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---
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
---
<!-- 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. -->
# vit-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.
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](https://huggingface.co/datasets/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 |