---
license: other
library_name: pytorch
tags:
  - clip
  - vifi-clip
  - feature-extraction
  - video-classification
  - deepfake-detection
pipeline_tag: video-classification
---

# DAViD — Checkpoints (ViFi-CLIP encoder + classification head)

Model weights for **[DAViD](https://huggingface.co/spaces/aitf-its-tim3-dfk/David)**,
a deepfake & AI-generated video/image detector.

This repo hosts two checkpoints:

| File | Size | Description |
|---|---|---|
| `k400_clip_complete_finetuned_30_epochs.pth` | ~1.6 GB | ViFi-CLIP (ViT-B/16) image encoder, fine-tuned on Kinetics-400 for 30 epochs |
| `best_detector_model.pt` | ~3 MB | MLP classification head (`dense → dense1 → dense2`), trained on the DAViD dataset + CDDB |

## How they fit together

1. **Encoder** — a ViFi-CLIP (ViT-B/16) visual backbone fine-tuned on
   Kinetics-400. Each frame (or image) is encoded into a **512-dim** embedding.
2. **Classification head** — a lightweight MLP that maps the (averaged) 512-dim
   embedding to 3 classes: `real`, `deepfake`, `ai_gen`. It was trained on a mix
   of the DAViD video dataset and **CDDB** (an image-based deepfake benchmark),
   so it supports both video and single-image input.

## Why these live on the Hub

The DAViD Space downloads these at Docker build time. They were previously on
Google Drive, but Drive throttles datacenter IPs and broke the Space build.
Serving them from the HF Hub is reliable from HF's build infrastructure.

## Usage

### 1. Get the model code from GitHub

The model definitions (`model.py`, `encoder.py`, and the `clip/` package) are
**not** in this weights repo — they live in the training repo
[**`aitf-its-tim3-dfk/david`**](https://github.com/aitf-its-tim3-dfk/david)
(branch `feat-cddb`). Clone it first and run from inside it:

```bash
git clone -b feat-cddb https://github.com/aitf-its-tim3-dfk/david
cd david
pip install -r requirements.txt
```

This is what makes `from model import ...` and `from encoder import ...` below work.

### 2. Download the checkpoints (no auth needed — public repo)

```python
from huggingface_hub import hf_hub_download

REPO = "aitf-its-tim3-dfk/david-encoder"
encoder_ckpt    = hf_hub_download(REPO, "k400_clip_complete_finetuned_30_epochs.pth")
classifier_ckpt = hf_hub_download(REPO, "best_detector_model.pt")
```

### 3. Load and run

```python
import torch
from encoder import load_feature_extractor   # from the cloned GitHub repo
from model import ClassificationHead          # from the cloned GitHub repo

feature_extractor = load_feature_extractor(
    arch="ViT-B/16",
    class_names=("real", "deepfake", "ai_gen"),
    checkpoint_path=encoder_ckpt,
).eval()

classifier = ClassificationHead(input_dim=512, num_classes=3)
classifier.load_state_dict(torch.load(classifier_ckpt, map_location="cpu", weights_only=False))
classifier.eval()

# feats = feature_extractor.image_encoder(frames)   # (N, 512)
# logits = classifier(feats.mean(dim=0, keepdim=True))  # (1, 3)
```

## Training

- **Encoder:** CLIP ViT-B/16 (ViFi-CLIP), fine-tuned on Kinetics-400, 30 epochs, output dim 512.
- **Classification head:** MLP trained on DAViD video dataset + CDDB images (branch `feat-cddb`).

## Related

- 🛰️ Space: [`aitf-its-tim3-dfk/David`](https://huggingface.co/spaces/aitf-its-tim3-dfk/David)
- 🧪 Training code: [`aitf-its-tim3-dfk/david`](https://github.com/aitf-its-tim3-dfk/david) (branch `feat-cddb`)

## License

Set the appropriate license for these weights (currently `other`). The CLIP
backbone, Kinetics-400, and CDDB carry their own upstream licenses/terms.