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README.md

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-license: agpl-3.0
+license: mit
+tags:
+- monocular-depth-estimation
+- self-supervised
+- autonomous-driving
+- yolo
+- pytorch
+- kitti
+- cityscapes
+library_name: pytorch
+pipeline_tag: depth-estimation
 ---
+
+# FlexDepth: A Flexible Scale-Driven Family for Self-Supervised Monocular Depth Estimation
+
+**Towards Robust Driving Perception: A Flexible Scale-Driven Family for Self-Supervised Monocular Depth Estimation**
+
+[![ECCV 2026](https://img.shields.io/badge/ECCV-2026-4F70F2?style=flat-square)](https://eccv.ecva.net/)
+[![arXiv](https://img.shields.io/badge/arXiv-2607.00736-b31b1b?style=flat-square)](https://arxiv.org/abs/2607.00736)
+[![Project Page](https://img.shields.io/badge/Project-Page-4F70F2?style=flat-square)](https://startnew.github.io/projects/flexdepth/)
+[![Code](https://img.shields.io/badge/GitHub-Code-181717?style=flat-square)](https://github.com/StarNew/FlexDepth)
+
+## Overview
+
+FlexDepth is a family of self-supervised monocular depth estimation models designed for robust driving perception. It introduces a **Scale-Driven Decoder (SDD)** with adaptive component selection, enabling a single architecture to span five model scales — from ultra-lightweight (1.5M params, 0.7 GFLOPs) to high-accuracy (32.3M params, 24.6 GFLOPs).
+
+### Key Features
+
+- **Five model scales**: Nano (N), Small (S), Medium (M), Large (L), X-Large (X)
+- **Scale-Driven Decoder (SDD)**: Adaptive component selection based on model scale
+- **High-Efficiency Bottleneck (HEB)**: For small models (N, S) — maximizes efficiency
+- **High-Performance Bottleneck (HPB)**: For large models (M, L, X) — maximizes accuracy
+- **Dynamic upsampling**: Sharper depth boundaries via learned upsampling
+- **Two-stage static-dynamic decoupled training**: Handles dynamic scenes in driving scenarios
+- **YOLO11-based encoder**: Leverages rich visual representations from YOLO segmentation pretraining
+
+## Model Zoo
+
+### KITTI-trained Models
+
+| Model | Params | GFLOPs | Abs Rel ↓ | Sq Rel ↓ | RMSE ↓ | RMSE log ↓ | δ<1.25 ↑ | δ<1.25² ↑ | δ<1.25³ ↑ |
+|-------|--------|--------|-----------|----------|--------|------------|----------|-----------|-----------|
+| Flex-Nano | 1.5M | 0.7 | 0.110 | 0.794 | 4.678 | 0.184 | 0.878 | 0.961 | 0.983 |
+| Flex-Small | 6.1M | 2.8 | 0.104 | 0.713 | 4.458 | 0.179 | 0.890 | 0.964 | 0.983 |
+| Flex-Medium | 12.7M | 10.0 | 0.096 | 0.639 | 4.253 | 0.172 | 0.903 | 0.968 | 0.985 |
+| Flex-Large | 15.2M | 11.5 | 0.095 | 0.642 | 4.199 | 0.171 | 0.906 | 0.968 | 0.984 |
+| Flex-X-Large | 32.3M | 24.6 | **0.093** | **0.605** | **4.114** | **0.167** | **0.910** | **0.969** | **0.985** |
+
+### Cityscapes-trained Models
+
+| Model | Params | GFLOPs | Abs Rel ↓ | Sq Rel ↓ | RMSE ↓ | RMSE log ↓ | δ<1.25 ↑ | δ<1.25² ↑ | δ<1.25³ ↑ |
+|-------|--------|--------|-----------|----------|--------|------------|----------|-----------|-----------|
+| Flex-Nano | 1.5M | 0.6 | 0.107 | 1.261 | 6.133 | 0.164 | 0.893 | 0.971 | 0.989 |
+| Flex-Small | 6.1M | 2.2 | 0.100 | 1.078 | 5.813 | 0.153 | 0.904 | 0.975 | 0.991 |
+| Flex-Medium | 12.7M | 8.0 | 0.089 | 0.885 | 5.358 | 0.143 | 0.917 | 0.979 | 0.993 |
+| Flex-Large | 15.2M | 9.2 | 0.087 | 0.911 | 5.310 | 0.139 | 0.924 | 0.981 | 0.993 |
+| Flex-X-Large | 32.3M | 19.7 | **0.086** | **0.877** | **5.268** | **0.137** | **0.926** | **0.982** | **0.993** |
+
+### Efficiency
+
+| Model | FPS (Snapdragon 8 Elite) | FPS (RTX 2080 Ti) |
+|-------|--------------------------|---------------------|
+| Flex-Nano | 37.6 | 180+ |
+| Flex-Small | 18.6 | 120+ |
+| Flex-Medium | 5.8 | 60+ |
+| Flex-Large | 5.2 | 55+ |
+| Flex-X-Large | 3.0 | 40+ |
+
+## Model Files
+
+Each model consists of two weight files:
+
+```
+├── kitti/
+│   ├── flex_n/
+│   │   ├── encoder.pth    # YOLO11-based encoder weights
+│   │   └── depth.pth      # Scale-Driven Decoder weights
+│   ├── flex_s/
+│   ├── flex_m/
+│   ├── flex_l/
+│   └── flex_x/
+└── cs/
+    ├── flex_n/
+    ├── flex_s/
+    ├── flex_m/
+    ├── flex_l/
+    └── flex_x/
+```
+
+## Usage
+
+### Installation
+
+```bash
+conda create -n flexdepth python=3.10
+conda activate flexdepth
+
+pip install torch==2.3.1 torchvision==0.18.1 torchaudio==2.3.1 --index-url https://download.pytorch.org/whl/cu118
+pip install -r requirements.txt
+```
+
+### Download Weights
+
+```python
+from huggingface_hub import snapshot_download
+
+# Download all models
+snapshot_download(repo_id="StarNew/flexdepth", local_dir="./models")
+
+# Or download a specific model
+from huggingface_hub import hf_hub_download
+
+hf_hub_download(
+    repo_id="StarNew/flexdepth",
+    filename="kitti/flex_n/encoder.pth",
+    local_dir="./models"
+)
+hf_hub_download(
+    repo_id="StarNew/flexdepth",
+    filename="kitti/flex_n/depth.pth",
+    local_dir="./models"
+)
+```
+
+### Evaluation
+
+```bash
+# Flex-Nano on KITTI
+python evaluate_depth.py --png --eval_mono --scale 4 \
+    --encoder_model_type yolo11n-seg --decoder_model_type flexn \
+    --load_weights_folder ./models/kitti/flex_n \
+    --data_path <kitti_data_path> --split_path <splits_path>
+
+# Flex-X-Large on KITTI
+python evaluate_depth.py --png --eval_mono --scale 4 \
+    --encoder_model_type yolo11x-seg --decoder_model_type flexx \
+    --load_weights_folder ./models/kitti/flex_x \
+    --data_path <kitti_data_path> --split_path <splits_path>
+```
+
+### ONNX Export
+
+```bash
+python export_onnx.py --encoder_model_type yolo11n-seg --decoder_model_type flexn \
+    --load_weights_folder ./models/kitti/flex_n --scales 4 --export_name flex-n
+```
+
+## Comparison with Depth Anything V2
+
+On the KITTI Eigen benchmark with dense ground truth and least-squares alignment:
+
+| Method | Type | Params | GFLOPs | Resolution | Abs Rel ↓ | δ<1.25 ↑ |
+|--------|------|--------|--------|------------|-----------|----------|
+| DA2 (ViT-L) | Zero-Shot | 335M | 1947 | 1722×518 | 0.070 | **0.956** |
+| DA2 (ViT-S) | Zero-Shot | 25M | 137 | 1722×518 | 0.077 | 0.944 |
+| DA2 (ViT-L) | Zero-Shot | 335M | 276 | 644×196 | 0.092 | 0.915 |
+| DA2 (ViT-S) | Zero-Shot | 25M | 19 | 644×196 | 0.110 | 0.881 |
+| **Flex-X-Large** | Self-Supervised | 32M | 25 | 640×192 | **0.063** | 0.952 |
+
+FlexDepth achieves comparable or better accuracy than Depth Anything V2 with **~13× fewer parameters** and **~78× fewer GFLOPs** at similar resolution.
+
+## Citation
+
+```bibtex
+@misc{zhu2026robustdrivingperceptionflexible,
+  title={Towards Robust Driving Perception: A Flexible Scale-Driven Family for Self-Supervised Monocular Depth Estimation},
+  author={Zhaowen Zhu and Li Zhang and Yujie Chen and Tian Zhang and Yingjie Wang and Mingxia Zhan},
+  year={2026},
+  eprint={2607.00736},
+  archivePrefix={arXiv},
+  primaryClass={cs.CV},
+  url={https://arxiv.org/abs/2607.00736}
+}
+```
+
+## Acknowledgment
+
+This work is supported by the National Natural Science Foundation of China under Grant 62332016.
+
+Our code is built upon [Monodepth2](https://github.com/nianticlabs/monodepth2), [Manydepth](https://github.com/nianticlabs/manydepth), and [Ultralytics](https://github.com/ultralytics/ultralytics).