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ViTFERPP Vit Small Patch16 224.augreg In1k | ST Studio
Recursos / ViTFERPP Vit Small Patch16 224.augreg In1k ViTFERPP Vit Small Patch16 224.augreg In1k This repository contains a Vision Transformer (ViT-Small) model fine-tuned for Facial Expression Recognition (FER).
This repository contains a Vision Transformer (ViT-Small) model fine-tuned for Facial Expression Recognition (FER).
Tipo Other Versão Revision 75c1b11
Fonte do modelo
Tipo Other
Tamanho do arquivo 82,7 MB
Versão vRevision 75c1b11
Atualizado 21 de set. de 2026
Downloads 0
Fonte Hugging Face ViT-Small for Facial Expression Recognition (FER++)
This repository contains a Vision Transformer (ViT-Small) model fine-tuned for Facial Expression Recognition (FER) .
The model is initialized from the pretrained checkpoint vit_small_patch16_224.augreg_in1k from the timm library and fine-tuned on a 7-class emotion classification dataset (aligned with FER++ taxonomy).
Model Details
Artefatos de modelo
1 artefato vitsmall2.pth
pth · 82,7 MB · SHA-256 bc3993eb6db7…ff42 · Hugging Face
Baixar Trechos de fonte
2 trechos
Model Architecture: Vision Transformer (ViT-Small)Pretrained Base: vit_small_patch16_224.augreg_in1kNumber of Parameters: ~22MTask: 7-class Facial Expression ClassificationClasses:
0: Angry1: Disgust2: Fear3: Happy4: Neutral5: Sad6: Surprise
Training Parameters & Hyperparameters The model was fine-tuned using PyTorch and the timm library with the following setup:
Hyperparameter Value Epochs 50 Batch Size 128 Base Learning Rate 5e-4 Weight Decay 0.01 Optimizer AdamW Scheduler Cosine Annealing (with 5-epoch warmup starting at 1e-6, min LR 1e-5) Image Resolution 224 x 224 (Bicubic Interpolation) Stochastic Depth (Drop Path) 0.1 Precision Mixed Precision (AMP) Data Augmentations RandAugment, Random Erasing (p=0.1)
Evaluation Results
Validation Set Performance
Accuracy: 83.83%Precision (Macro): 0.7892Recall (Macro): 0.7767F1-Score (Macro): 0.7817F1-Score (Weighted): 0.8378
Per-Class Validation Metrics: Class ID Class Name Precision Recall F1-Score Support 3 Happy 0.9247 0.9345 0.9295 473 6 Surprise 0.8636 0.7661 0.8120 124 4 Neutral 0.7943 0.8145 0.8043 275 0 Angry 0.7412 0.8400 0.7875 75 5 Sad
Test Set Performance
Accuracy: 83.68%Precision (Macro): 0.7800Recall (Macro): 0.7413F1-Score (Macro): 0.7565F1-Score (Weighted): 0.8348
Per-Class Test Metrics: Class ID Class Name Precision Recall F1-Score Support 3 Happy 0.9187 0.9378 0.9281 482 6 Surprise 0.8793 0.7969 0.8361 128 0 Angry 0.8158 0.8493 0.8322 73 5 Sad 0.8688 0.7277 0.7920 191 4 Neutral
How to Use Here is how you can load the model and run inference on an image using PyTorch and timm:
import torch
import torch.nn as nn
from torchvision import transforms
from PIL import Image
from timm import create_model
# Define class mapping
class_mapping = {
0: "Angry",
1: "Disgust",
2: "Fear",
3: "Happy",
4: "Neutral",
5: "Sad",
6: "Surprise"
}
# 1. Define image preprocessing matching validation/test set config
transform = transforms.Compose([
transforms.Resize((224, 224), interpolation=transforms.InterpolationMode.BICUBIC),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
])
# 2. Instantiate and build the model structure
model = create_model('vit_small_patch16_224.augreg_in1k', pretrained=False, num_classes=7)
# 3. Load weight state dictionary
state_dict = torch.load('vitsmall2.pth', map_location='cpu')
# Clean state_dict if it was saved using DataParallel (remove 'module.' prefix)
if list(state_dict.keys())[0].startswith('module.'):
state_dict = {k.replace('module.', ''): v for k, v in state_dict.items()}
model.load_state_dict(state_dict)
model.eval()
# 4. Perform Inference
image_path = "path_to_facial_image.jpg"
image = Image.open(image_path).convert('RGB')
input_tensor = transform(image).unsqueeze(0) # Add batch dimension
with torch.no_grad():
outputs = model(input_tensor)
probabilities = torch.softmax(outputs, dim=1)[0]
predicted_class_id = torch.argmax(probabilities).item()
print(f"Predicted emotion: {class_mapping[predicted_class_id]} ({probabilities[predicted_class_id].item():.2%})")
---
library_name: timm
tags:
- vision
- image-classification
- facial-expression-recognition
- vit
- vision-transformer
- fer
- ferplus
datasets:
- ferplus
metrics:
- accuracy
- f1
- precision
- recall
pipeline_tag: image-classification
---
# ViT-Small for Facial Expression Recognition (FER++)
This repository contains a **Vision Transformer (ViT-Small)** model fine-tuned for **Facial Expression Recognition (FER)**.
The model is initialized from the pretrained checkpoint `vit_small_patch16_224.augreg_in1k` from the `timm` library and fine-tuned on a 7-class emotion classification dataset (aligned with FER++ taxonomy).
## Model Details
- **Model Architecture:** Vision Transformer (ViT-Small)
- **Pretrained Base:** `vit_small_patch16_224.augreg_in1k`
- **Number of Parameters:** ~22M
- **Task:** 7-class Facial Expression Classification
- **Classes:**
- `0`: Angry
- `1`: Disgust
- `2`: Fear
- `3`: Happy
- `4`: Neutral
- `5`: Sad
- `6`: Surprise
## Training Parameters & Hyperparameters
The model was fine-tuned using PyTorch and the `timm` library with the following setup:
| Hyperparameter | Value |
| :--- | :--- |
| **Epochs** | 50 |
| **Batch Size** | 128 |
| **Base Learning Rate** | 5e-4 |
| **Weight Decay** | 0.01 |
| **Optimizer** | AdamW |
| **Scheduler** | Cosine Annealing (with 5-epoch warmup starting at 1e-6, min LR 1e-5) |
| **Image Resolution** | 224 x 224 (Bicubic Interpolation) |
| **Stochastic Depth (Drop Path)** | 0.1 |
| **Precision** | Mixed Precision (AMP) |
| **Data Augmentations** | RandAugment, Random Erasing (p=0.1) |
| **Loss Function** | Weighted Cross-Entropy Loss |
| **Class Weights** | `[1.560, 3.737, 2.242, 0.541, 0.527, 0.970, 1.149]` (mapping to classes 0 to 6) |
---
## Evaluation Results
### Validation Set Performance
- **Accuracy:** 83.83%
- **Precision (Macro):** 0.7892
- **Recall (Macro):** 0.7767
- **F1-Score (Macro):** 0.7817
- **F1-Score (Weighted):** 0.8378
#### Per-Class Validation Metrics:
| Class ID | Class Name | Precision | Recall | F1-Score | Support |
| :---: | :--- | :---: | :---: | :---: | :---: |
| 3 | Happy | 0.9247 | 0.9345 | 0.9295 | 473 |
| 6 | Surprise | 0.8636 | 0.7661 | 0.8120 | 124 |
| 4 | Neutral | 0.7943 | 0.8145 | 0.8043 | 275 |
| 0 | Angry | 0.7412 | 0.8400 | 0.7875 | 75 |
| 5 | Sad | 0.7758 | 0.7711 | 0.7734 | 166 |
| 2 | Fear | 0.8000 | 0.7273 | 0.7619 | 33 |
| 1 | Disgust | 0.6250...
Source context: 0 downloads · 0 likes · Pipeline image-classification · Library timm · Repo peepeeyanto/ViTFERPP_vit_small_patch16_224.augreg_in1k
Loss Function
Weighted Cross-Entropy Loss
Class Weights [1.560, 3.737, 2.242, 0.541, 0.527, 0.970, 1.149] (mapping to classes 0 to 6)
2 Fear 0.8000 0.7273 0.7619 33
1 Disgust 0.6250 0.5833 0.6034 60
2 Fear 0.6471 0.5000 0.5641 22
1 Disgust 0.6066 0.5068 0.5522 73
