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README.md

@@ -81,12 +81,35 @@ This dataset is designed for training and evaluating **open-world object detecti
 
 | Metric | Value |
 |--------|-------|
-| Total Images | 8,001 |
+| Labeled Images | 8,001 |
 | Total Annotations | 344,079 |
-| Total Categories | 1,106 |
+| Total Categories | 1,038 |
 | Avg. Annotations per Image | ~43 |
+| Unlabeled Images | 4,000+ |
 | Annotation Format | COCO-style bounding boxes |
 
+### Unlabeled Images
+
+This dataset also includes **4,000+ unlabeled images** in `unlabeled_images.zip`. These images:
+- Have been deduplicated using DINOv3 embeddings (similarity threshold 0.95)
+- Do not overlap with the labeled training images
+- Can be used for semi-supervised learning, self-training, or pseudo-labeling
+
+```python
+# Download and extract unlabeled images
+from huggingface_hub import hf_hub_download
+import zipfile
+
+zip_path = hf_hub_download(
+    repo_id="shubh303/open-world-dense-object-detection",
+    filename="unlabeled_images.zip",
+    repo_type="dataset"
+)
+
+with zipfile.ZipFile(zip_path, 'r') as zip_ref:
+    zip_ref.extractall("./unlabeled_images")
+```
+
 ### Example Categories
 
 The dataset includes diverse categories such as:
@@ -218,4 +241,4 @@ If you use this dataset in your research, please cite:
 
 ## License
 
-This dataset is released under the MIT License.
+This dataset is released under the MIT License.