Intended uses & limitations

Intended Uses

Emotion classification from visual inputs (images).

Limitations

May reflect biases from the training dataset.
Performance may degrade in domains outside the training data.
Not suitable for critical or sensitive decision-making tasks.

Training and evaluation data

This model was trained on the FastJobs/Visual_Emotional_Analysis dataset.

The dataset contains:

800 images annotated with 8 emotion labels:
- Anger
- Contempt
- Disgust
- Fear
- Happy
- Neutral
- Sad
- Surprise

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0002
train_batch_size: 16
eval_batch_size: 8
seed: 42
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 10
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.8454	2.5	100	1.4373	0.4813
0.2022	5.0	200	1.4067	0.55
0.0474	7.5	300	1.3802	0.6125
0.0368	10.0	400	1.4388	0.5938

How to use this model

python

from transformers import AutoImageProcessor, ViTForImageClassification
import torch
from PIL import Image
import requests

from huggingface_hub import login
login(api_key)

image = Image.open("image.jpg").convert("RGB")

image_processor = AutoImageProcessor.from_pretrained("digo-prayudha/vit-emotion-classification")
model = ViTForImageClassification.from_pretrained("digo-prayudha/vit-emotion-classification")

inputs = image_processor(image, return_tensors="pt")

with torch.no_grad():
    logits = model(**inputs).logits

predicted_label = logits.argmax(-1).item()
print(model.config.id2label[predicted_label])

Framework versions

Transformers 4.47.1
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0