{"id":15131217,"url":"https://github.com/isl-org/dpt","last_synced_at":"2025-08-28T21:31:57.883Z","repository":{"id":40334171,"uuid":"350409920","full_name":"isl-org/DPT","owner":"isl-org","description":"Dense Prediction Transformers","archived":true,"fork":false,"pushed_at":"2024-12-18T16:24:13.000Z","size":437,"stargazers_count":2054,"open_issues_count":38,"forks_count":261,"subscribers_count":43,"default_branch":"main","last_synced_at":"2024-12-26T01:02:35.715Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/isl-org.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2021-03-22T16:17:15.000Z","updated_at":"2024-12-23T06:41:52.000Z","dependencies_parsed_at":"2023-01-28T16:16:07.139Z","dependency_job_id":"1bc3270c-4ac9-4ada-8808-3f7dd9b9fb05","html_url":"https://github.com/isl-org/DPT","commit_stats":{"total_commits":104,"total_committers":10,"mean_commits":10.4,"dds":0.2692307692307693,"last_synced_commit":"f43ef9e08d70a752195028a51be5e1aff227b913"},"previous_names":["intel-isl/dpt"],"tags_count":1,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/isl-org%2FDPT","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/isl-org%2FDPT/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/isl-org%2FDPT/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/isl-org%2FDPT/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/isl-org","download_url":"https://codeload.github.com/isl-org/DPT/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":231299745,"owners_count":18354983,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-09-26T03:24:33.517Z","updated_at":"2024-12-26T02:04:30.326Z","avatar_url":"https://github.com/isl-org.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# PROJECT NOT UNDER ACTIVE MANAGEMENT\nThis project will no longer be maintained by Intel.  \nIntel has ceased development and contributions including, but not limited to, maintenance, bug fixes, new releases, or updates, to this project.  \nIntel no longer accepts patches to this project.  \nIf you have an ongoing need to use this project, are interested in independently developing it, or would like to maintain patches for the open source software community, please create your own fork of this project.  \n\n## Vision Transformers for Dense Prediction\n\nThis repository contains code and models for our [paper](https://arxiv.org/abs/2103.13413):\n\n\u003e Vision Transformers for Dense Prediction  \n\u003e René Ranftl, Alexey Bochkovskiy, Vladlen Koltun\n\n\n### Changelog \n* [March 2021] Initial release of inference code and models\n\n### Setup \n\n1) Download the model weights and place them in the `weights` folder:\n\n\nMonodepth:\n- [dpt_hybrid-midas-501f0c75.pt](https://github.com/intel-isl/DPT/releases/download/1_0/dpt_hybrid-midas-501f0c75.pt), [Mirror](https://drive.google.com/file/d/1dgcJEYYw1F8qirXhZxgNK8dWWz_8gZBD/view?usp=sharing)\n- [dpt_large-midas-2f21e586.pt](https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt), [Mirror](https://drive.google.com/file/d/1vnuhoMc6caF-buQQ4hK0CeiMk9SjwB-G/view?usp=sharing)\n\nSegmentation:\n - [dpt_hybrid-ade20k-53898607.pt](https://github.com/intel-isl/DPT/releases/download/1_0/dpt_hybrid-ade20k-53898607.pt), [Mirror](https://drive.google.com/file/d/1zKIAMbltJ3kpGLMh6wjsq65_k5XQ7_9m/view?usp=sharing)\n - [dpt_large-ade20k-b12dca68.pt](https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-ade20k-b12dca68.pt), [Mirror](https://drive.google.com/file/d/1foDpUM7CdS8Zl6GPdkrJaAOjskb7hHe-/view?usp=sharing)\n  \n2) Set up dependencies: \n\n    ```shell\n    pip install -r requirements.txt\n    ```\n\n   The code was tested with Python 3.7, PyTorch 1.8.0, OpenCV 4.5.1, and timm 0.4.5\n\n### Usage \n\n1) Place one or more input images in the folder `input`.\n\n2) Run a monocular depth estimation model:\n\n    ```shell\n    python run_monodepth.py\n    ```\n\n    Or run a semantic segmentation model:\n\n    ```shell\n    python run_segmentation.py\n    ```\n\n3) The results are written to the folder `output_monodepth` and `output_semseg`, respectively.\n\nUse the flag `-t` to switch between different models. Possible options are `dpt_hybrid` (default) and `dpt_large`.\n\n\n**Additional models:**\n\n- Monodepth finetuned on KITTI: [dpt_hybrid_kitti-cb926ef4.pt](https://github.com/intel-isl/DPT/releases/download/1_0/dpt_hybrid_kitti-cb926ef4.pt) [Mirror](https://drive.google.com/file/d/1-oJpORoJEdxj4LTV-Pc17iB-smp-khcX/view?usp=sharing)\n- Monodepth finetuned on NYUv2: [dpt_hybrid_nyu-2ce69ec7.pt](https://github.com/intel-isl/DPT/releases/download/1_0/dpt_hybrid_nyu-2ce69ec7.pt) [Mirror](https\\://drive.google.com/file/d/1NjiFw1Z9lUAfTPZu4uQ9gourVwvmd58O/view?usp=sharing)\n\nRun with \n\n```shell\npython run_monodepth -t [dpt_hybrid_kitti|dpt_hybrid_nyu] \n```\n\n### Evaluation\n\nHints on how to evaluate monodepth models can be found here: https://github.com/intel-isl/DPT/blob/main/EVALUATION.md\n\n\n### Citation\n\nPlease cite our papers if you use this code or any of the models. \n```\n@article{Ranftl2021,\n\tauthor    = {Ren\\'{e} Ranftl and Alexey Bochkovskiy and Vladlen Koltun},\n\ttitle     = {Vision Transformers for Dense Prediction},\n\tjournal   = {ArXiv preprint},\n\tyear      = {2021},\n}\n```\n\n```\n@article{Ranftl2020,\n\tauthor    = {Ren\\'{e} Ranftl and Katrin Lasinger and David Hafner and Konrad Schindler and Vladlen Koltun},\n\ttitle     = {Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer},\n\tjournal   = {IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},\n\tyear      = {2020},\n}\n```\n\n### Acknowledgements\n\nOur work builds on and uses code from [timm](https://github.com/rwightman/pytorch-image-models) and [PyTorch-Encoding](https://github.com/zhanghang1989/PyTorch-Encoding). We'd like to thank the authors for making these libraries available.\n\n### License \n\nMIT License \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fisl-org%2Fdpt","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fisl-org%2Fdpt","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fisl-org%2Fdpt/lists"}