Update README.md

README.md

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 ---
 license: mit
+library_name: onnx
+tags:
+- depth-estimation
+- dpt
+- midas
+- onnx
+base_model: Intel/dpt-large
+pipeline_tag: depth-estimation
+language:
+- en
 ---
+
+# DPT-Large — Monocular Depth Estimation (ONNX)
+
+ONNX export of [Intel/dpt-large](https://huggingface.co/Intel/dpt-large) — the Dense Prediction Transformer for monocular depth. ~330M params, originally published as part of the [MiDaS](https://github.com/isl-org/MiDaS) project at Intel Intelligent Systems Lab.
+
+Re-hosted under Heliosoph for distribution stability — Intel's published checkpoint is the authoritative source.
+
+Credit: Intel ISL (DPT / MiDaS team — Ranftl et al.).
+
+## What this repo contains
+
+```
+dpt_large_384.onnx     # ~1.3 GB
+```
+
+A single ONNX file. No tokenizer, no preprocessor config — preprocessing is fixed by convention.
+
+## Input/output shape
+
+| | Spec |
+|---|---|
+| Input name | `pixel_values` (or `image` — verify in Netron) |
+| Input shape | `[1, 3, 384, 384]` |
+| Input dtype | float32 |
+| Preprocessing | RGB, divide by 255, normalize by `mean=[0.5, 0.5, 0.5]` / `std=[0.5, 0.5, 0.5]` |
+| Output shape | `[1, 384, 384]` |
+| Output meaning | Relative depth — **not** metric. Lower values = farther; higher values = closer. Linearly map to your visualization range. |
+
+## How to use
+
+```python
+import onnxruntime as ort
+import numpy as np
+from PIL import Image
+
+sess = ort.InferenceSession("dpt_large_384.onnx")
+
+# Resize input image to 384×384, normalize, NCHW
+img = Image.open("photo.jpg").convert("RGB").resize((384, 384))
+arr = (np.asarray(img, dtype=np.float32) / 255.0 - 0.5) / 0.5  # HWC, [-1,1]
+arr = arr.transpose(2, 0, 1)[None, ...]                         # 1x3x384x384
+
+depth = sess.run(None, {sess.get_inputs()[0].name: arr})[0][0]  # 384x384
+```
+
+For metric depth, pair with a calibration scheme — DPT-Large is trained for relative depth and will not give you "this object is 1.7m away" without further work.
+
+## When to pick DPT-Large
+
+- **Quality matters more than speed**: ~330M params, slowest variant in the MiDaS family.
+- **Single static image, not video**: no temporal smoothing built in.
+- **GPU available**: CPU inference is workable but slow (~1–2 sec on consumer CPU).
+
+For real-time or edge use, prefer `dpt-hybrid` or `midas-small` — not in this repo, but available as separate uploads upstream.
+
+## License
+
+**Apache 2.0** — same as upstream. `LICENSE` file included.