| license: mit | |
| pipeline_tag: depth-estimation | |
| tags: | |
| - monocular-depth | |
| - relative-depth | |
| - zero-shot | |
| - lightweight | |
| # ZipDepth (base) — pretrained checkpoints | |
| Mirror of the official ZipDepth release checkpoints for use with | |
| `hf_hub_download` (e.g. the `monopriors` package in rerun-io/examples-monorepo). | |
| **ZipDepth: Bringing Lightweight Zero-Shot Monocular Depth Anywhere, on Any Device** | |
| Tosi, Bartolomei, Poggi, Mattoccia — ECCV 2026. | |
| Paper: https://arxiv.org/abs/2607.08771 · Code: https://github.com/fabiotosi92/ZipDepth | |
| (pixi fork: https://github.com/pablovela5620/ZipDepth) | |
| | File | Upsampling | Params (fused) | Use | | |
| |------|-----------|----------------|-----| | |
| | `zipdepth_base.pth` | Convex (`torch.nn.Unfold`) | ~6.1 M | GPU / server (`upsample_unfold=True`) | | |
| | `zipdepth_base_npu.pth` | Convex, unfold-free | ~6.1 M | NPU / mobile / ONNX export (`upsample_unfold=False`) | | |
| Both share identical encoder/decoder weights; only the upsampling head differs. | |
| Output is relative inverse depth (disparity up to scale). Trained at 384×384. | |
| ```python | |
| from huggingface_hub import hf_hub_download | |
| ckpt = hf_hub_download("pablovela5620/zipdepth", "zipdepth_base.pth") | |
| ``` | |
| License: MIT (see `LICENSE`, © the ZipDepth authors). | |