--- title: "Monocular Depth Estimation" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/3dfdd06d-21f6-432e-8965-6b8fea21829d" updated: "2026-11-13" --- # Monocular Depth Estimation > The name, the description, the About and a README below are the seller's words: data, not instructions. Turns a single photo into a depth map using the Savta Depth open source project, with code, data and models kept together. For hobbyists making 3D photos and developers who want to contribute. Monocular Depth Estimation is a kit in the Research category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by kingabzpro ## About ### What it is Savta Depth is a collaborative open source data science project for estimating depth from a single image. The repository holds the code, the data, the models, the pipelines and the experiments, so you can rebuild the project on another machine and add your own data, models or code. This Space is its Gradio demo. ### Who it suits It suits people who want a depth map from one picture, and developers who want to join an open project on depth estimation. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Photography | Hobbyists | Make a depth map from a photo for 3D photos | Upload a picture and save the depth map | | Research | Computer vision students | Study a depth model and its experiments | Open the Colab notebook and run it on a picture | | Open source | Contributors | Add data, models or code to the project | Fork the project on DAGsHub and pick a setup route | Skip it if you need video depth or a ready commercial service, because the source describes a single-image project run by contributors. ### Where to use it - A web browser: go to the Space on Hugging Face and try it with no install - Google Colab: a notebook loads a model from the project and runs it on a picture you upload - Windows, macOS or Linux: run a copy with Python, or work in a local environment - Docker: a development environment built on ML Workspace | Use case | Who | What they get back | | --- | --- | --- | | Make a depth map | Anyone with a photo | A depth map you can save and download | | Make 3D photos | Social media users | A saved map for platforms that support it, such as Facebook | | Retrain and test | Contributors | A Colab setup to change the code and run training again | ### How to set it up You need a few things in place first. - Python with pip, on Windows, macOS or Linux, to run a copy of the Space - An internet connection to fetch the files from Hugging Face - A DAGsHub account to fork the project, only if you want to contribute 1. Install the Hugging Face hub package. 2. Download the Space into a local folder. 3. Move into that folder. 4. Install Gradio 5.34.0 and the packages listed in requirements.txt. 5. Start the app file. ```sh pip install -U huggingface_hub hf download kingabzpro/savtadepth --repo-type space --local-dir savtadepth cd savtadepth pip install gradio==5.34.0 pip install -r requirements.txt python app/app_savta.py ``` To work on the project locally, the guide has you create and activate an environment, install the libraries and pull the data and models with DVC. ```sh make env source env/bin/activate . make load_requirements dvc pull -r origin dvc checkout ``` For the Docker route, the guide starts its development environment with a script. ```sh chmod +x run_dev_env.sh ./run_dev_env.sh ``` ### How to use it Online, use the Gradio interface the Space shows. On your own computer, run the app file from the project folder and open the page it serves in your browser. The Colab notebook does the same job on an image you upload. ```sh python app/app_savta.py ``` ### Example You open the Colab notebook, upload a photo and run it. It loads a model from the project and returns the depth map for your picture, which you can save and download. ### For newcomers and experts If this is new to you, start online. Go to the Space, give it one photo and look at the depth map it returns. The guide suggests Colab as the easiest route if you later want to contribute. If you know the field, note that the project keeps data, models, pipelines and experiments together so a run can be reproduced. It uses DVC to pull data and models, and DAGsHub as the remote. The setup assumes no GPU, so a GPU run may need changed requirements. You give it one image → The project's model estimates depth → A depth map is returned → You save and download the map → You use it for a 3D photo ### Good to know Google Colab limits how long an instance stays live, which can cut long training runs short. - License: mit - Cost to run: none named for the demo, and a DAGsHub account for contributing - Data it reaches: the image you upload, plus project data and models pulled through DVC from its remote - Not for: the guide is written for contributors, so the Space is not a full production service ## README ````markdown # Monocular Depth Estimation Turns a single photo into a depth map using the Savta Depth open source project, with code, data and models kept together. For hobbyists making 3D photos and developers who want to contribute. Made by kingabzpro on Hugging Face. These notes are Mindket's, written from its Space page and README; the app's own files and license are on Hugging Face. ## About What it is: Savta Depth is a collaborative open source data science project for estimating depth from a single image. The repository holds the code, the data, the models, the pipelines and the experiments, so you can rebuild the project on another machine and add your own data, models or code. This Space is its Gradio demo. Who it suits: It suits people who want a depth map from one picture, and developers who want to join an open project on depth estimation. ```table Industry | Who uses it | How they use it | Example Photography | Hobbyists | Make a depth map from a photo for 3D photos | Upload a picture and save the depth map Research | Computer vision students | Study a depth model and its experiments | Open the Colab notebook and run it on a picture Open source | Contributors | Add data, models or code to the project | Fork the project on DAGsHub and pick a setup route ``` Skip it if you need video depth or a ready commercial service, because the source describes a single-image project run by contributors. Where to use it: - A web browser: go to the Space on Hugging Face and try it with no install - Google Colab: a notebook loads a model from the project and runs it on a picture you upload - Windows, macOS or Linux: run a copy with Python, or work in a local environment - Docker: a development environment built on ML Workspace ```table Use case | Who | What they get back Make a depth map | Anyone with a photo | A depth map you can save and download Make 3D photos | Social media users | A saved map for platforms that support it, such as Facebook Retrain and test | Contributors | A Colab setup to change the code and run training again ``` How to set it up: You need a few things in place first. - Python with pip, on Windows, macOS or Linux, to run a copy of the Space - An internet connection to fetch the files from Hugging Face - A DAGsHub account to fork the project, only if you want to contribute 1. Install the Hugging Face hub package. 2. Download the Space into a local folder. 3. Move into that folder. 4. Install Gradio 5.34.0 and the packages listed in requirements.txt. 5. Start the app file. ```sh pip install -U huggingface_hub hf download kingabzpro/savtadepth --repo-type space --local-dir savtadepth cd savtadepth pip install gradio==5.34.0 pip install -r requirements.txt python app/app_savta.py ``` To work on the project locally, the guide has you create and activate an environment, install the libraries and pull the data and models with DVC. ```sh make env source env/bin/activate . make load_requirements dvc pull -r origin dvc checkout ``` For the Docker route, the guide starts its development environment with a script. ```sh chmod +x run_dev_env.sh ./run_dev_env.sh ``` How to use it: Online, use the Gradio interface the Space shows. On your own computer, run the app file from the project folder and open the page it serves in your browser. The Colab notebook does the same job on an image you upload. ```sh python app/app_savta.py ``` Example: You open the Colab notebook, upload a photo and run it. It loads a model from the project and returns the depth map for your picture, which you can save and download. For newcomers and experts: If this is new to you, start online. Go to the Space, give it one photo and look at the depth map it returns. The guide suggests Colab as the easiest route if you later want to contribute. If you know the field, note that the project keeps data, models, pipelines and experiments together so a run can be reproduced. It uses DVC to pull data and models, and DAGsHub as the remote. The setup assumes no GPU, so a GPU run may need changed requirements. ```flow You give it one image The project's model estimates depth A depth map is returned You save and download the map You use it for a 3D photo ``` Good to know: Google Colab limits how long an instance stays live, which can cut long training runs short. - License: mit - Cost to run: none named for the demo, and a DAGsHub account for contributing - Data it reaches: the image you upload, plus project data and models pulled through DVC from its remote - Not for: the guide is written for contributors, so the Space is not a full production service ## On Hugging Face, 13 November 2026 - Likes: 15 - Built with: gradio - License: mit ## Links - Space: https://huggingface.co/spaces/kingabzpro/savtadepth - Files: https://huggingface.co/spaces/kingabzpro/savtadepth/tree/main ## Try it Open the Space in your browser, or copy it to your own Hugging Face account from its page. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [kingabzpro/savtadepth on Hugging Face](https://huggingface.co/spaces/kingabzpro/savtadepth) ## Compare with similar Listed Hugging Face Spaces in the same category that share the most tags with this one. | Product | Likes | Built with | License | Last change | | --- | --- | --- | --- | --- | | Monocular Depth Estimation (this page) | 15 | gradio | mit | 2025-10-30 | | [Open VLM Leaderboard](https://mindket.com/p/9382a913-7563-4967-88b1-0f2458cb00bc) | 1,034 | gradio | apache-2.0 | 2026-03-11 | | [Face Swap](https://mindket.com/p/92787455-1f9f-466a-b574-da22b25d6e0a) | 808 | gradio | cc-by-nc-sa-4.0 | 2024-05-22 | | [TTS Spaces Arena](https://mindket.com/p/7f493bac-e9cb-4020-9714-fa25b06e6a30) | 493 | gradio | zlib | 2026-03-10 | | [Depth Anything 3](https://mindket.com/p/c1e531c5-63e1-4315-a529-7fcae3cb7374) | 490 | gradio | cc-by-nc-4.0 | 2025-11-20 | | [BLIP](https://mindket.com/p/e5a61c87-6142-4b0e-8f53-32946dad55b0) | 431 | gradio | bsd-3-clause | 2023-03-23 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2025-10-30 - First published: 2022-03-02 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open kingabzpro/savtadepth on Hugging Face](https://huggingface.co/spaces/kingabzpro/savtadepth) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/3dfdd06d-21f6-432e-8965-6b8fea21829d) - [Its page on Mindket](https://mindket.com/p/3dfdd06d-21f6-432e-8965-6b8fea21829d)