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license: cc-by-nc-4.0
tags:
- autonomous-driving
- depth-estimation
- segmentation
- motion-prediction
- self-supervised
library_name: pytorch
---
# LFG
LFG checkpoint that jointly predicts **dense depth / 3D points, camera pose, per-point confidence, object segmentation (7 classes), and per-pixel motion** from monocular driving video — no human labels.
LFG: Self-Supervised 4D Learning from In-the-Wild Driving Videos — trained on 58M+ unlabeled dashcam driving frames with frozen teachers generating pseudo ground truth on the fly. See the [project page](https://lfg-ai.github.io/) and the [inference repo](https://github.com/Applied-Intuition-Open-Source/LFG).
## Model details
| | |
|---|---|
| Architecture | LFG (autoregressive transformer with future-frame prediction) |
| Parameters | 1.22B (fp32) |
| Input | monocular video, 3 history frames |
| Encoder | DINOv2 |
| Heads | point/depth, camera/pose, confidence, segmentation (7 classes), motion |
## Files
- `lfg_seg_motion_1.3b.pt` — minimal inference checkpoint: `model_state_dict` (inference weights only), `config` (architecture settings), `global_step`. Loads with `torch.load(..., weights_only=True)`.
## Usage
Use the [official inference repo](https://github.com/Applied-Intuition-Open-Source/LFG):
```bash
python infer.py <video-or-image-dir> \
--checkpoint lfg_seg_motion_1.3b.pt \
--output-dir outputs/ \
--save-visualizations
```
Outputs per window: `points`, `local_points`, `camera_poses`, `conf`, `segmentation`, `motion`.
|