139 | 24.0 | 240 | 1.2439 | 0.5625 | 0.5733 | 0.5417 |
| 1.1766 | 25.0 | 250 | 1.2228 | 0.5938 | 0.6099 | 0.5735 |
| 1.1483 | 26.0 | 260 | 1.2464 | 0.5625 | 0.6016 | 0.5508 |
| 1.1344 | 27.0 | 270 | 1.1877 | 0.5875 | 0.6142 | 0.5718 |
| 1.0898 | 28.0 | 280 | 1.1871 | 0.6 | 0.6481 | 0.5817 |
| 1.0515 | 29.0 | 290 | 1.1553 | 0.6438 | 0.6506 | 0.6345 |
| 1.0628 | 30.0 | 300 | 1.1603 | 0.575 | 0.6209 | 0.5727 |
| 1.0257 | 31.0 | 310 | 1.1326 | 0.6125 | 0.6312 | 0.6109 |
| 1.0048 | 32.0 | 320 | 1.1450 | 0.6125 | 0.6402 | 0.6079 |
| 0.9646 | 33.0 | 330 | 1.1250 | 0.6062 | 0.6161 | 0.6004 |
| 0.9231 | 34.0 | 340 | 1.1299 | 0.6 | 0.6183 | 0.5976 |
| 0.8944 | 35.0 | 350 | 1.1312 | 0.5938 | 0.5996 | 0.5885 |
| 0.9001 | 36.0 | 360 | 1.1293 | 0.625 | 0.6358 | 0.6220 |
| 0.8587 | 37.0 | 370 | 1.1415 | 0.6062 | 0.6122 | 0.6037 |
| 0.8708 | 38.0 | 380 | 1.1171 | 0.6062 | 0.6379 | 0.5985 |
| 0.843 | 39.0 | 390 | 1.1220 | 0.625 | 0.6658 | 0.6229 |
| 0.8038 | 40.0 | 400 | 1.1144 | 0.6188 | 0.6243 | 0.6153 |
| 0.7815 | 41.0 | 410 | 1.1538 | 0.575 | 0.6042 | 0.5679 |
| 0.7289 | 42.0 | 420 | 1.1125 | 0.6062 | 0.6218 | 0.6024 |
| 0.7255 | 43.0 | 430 | 1.1401 | 0.6 | 0.6307 | 0.5947 |
| 0.7182 | 44.0 | 440 | 1.1092 | 0.6 | 0.6121 | 0.5916 |
| 0.6533 | 45.0 | 450 | 1.1219 | 0.625 | 0.6448 | 0.6268 |
| 0.6658 | 46.0 | 460 | 1.1322 | 0.6125 | 0.6272 | 0.6135 |
| 0.6293 | 47.0 | 470 | 1.1306 | 0.6 | 0.6075 | 0.5980 |
| 0.6287 | 48.0 | 480 | 1.1227 | 0.6125 | 0.6210 | 0.6099 |
| 0.622 | 49.0 | 490 | 1.1441 | 0.5938 | 0.6154 | 0.5940 |
| 0.6004 | 50.0 | 500 | 1.1119 | 0.625 | 0.6267 | 0.6206 |
| 0.606 | 51.0 | 510 | 1.1301 | 0.5938 | 0.6146 | 0.5925 |
| 0.5924 | 52.0 | 520 | 1.1552 | 0.6062 | 0.6135 | 0.6022 |
| 0.5639 | 53.0 | 530 | 1.1956 | 0.5938 | 0.6411 | 0.5945 |
| 0.5434 | 54.0 | 540 | 1.1843 | 0.5813 | 0.5925 | 0.5765 |
| 0.5479 | 55.0 | 550 | 1.1529 | 0.6125 | 0.6247 | 0.6142 |
| 0.5227 | 56.0 | 560 | 1.1730 | 0.5687 | 0.5724 | 0.5628 |
| 0.5402 | 57.0 | 570 | 1.1919 | 0.6 | 0.6075 | 0.5954 |
| 0.4971 | 58.0 | 580 | 1.1761 | 0.5938 | 0.5984 | 0.5925 |
| 0.5004 | 59.0 | 590 | 1.2305 | 0.5687 | 0.5957 | 0.5645 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.0
- Datasets 2.14.5
- Tokenizers 0.13.3
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