README.md

---
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