|
|
--- |
|
|
license: mit |
|
|
tags: |
|
|
- computer-vision |
|
|
- emotion-detection |
|
|
- facial-recognition |
|
|
- efficientnet |
|
|
- pytorch |
|
|
datasets: |
|
|
- custom |
|
|
language: |
|
|
- en |
|
|
model-index: |
|
|
- name: emotion-detection-efficientnet-b2-v1 |
|
|
results: |
|
|
- task: |
|
|
type: image-classification |
|
|
name: Emotion Detection |
|
|
dataset: |
|
|
name: Facial Emotion Dataset (Kaggle) |
|
|
type: image |
|
|
metrics: |
|
|
- type: accuracy |
|
|
value: 0.8025 |
|
|
--- |
|
|
|
|
|
# EfficientNet-B2 Emotion Detection (v1) |
|
|
|
|
|
This model classifies facial emotions into 7 categories: |
|
|
`angry`, `disgust`, `fear`, `happy`, `neutral`, `sad`, `surprise`. |
|
|
|
|
|
**Architecture:** EfficientNet-B2 |
|
|
**Training Platform:** Kaggle GPU |
|
|
**Accuracy:** ~80.25% |
|
|
**Framework:** PyTorch |
|
|
**Developer:** Varad V. Choudhari (Atman AI) |
|
|
**License:** MIT |
|
|
|
|
|
### Example Usage |
|
|
```python |
|
|
from huggingface_hub import hf_hub_download |
|
|
path = hf_hub_download( |
|
|
repo_id="AtmanAI/emotion-detection-efficientnet-b2-v1", |
|
|
filename="efficientnet_b2_emotion_final.pth" |
|
|
) |
|
|
|
