File size: 2,395 Bytes
362c87c 5866a87 362c87c 5866a87 362c87c 32132bd 5866a87 362c87c 5866a87 362c87c 736c913 362c87c b684f27 362c87c 5866a87 362c87c b684f27 736c913 362c87c 5866a87 362c87c 5866a87 362c87c 5866a87 362c87c 5866a87 362c87c 5866a87 736c913 b684f27 362c87c 5866a87 362c87c b684f27 5866a87 362c87c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 |
---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
{}
---
# Facial-Expression-Recognition
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the FER 2013 and AffectNet dataset datasets. It achieves the following results on the evaluation set:
Accuracy - 0.922
Loss - 0.213
### Model Description
The vit-face-expression model is a Vision Transformer fine-tuned for the task of facial emotion recognition.
It is trained on the FER2013and AffectNet datasets, which consist of facial images categorized into eight different emotions:
-anger
-contempt
-sad
-happy
-neutral
-disgust
-fear
-surprise
## Model Details
The model has been fine-tuned using the following hyperparameters:
| Hyperparameter | Value |
|-------------------------|------------|
| Train Batch Size | 32 |
| Eval Batch Size | 64 |
| Learning Rate | 2e-4 |
| Gradient Accumulation | 2 |
| LR Scheduler | Linear |
| Warmup Ratio | 0.04 |
| Num Epochs | 10 |
## How to Get Started with the Model
Example usage:
```python
from transformers import AutoImageProcessor, AutoModelForImageClassification, pipeline
pipe = pipeline("image-classification", model="HardlyHumans/Facial-expression-detection")
processor = AutoImageProcessor.from_pretrained("HardlyHumans/Facial-expression-detection")
model = AutoModelForImageClassification.from_pretrained("HardlyHumans/Facial-expression-detection")
labels = model.config.id2label
outputs = model(**inputs)
logits = outputs.logits
predicted_class_idx = logits.argmax(-1).item()
predicted_label = labels[predicted_class_idx]
```
## Environmental Impact
The net estimated CO2 emission using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) scale is around 8.82kg of CO2.
- **Developed by:** Hardly Humans club, IIT Dharwad
- **Model type:** Vision transformer
- **License:** MIT
- **Finetuned from model:** google/vit-base-patch16-224-in21k
- **Hardware Type:** T4
- **Hours used:** 8+27
- **Cloud Provider:** Google collabotary service
- **Compute Region:** South asia-1
- **Carbon Emitted:** 8.82
### Model Architecture and Objective
|