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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: emotion_classification
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: 0.63125
- name: Precision
type: precision
value: 0.6430986797647803
- name: F1
type: f1
value: 0.6224944698106615
---
<!-- 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 [FastJobs/Visual_Emotional_Analysis](https://huggingface.co/datasets/FastJobs/Visual_Emotional_Analysis) dataset.
In theory, the accuracy for a random guess on this dataset is 0.1429.
It achieves the following results on the evaluation set:
- Loss: 1.1031
- Accuracy: 0.6312
- Precision: 0.6431
- F1: 0.6225
## Model description
The Vision Transformer base version trained on ImageNet-21K released by Google.
Further details can be found on their [repo](https://huggingface.co/google/vit-base-patch16-224-in21k).
## Training and evaluation data
### Data Split
Used a 4:1 ratio for training and development sets and a random seed of 42.
Also used a seed of 42 for batching the data, completely unrelated lol.
### Pre-processing Augmentation
The main pre-processing phase for both training and evaluation includes:
- Bilinear interpolation to resize the image to (224, 224, 3) because it uses ImageNet images to train the original model
- Normalizing images using a mean and standard deviation of [0.5, 0.5, 0.5] just like the original model
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: 0.0002
- 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: 20
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|
| 2.0742 | 1.0 | 10 | 2.0533 | 0.1938 | 0.1942 | 0.1858 |
| 2.0081 | 2.0 | 20 | 1.8908 | 0.3438 | 0.3701 | 0.3368 |
| 1.7211 | 3.0 | 30 | 1.5199 | 0.5312 | 0.4821 | 0.4844 |
| 1.5641 | 4.0 | 40 | 1.4248 | 0.4875 | 0.5314 | 0.4532 |
| 1.3979 | 5.0 | 50 | 1.2973 | 0.5375 | 0.5162 | 0.5023 |
| 1.2997 | 6.0 | 60 | 1.2016 | 0.525 | 0.4828 | 0.4826 |
| 1.2348 | 7.0 | 70 | 1.1670 | 0.5875 | 0.6375 | 0.5941 |
| 1.1481 | 8.0 | 80 | 1.1292 | 0.6 | 0.6111 | 0.5961 |
| 1.079 | 9.0 | 90 | 1.1782 | 0.5188 | 0.5265 | 0.5005 |
| 0.9909 | 10.0 | 100 | 1.1115 | 0.5813 | 0.5892 | 0.5668 |
| 0.9662 | 11.0 | 110 | 1.1047 | 0.5938 | 0.6336 | 0.5723 |
| 0.8149 | 12.0 | 120 | 1.0944 | 0.5563 | 0.5648 | 0.5499 |
| 0.7661 | 13.0 | 130 | 1.0932 | 0.5625 | 0.5738 | 0.5499 |
| 0.7067 | 14.0 | 140 | 1.0787 | 0.6062 | 0.6318 | 0.6045 |
| 0.6708 | 15.0 | 150 | 1.1140 | 0.6188 | 0.6463 | 0.6134 |
| 0.6268 | 16.0 | 160 | 1.0875 | 0.5813 | 0.6016 | 0.5815 |
| 0.5473 | 17.0 | 170 | 1.1483 | 0.5938 | 0.6027 | 0.5844 |
| 0.5228 | 18.0 | 180 | 1.1031 | 0.6312 | 0.6431 | 0.6225 |
| 0.4805 | 19.0 | 190 | 1.1747 | 0.5813 | 0.6057 | 0.5848 |
| 0.4995 | 20.0 | 200 | 1.1865 | 0.6062 | 0.6062 | 0.5980 |
| 0.456 | 21.0 | 210 | 1.2619 | 0.6 | 0.6020 | 0.5843 |
| 0.4697 | 22.0 | 220 | 1.2476 | 0.5625 | 0.5804 | 0.5647 |
| 0.3656 | 23.0 | 230 | 1.3106 | 0.6125 | 0.6645 | 0.6130 |
| 0.394 | 24.0 | 240 | 1.3398 | 0.5437 | 0.5627 | 0.5460 |
| 0.35 | 25.0 | 250 | 1.3391 | 0.5938 | 0.5940 | 0.5860 |
| 0.3508 | 26.0 | 260 | 1.2846 | 0.575 | 0.6070 | 0.5821 |
| 0.3106 | 27.0 | 270 | 1.3495 | 0.575 | 0.6258 | 0.5663 |
| 0.3265 | 28.0 | 280 | 1.4450 | 0.5375 | 0.6512 | 0.5248 |
| 0.2806 | 29.0 | 290 | 1.5145 | 0.5188 | 0.5840 | 0.5151 |
| 0.3276 | 30.0 | 300 | 1.5207 | 0.5188 | 0.5741 | 0.5164 |
| 0.2932 | 31.0 | 310 | 1.3179 | 0.6312 | 0.6421 | 0.6298 |
| 0.3542 | 32.0 | 320 | 1.3720 | 0.5875 | 0.6157 | 0.5780 |
| 0.3321 | 33.0 | 330 | 1.4787 | 0.5625 | 0.6088 | 0.5714 |
| 0.2641 | 34.0 | 340 | 1.5468 | 0.5375 | 0.5817 | 0.5385 |
| 0.2432 | 35.0 | 350 | 1.4893 | 0.5687 | 0.6012 | 0.5538 |
| 0.275 | 36.0 | 360 | 1.4775 | 0.575 | 0.5827 | 0.5710 |
| 0.239 | 37.0 | 370 | 1.4812 | 0.575 | 0.6100 | 0.5739 |
| 0.2658 | 38.0 | 380 | 1.7335 | 0.5563 | 0.6547 | 0.5436 |
| 0.3026 | 39.0 | 390 | 1.5692 | 0.5875 | 0.6401 | 0.5854 |
| 0.1867 | 40.0 | 400 | 1.4908 | 0.5687 | 0.5921 | 0.5741 |
| 0.1931 | 41.0 | 410 | 1.6608 | 0.5375 | 0.5834 | 0.5396 |
| 0.2416 | 42.0 | 420 | 1.5172 | 0.5938 | 0.6259 | 0.5935 |
| 0.1943 | 43.0 | 430 | 1.5260 | 0.5437 | 0.5775 | 0.5498 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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