question about training on my own data about face-emotion-recognition HOT 3 CLOSED

Thanks for your interest! You can train your classifier similarly to conventional image recognition techniques with pre-trained neural nets. You have two options:

1. Extract emotional features with on of my models, and then train a classifier from scikit-learn on top of this features. Example is available at train_emotions-pytorch-afew-vgaf.ipynb. Here the features are extracted from video frames, so I need to aggregate them into a single descriptor (see function create_dataset), but you could skip this step if you have a dataset with static photos.
2. Fine-tune the model (head only or the whole model). Example is available at train_emotions-pytorch.ipynb. Just replace the cell starting from "model=timm.create_model('tf_efficientnet_b0_ns', pretrained=False)" to "model=torch.load('../../models/affectnet_emotions/enet_b0_8_best_afew.pt')", and use your own train_dir and test_dir

from face-emotion-recognition.

Thanks for your interest! You can train your classifier similarly to conventional image recognition techniques with pre-trained neural nets. You have two options:

1. Extract emotional features with on of my models, and then train a classifier from scikit-learn on top of this features. Example is available at train_emotions-pytorch-afew-vgaf.ipynb. Here the features are extracted from video frames, so I need to aggregate them into a single descriptor (see function create_dataset), but you could skip this step if you have a dataset with static photos.
2. Fine-tune the model (head only or the whole model). Example is available at train_emotions-pytorch.ipynb. Just replace the cell starting from "model=timm.create_model('tf_efficientnet_b0_ns', pretrained=False)" to "model=torch.load('../../models/affectnet_emotions/enet_b0_8_best_afew.pt')", and use your own train_dir and test_dir

copy that~so many thanks!! OvO

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