vox-adv-cpk.pth.tar pre-trained model weight file used for image animation, most notably with the Avatarify-Python project and the First Order Motion Model
version is fine-tuned for an additional 50 epochs with an adversarial discriminator to improve the visual quality and realism of the generated faces. Common Applications Questions about the pre-trained models of vox #127 - GitHub 28 Apr 2020 — Vox-adv-cpk.pth.tar
File Structure
# For evaluation or prediction model.eval() # Make sure to move the model to the device (GPU if available) device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') model.to(device)vox : Refers to the VoxCeleb dataset. VoxCeleb is a large-scale speaker identification dataset containing thousands of short video clips of celebrity interviews extracted from YouTube. It features diverse facial poses, lighting conditions, and natural movements, making it ideal for training talking-head models.adv : Stands for Adversarial. This indicates the model was trained using an adversarial loss, characteristic of Generative Adversarial Networks (GANs). The "adv" often implies the generator has been fine-tuned to fool a discriminator, resulting in sharper, more realistic outputs.cpk : Short for Checkpoint. This is not the final production model but a saved state at a specific training iteration. Using a checkpoint allows a user to resume training or perform inference without re-running weeks of computation..pth.tar : A hybrid extension. .pth is PyTorch’s standard file extension for model weights. The .tar (Tape Archive) suffix indicates that the .pth file has been bundled—often along with optimizer states, epoch numbers, and metadata—into a single archive, typical of PyTorch’s torch.save() function.What makes Vox-adv-cpk.pth.tar superior to a standard checkpoint? Let’s look at the numbers typically reported in the literature. vox-adv-cpk
import torch
import torch.nn as nn
Load the Model Checkpoint in PyTorch: