from peft import PeftModel
from transformers import AutoImageProcessor, AutoModelForVideoClassification

repo_id = "DS4AI-UPB/vivit-misinfo-lora"
base_model_id = "google/vivit-b-16x2-kinetics400"

processor = AutoImageProcessor.from_pretrained(repo_id)
base_model = AutoModelForVideoClassification.from_pretrained(
    base_model_id, num_labels=2, ignore_mismatched_sizes=True
)
model = PeftModel.from_pretrained(base_model, repo_id).eval()

Use the same frame sampling and preprocessing procedure as during training before passing pixel_values to the model.

Intended Use

Base model: google/vivit-b-16x2-kinetics400
Task: Binary misinformation classification
Modality: Video-only
Fine-tuning: LoRA / PEFT
Dataset: FakeTT
Number of classes: 2
Primary metric: Macro-F1

Research and benchmarking of video-only misinformation detection on short social-media videos.

Limitations

The model does not use titles, descriptions or other textual metadata. It can miss linguistic misinformation cues and can degrade under domain shift or a different frame-sampling strategy.

Citation

bibtex

@thesis{radu2026misinformation,
    author = {Radu, Andrei-Gabriel and Truică, Ciprian-Octavian and Apostol, Elena-Simona},
    title  = {Misinformation Detection in Social Media Videos},
    school = {National University of Science and Technology POLITEHNICA Bucharest},
    year   = {2026}
}