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README.md

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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`.

lfg_seg_motion_1.3b.pt

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