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# VGG Finetuned on AffectNet
VGG model taken finetuned on AffectNet data for prediction of the 7 basic emotions. The model architecture can be described in code as follows:
```
from torchvision import models
import torch.nn as nn
import torch
from huggingface_hub import hf_hub_download
class CustomVGG(nn.Module):
def __init__(self):
super(CustomVGG, self).__init__()
# Download VGG model
self.vgg = models.vgg16(pretrained=True)
# Add a final MLP to be run after the VGG model
self.vgg.classifier[6] = nn.Linear(in_features=4096, out_features=7)
def forward(self, x):
# Get features up to classifier[4] (second-to-last layer)
features = self.vgg.features(x)
features = self.vgg.avgpool(features)
features = torch.flatten(features, 1)
# Pass through first 5 classifier layers
for i in range(5):
features = self.vgg.classifier[i](features)
second_to_last = features # Features before final layer
pred = self.vgg.classifier[6](features) # Final prediction
return pred, second_to_last
# Load model
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
weights_path = hf_hub_download(repo_id="harveymannering/VGG_AffectNet7", filename="vgg_7_best.pth")
classifier = CustomVGG()
classifier.load_state_dict(torch.load(weights_path, map_location=device))
classifier = classifier.to(device)
```
Here is the loss plot for this training run. This checkpoint is taken from the epoch with the best validation loss. At it's peak it acheievd 59% accuracy on the validation set. It was trained on 7 basic emotion classes with no face cropping, but a bunch of augmentations including the standard ImageNet normalization.
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