ANCHURUS

Lightweight Monocular Depth Estimation

ANCHURUS is an experimental deep learning model for monocular depth estimation, developed by Davitestro.

The project explores computationally efficient depth prediction using a student-teacher learning approach.

Its primary goal is to investigate lightweight neural network architectures capable of generating depth maps from single RGB images while keeping computational requirements relatively low.

Model Details

Property	Value
Model	ANCHURUS v5
Framework	PyTorch
Task	Monocular Depth Estimation
Encoder	MobileNetV2
Architecture	Encoder-Decoder
Input	RGB, 256 × 256
Output	Single-channel depth map
Output range	0–1 (normalized)
Checkpoint	model/ANCHURUS-v5.pth
License	MIT

Architecture

ANCHURUS uses a MobileNetV2 feature extraction backbone followed by a progressive decoder consisting of five upsampling blocks.

The network produces a normalized depth representation from a single RGB image.

       RGB Image
           |
           v
    MobileNetV2 Encoder
           |
           v
      Feature Maps
           |
           v
     Progressive Decoder
      (5 Upsampling Blocks)
           |
           v
     Sigmoid Activation
           |
           v
    Normalized Depth Map

Training Approach

ANCHURUS was developed using a student-teacher learning framework.

MiDaS serves as the teacher model, providing depth-related supervision for the lightweight student architecture.

The training implementation also includes an adaptive learning-rate strategy.

Model Weights

The recommended checkpoint is:

ANCHURUS v5 — Download

The checkpoint contains the model state dictionary.

Intended Applications

Potential research and development applications include:

• Robotics and computer vision.
• Embedded perception systems.
• Resource-constrained inference.
• Monocular depth estimation experiments.
• Real-time visual perception research.

These applications are not claims of validated real-world performance.

Limitations

ANCHURUS is an experimental model.

Its output represents normalized depth values and should not be interpreted as calibrated metric distance without additional calibration.

Prediction quality and generalization depend on training data and environmental conditions.

Independent benchmarking is encouraged.

Source Code

The complete implementation, training scripts and development history are available on GitHub:

Davitestro/ANCHURUS

Author

Davitestro

GitHub: https://github.com/Davitestro

Hugging Face: https://huggingface.co/Testro

License

MIT License.

Copyright (c) 2026 Davit

See the LICENSE file for the complete license text.

Feedback, independent evaluation and research collaboration are welcome.