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---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- FastJobs/Visual_Emotional_Analysis
metrics:
- accuracy
- precision
- f1
model-index:
- name: emo-vit-base-patch16-224-in21k
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: FastJobs/Visual_Emotional_Analysis
type: FastJobs/Visual_Emotional_Analysis
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.61875
- name: Precision
type: precision
value: 0.6229001976284585
- name: F1
type: f1
value: 0.6163114517061885
---
<!-- 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. -->
# emo-vit-base-patch16-224-in21k
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 [FastJobs/Visual_Emotional_Analysis](https://huggingface.co/datasets/FastJobs/Visual_Emotional_Analysis) dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2392
- Accuracy: 0.6188
- Precision: 0.6229
- F1: 0.6163
## Training and evaluation data
### Data Split
Used a 4:1 ratio for training and development sets and a seed of 42.
### Pre-processing Augmentation
The main pre-processing phase for both training and evaluation includes:
- Resizing to (224, 224, 3)
- Normalizing images using a mean and standard deviation of [0.5, 0.5, 0.5]
Other than the aforementioned pre-processing, the training set was augmented using:
- Random horizontal & vertical flip
- Color jitter
- Random resized crop
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- 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: 10
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|
| 2.0652 | 1.0 | 10 | 1.9712 | 0.35 | 0.3441 | 0.3294 |
| 1.9006 | 2.0 | 20 | 1.6055 | 0.425 | 0.3497 | 0.3578 |
| 1.6274 | 3.0 | 30 | 1.4991 | 0.4875 | 0.5747 | 0.4621 |
| 1.4742 | 4.0 | 40 | 1.4417 | 0.4313 | 0.4744 | 0.4037 |
| 1.3546 | 5.0 | 50 | 1.3699 | 0.4125 | 0.3896 | 0.3387 |
| 1.2574 | 6.0 | 60 | 1.2200 | 0.5125 | 0.5072 | 0.4783 |
| 1.183 | 7.0 | 70 | 1.1368 | 0.5375 | 0.5802 | 0.5341 |
| 1.0869 | 8.0 | 80 | 1.1332 | 0.5687 | 0.6024 | 0.5622 |
| 1.002 | 9.0 | 90 | 1.1178 | 0.55 | 0.5663 | 0.5423 |
| 0.9453 | 10.0 | 100 | 1.1601 | 0.5563 | 0.5994 | 0.5515 |
| 0.9495 | 11.0 | 110 | 1.1202 | 0.525 | 0.5695 | 0.5266 |
| 0.7805 | 12.0 | 120 | 1.1620 | 0.5375 | 0.5577 | 0.5323 |
| 0.7487 | 13.0 | 130 | 1.2094 | 0.5687 | 0.6218 | 0.5716 |
| 0.6805 | 14.0 | 140 | 1.2662 | 0.5437 | 0.5875 | 0.5345 |
| 0.6491 | 15.0 | 150 | 1.1673 | 0.5625 | 0.5707 | 0.5511 |
| 0.6168 | 16.0 | 160 | 1.2981 | 0.475 | 0.5388 | 0.4846 |
| 0.5512 | 17.0 | 170 | 1.2624 | 0.575 | 0.6110 | 0.5726 |
| 0.5532 | 18.0 | 180 | 1.2392 | 0.6188 | 0.6229 | 0.6163 |
| 0.4931 | 19.0 | 190 | 1.4012 | 0.5375 | 0.5542 | 0.5277 |
| 0.4919 | 20.0 | 200 | 1.2323 | 0.5813 | 0.5825 | 0.5758 |
| 0.4243 | 21.0 | 210 | 1.3046 | 0.5875 | 0.5967 | 0.5750 |
| 0.3971 | 22.0 | 220 | 1.3169 | 0.5687 | 0.5812 | 0.5610 |
| 0.3534 | 23.0 | 230 | 1.4052 | 0.5625 | 0.6240 | 0.5527 |
| 0.3456 | 24.0 | 240 | 1.3372 | 0.5875 | 0.5998 | 0.5838 |
| 0.3381 | 25.0 | 250 | 1.4000 | 0.55 | 0.5589 | 0.5468 |
| 0.3786 | 26.0 | 260 | 1.3531 | 0.5687 | 0.6269 | 0.5764 |
| 0.3614 | 27.0 | 270 | 1.3696 | 0.5687 | 0.6019 | 0.5704 |
| 0.312 | 28.0 | 280 | 1.3523 | 0.6125 | 0.6351 | 0.6148 |
| 0.2643 | 29.0 | 290 | 1.4510 | 0.5813 | 0.6286 | 0.5825 |
| 0.3553 | 30.0 | 300 | 1.5255 | 0.6062 | 0.6560 | 0.6113 |
| 0.2807 | 31.0 | 310 | 1.5901 | 0.5813 | 0.5921 | 0.5655 |
| 0.3252 | 32.0 | 320 | 1.5669 | 0.575 | 0.5764 | 0.5639 |
| 0.3796 | 33.0 | 330 | 1.6251 | 0.5375 | 0.5776 | 0.5431 |
| 0.2635 | 34.0 | 340 | 1.7397 | 0.4938 | 0.5513 | 0.4944 |
| 0.2583 | 35.0 | 350 | 1.4806 | 0.6 | 0.6566 | 0.6099 |
| 0.3006 | 36.0 | 360 | 1.4808 | 0.5813 | 0.6310 | 0.5863 |
| 0.3082 | 37.0 | 370 | 1.7077 | 0.5188 | 0.5680 | 0.5156 |
| 0.3346 | 38.0 | 380 | 1.6861 | 0.575 | 0.6725 | 0.5638 |
| 0.291 | 39.0 | 390 | 1.5484 | 0.5625 | 0.5631 | 0.5535 |
| 0.2313 | 40.0 | 400 | 1.4933 | 0.5563 | 0.5564 | 0.5526 |
| 0.2163 | 41.0 | 410 | 1.5836 | 0.5938 | 0.6046 | 0.5929 |
| 0.2201 | 42.0 | 420 | 1.6363 | 0.5687 | 0.5954 | 0.5672 |
| 0.2077 | 43.0 | 430 | 1.6746 | 0.5687 | 0.5623 | 0.5622 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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