README.md

metadata

library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
  - image-classification
  - generated_from_trainer
datasets:
  - imagefolder
  - FastJobs/Visual_Emotional_Analysis
metrics:
  - accuracy
model-index:
  - name: vit-emotion-classification
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: FastJobs/Visual_Emotional_Analysis
          type: imagefolder
          config: default
          split: train
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.6125
pipeline_tag: image-classification
widget:
  - src: >-
      https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
    example_title: Tiger
  - src: >-
      https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
    example_title: Teapot

vit-emotion-classification

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the FastJobs/Visual_Emotional_Analysis dataset. It achieves the following results on the evaluation set:

• Loss: 1.3802
• Accuracy: 0.6125

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

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