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: 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
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# 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