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README.md

@@ -21,12 +21,11 @@ WildDet3D-Data consists of 3D bounding box annotations for in-the-wild images fr
 
 | Split | Description | Annotation Source | Images | Annotations | Categories |
 |-------|-------------|-------------------|--------|-------------|------------|
-| **Val** | Validation set | Human | 2,470 | 9,256 | 785 |
-| **Test** | Test set | Human | 2,433 | 5,596 | 633 |
 | **Train (Human)** | Human-reviewed annotations only | Human | 102,979 | 229,934 | 11,879 |
 | **Train (Essential)** | Human + VLM-qualified small objects | Human + VLM | 102,979 | 412,711 | 12,064 |
 | **Train (Synthetic)** | VLM auto-selected annotations | VLM | 896,004 | 3,483,292 | 11,896 |
-| **Total** | | | 1,003,886 | 3,910,855 | 13,499 |
+
+For val/test benchmarks, see [WildDet3D-Bench](https://huggingface.co/datasets/allenai/WildDet3D-Bench).
 
 ## Directory Structure
 
@@ -36,8 +35,6 @@ After downloading and extracting, the dataset should be organized as:
 WildDet3D-Data/
 ├── README.md
 ├── annotations/
-│   ├── InTheWild_v3_val.json                    # Val
-│   ├── InTheWild_v3_test.json                   # Test
 │   ├── InTheWild_v3_train_human_only.json       # Train (Human) — COCO, LVIS, Obj365
 │   ├── InTheWild_v3_train_human.json            # Train (Essential) — COCO, LVIS, Obj365
 │   ├── InTheWild_v3_train_synthetic.json        # Train (Synthetic) — COCO, LVIS, Obj365
@@ -50,9 +47,7 @@ WildDet3D-Data/
 ├── camera/{split}/                              # Camera parameters (extract from .tar.gz)
 │   └── {source}_{formatted_id}.json            # Camera intrinsics (K)
 └── images/                                      # Downloaded separately (see Step 2)
-    ├── coco_val/
     ├── coco_train/
-    ├── obj365_val/
     ├── obj365_train/
     └── v3det_train/
 ```
@@ -76,13 +71,14 @@ img = data["images"][0]
 source = img["file_path"].split("/")[1]   # e.g., "coco_train"
 fid = img["formatted_id"]                 # e.g., "000000262686"
 
-depth = np.load(f"depth/{split}/{source}_{fid}.npz")["depth"]  # float32, (H, W)
+depth_mm = np.load(f"depth/{split}/{source}_{fid}.npz")["depth"]  # float32, (H, W), in mm
+depth_m = depth_mm / 1000.0  # convert to meters
 camera = json.load(open(f"camera/{split}/{source}_{fid}.json"))
 ```
 
 ### Depth Format
 
-Each `.npz` file contains a single key `"depth"` with a float32 2D array at original image resolution (meters).
+Each `.npz` file contains a single key `"depth"` with a float32 2D array at original image resolution. **Values are in millimeters (mm).** To convert to meters: `depth_m = depth_mm / 1000.0`.
 
 ### Camera Format
 
@@ -118,10 +114,7 @@ huggingface-cli download weikaih/WildDet3D-Data --repo-type dataset --local-dir
 Depth maps are provided as compressed archives. Large splits are split into multiple parts.
 
 ```bash
-# Val and Test (small, single file each)
 mkdir -p depth && cd depth
-tar xzf ../packed/depth_val.tar.gz
-tar xzf ../packed/depth_test.tar.gz
 
 # Train Human (2 parts)
 tar xzf ../packed/depth_train_human_part000.tar.gz
@@ -157,25 +150,14 @@ Images must be downloaded from their original sources and organized into the fol
 
 ```
 images/
-├── coco_val/          # COCO val2017
 ├── coco_train/        # COCO train2017 (includes LVIS images)
-├── obj365_val/        # Objects365 validation
 ├── obj365_train/      # Objects365 training
 └── v3det_train/       # V3Det training
 ```
 
-### COCO (val2017 + train2017)
-
-Used by: Val, Test, Train (all splits)
+### COCO train2017
 
 ```bash
-# COCO val2017 — used by Val and Test
-wget http://images.cocodataset.org/zips/val2017.zip
-unzip val2017.zip
-mkdir -p images/coco_val
-mv val2017/* images/coco_val/
-
-# COCO train2017 — used by Val/Test for LVIS images, and all Train splits
 wget http://images.cocodataset.org/zips/train2017.zip
 unzip train2017.zip
 mkdir -p images/coco_train
@@ -184,13 +166,8 @@ mv train2017/* images/coco_train/
 
 ### Objects365
 
-Used by: Val, Test (val split), Train (train split)
-
 ```bash
 # Objects365 — download from https://www.objects365.org/
-mkdir -p images/obj365_val
-# Images should be named: obj365_val_000000XXXXXX.jpg
-
 mkdir -p images/obj365_train
 # Images should be named: obj365_train_000000XXXXXX.jpg
 ```
@@ -206,15 +183,11 @@ mkdir -p images/v3det_train
 # e.g., images/v3det_train/Q100507578/28_284_50119550013_7d06ded882_c.jpg
 ```
 
-| Source | Directory | Used By |
-|--------|-----------|---------|
-| COCO val2017 | `images/coco_val/` | Val, Test |
-| COCO train2017 | `images/coco_train/` | Val, Test, Train |
-| Objects365 val | `images/obj365_val/` | Val, Test |
-| Objects365 train | `images/obj365_train/` | Train |
-| V3Det train | `images/v3det_train/` | Train (V3Det) |
-
-**For evaluation only** (Val + Test): you only need COCO val2017, COCO train2017, and Objects365 val.
+| Source | Directory |
+|--------|-----------|
+| COCO train2017 | `images/coco_train/` |
+| Objects365 train | `images/obj365_train/` |
+| V3Det train | `images/v3det_train/` |
 
 ## Annotation Format (COCO3D)
 
@@ -275,7 +248,6 @@ for ann in data["annotations"]:
 
 | Use Case | Annotation Files |
 |----------|-----------------|
-| Evaluation | `InTheWild_v3_val.json`, `InTheWild_v3_test.json` |
 | Train (Human only) | `InTheWild_v3_train_human_only.json` + `InTheWild_v3_v3det_human_only.json` |
 | Train (Essential) | `InTheWild_v3_train_human.json` + `InTheWild_v3_v3det_human.json` |
 | Train (Synthetic) | `InTheWild_v3_train_synthetic.json` + `InTheWild_v3_v3det_synthetic.json` |
@@ -283,6 +255,4 @@ for ann in data["annotations"]:
 
 ## License
 
-CC BY 4.0
-
-This dataset is licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). It is intended for research and educational use in accordance with Ai2's [Responsible Use Guidelines](https://allenai.org/responsible-use).
+- **Annotations**: CC BY 4.0