Upload README.md with huggingface_hub

README.md

@@ -17,32 +17,33 @@ size_categories:
 
 SOCOv1 is a structured object correspondence dataset with rendered object images, per-view keypoint annotations, pair files, and metadata.
 
-The dataset is distributed as an unpacked folder tree on the Hugging Face Hub. Use `huggingface_hub.snapshot_download` to download the full dataset or selected subfolders.
+The dataset is distributed on the Hugging Face Hub as three zip archives plus an unpacked `Metadata/` folder. Download the repository and unzip the archives in place to obtain the full folder tree.
 
-## Folder Structure
+## Repository Layout
+
+```text
+GenIntelLab/SOCO
+  Images.zip                # -> Images/<category>/*.JPEG
+  KeypointAnnotations.zip   # -> KeypointAnnotations/<category>/*.json
+  PairAnnotations.zip       # -> PairAnnotations/{intra,cross,trainsplits/{train,test}}/<category>/*.json
+  Metadata/
+    filename_mapping.json
+    keypoint_taxonomy.json
+  README.md
+```
+
+After unzipping, the dataset tree is:
 
 ```text
 SOCOv1/
-  Images/
-    <category>/
-      *.JPEG
-  KeypointAnnotations/
-    <category>/
-      *.json
+  Images/<category>/*.JPEG
+  KeypointAnnotations/<category>/*.json
   PairAnnotations/
-    intra/
-      <category>/
-        *.json
-    cross/
-      <category>/
-        *.json
+    intra/<category>/*.json
+    cross/<category>/*.json
     trainsplits/
-      train/
-        <category>/
-          *.json
-      test/
-        <category>/
-          *.json
+      train/<category>/*.json
+      test/<category>/*.json
   Metadata/
     filename_mapping.json
     keypoint_taxonomy.json
@@ -50,14 +51,12 @@ SOCOv1/
 
 ## Contents
 
-- `Images/`: rendered object images, organized by category.
-- `KeypointAnnotations/`: per-view keypoint annotations, organized by category.
-- `PairAnnotations/intra/`: intra-category image-pair files.
-- `PairAnnotations/cross/`: cross-category image-pair files.
-- `PairAnnotations/trainsplits/`: train/test split pair files.
-- `Metadata/`: keypoint taxonomy and filename mapping.
+- `Images.zip`: rendered object images, organized by category.
+- `KeypointAnnotations.zip`: per-view keypoint annotations, organized by category.
+- `PairAnnotations.zip`: image-pair files for intra-category (`intra`), cross-category (`cross`), and the predefined train/test splits (`trainsplits`).
+- `Metadata/`: keypoint taxonomy and filename mapping (shipped unzipped).
 
-This release contains 100 categories, 4,000 images, 4,000 keypoint annotation files, and 60,001 pair annotation files.
+This release contains 100 categories, 4,000 images (40 per category), 4,000 keypoint annotation files, and 60,000 pair annotation files (20,000 intra-category, 20,000 cross-category, and a 10,000 / 10,000 intra-category train / test split).
 
 ## Download
 
@@ -67,33 +66,36 @@ Install the Hub client:
 pip install -U huggingface_hub
 ```
 
-Download the full dataset:
-
-```python
-from huggingface_hub import snapshot_download
+Download the repository and unpack the archives:
 
-path = snapshot_download(
-    repo_id="GenIntelLab/SOCO",
-    repo_type="dataset",
-    local_dir="SOCOv1",
-    token=True,  # required while the dataset is private
-)
+```bash
+huggingface-cli download GenIntelLab/SOCO --repo-type dataset --local-dir SOCOv1
+cd SOCOv1 && for z in *.zip; do unzip -q "$z" && rm "$z"; done
 ```
 
-Download only selected folders:
+Equivalently in Python:
 
 ```python
 from huggingface_hub import snapshot_download
 
-path = snapshot_download(
+snapshot_download(
     repo_id="GenIntelLab/SOCO",
     repo_type="dataset",
-    local_dir="SOCOv1_images_and_metadata",
-    allow_patterns=["Images/**", "Metadata/**"],
-    token=True,
+    local_dir="SOCOv1",
 )
+# then unzip Images.zip, KeypointAnnotations.zip, PairAnnotations.zip inside SOCOv1/
 ```
 
+## Using the data
+
+After extraction the folder layout maps directly onto a dataset-root path: point your
+loader at the `SOCOv1/` directory and read images from `Images/<category>/` and pairs from
+the relevant `PairAnnotations/` subfolder. For evaluation, use `PairAnnotations/intra`
+(within-category pairs); for training and evaluating a probe, use the predefined
+`PairAnnotations/trainsplits/train` and `PairAnnotations/trainsplits/test` splits. Each
+pair file references two views and their corresponding keypoints; per-view keypoints are
+also available under `KeypointAnnotations/<category>/`.
+
 ## Citation
 
 Citation information will be added with the public release.