Datasets:

inanxr
/

ProsopoLarge

@@ -1,75 +1,76 @@
----
-dataset_info:
-  features:
-  - name: image
-    dtype: image
-  - name: label
-    dtype: class_label
-  splits:
-  - name: train
-    num_bytes: 29320000000
-    num_examples: 5179510
-  download_size: 26157852001
-  dataset_size: 29320000000
-license: apache-2.0
-task_categories:
-- image-classification
-- face-recognition
-tags:
-- face-recognition
-- ms1mv3
-- prosopo
-pretty_name: Prosopo Large Dataset (MS1MV3)
-size_categories:
-- 1M<n<10M
----
-# 🧪 Prosopo Large Dataset (MS1MV3)
-> **5.1 Million high-quality aligned face images for state-of-the-art model training.**
-This dataset serves as the **core training engine** for the **Prosopo** face recognition system. It contains the **MS1MV3** (MS-Celeb-1M Cleaned) dataset, pre-aligned and packed into the high-performance MXNet RecordIO format.
-## 📊 Dataset Statistics
-| Metric | Value |
-| :--- | :--- |
-| **Identities** | 93,431 |
-| **Total Images** | 5,179,510 |
-| **Image Size** | 112 x 112 px |
-| **Alignment** | RetinaFace (5-point landmark) |
-| **Format** | MXNet RecordIO (Packed Binary) |
-| **Total Size** | ~28 GB (Unpacked) |
-## 📁 Content Structure
-The dataset is provided as a **ZIP archive** containing the following RecordIO files:
-- `train.rec`: The Data (27.3 GB) - All images packed
-- `train.idx`: The Index (97 MB) - Offsets for random access
-- `train.lst`: The Metadata (411 MB) - Path/Label/Index map
-It also includes standard validation benchmarks:
-- `lfw.bin`, `agedb_30.bin`, `cfp_fp.bin`
-## 🚀 Usage
-You can download the zip file and extract it to your training environment.
-```python
-from huggingface_hub import hf_hub_download
-import zipfile
-# Download
-zip_path = hf_hub_download(repo_id="inanxr/prosopo-large-dataset", filename="prosopo-large-dataset.zip", repo_type="dataset")
-# Extract
-with zipfile.ZipFile(zip_path, 'r') as zip_ref:
-    zip_ref.extractall("./ms1m_dataset")
-```
-## 📜 Acknowledgements
-Original Data Source: **MS-Celeb-1M** (Cleaned by InsightFace/DeepGlint)
-> *InanXR/Prosopo re-hosting for reproducibility.*
-> *Guo, Yandong, et al. "Ms-celeb-1m: A dataset and benchmark for large-scale face recognition." ECCV 2016.*

+---
+dataset_info:
+  features:
+  - name: image
+    dtype: image
+  - name: label
+    dtype: class_label
+  splits:
+  - name: train
+    num_bytes: 29320000000
+    num_examples: 5179510
+  download_size: 26157852001
+  dataset_size: 29320000000
+license: cc-by-4.0
+task_categories:
+- image-classification
+tags:
+- face-recognition
+- ms1mv3
+- prosopo
+pretty_name: Prosopo Large Dataset (MS1MV3)
+size_categories:
+- 1M<n<10M
+---
+# 🧪 Prosopo Large Dataset (MS1MV3)
+![Prosopo Banner](https://raw.githubusercontent.com/InanXR/Prosopo/refs/heads/main/assets/Prosopo_Large.png)
+> **5.1 Million high-quality aligned face images for state-of-the-art model training.**
+This dataset serves as the **core training engine** for the **Prosopo** face recognition system. It contains the **MS1MV3** (MS-Celeb-1M Cleaned) dataset, pre-aligned and packed into the high-performance MXNet RecordIO format.
+## 📊 Dataset Statistics
+| Metric | Value |
+| :--- | :--- |
+| **Identities** | 93,431 |
+| **Total Images** | 5,179,510 |
+| **Image Size** | 112 x 112 px |
+| **Alignment** | RetinaFace (5-point landmark) |
+| **Format** | MXNet RecordIO (Packed Binary) |
+| **Total Size** | ~28 GB (Unpacked) |
+## 📁 Content Structure
+The dataset is provided as a **ZIP archive** containing the following RecordIO files:
+- `train.rec`: The Data (27.3 GB) - All images packed
+- `train.idx`: The Index (97 MB) - Offsets for random access
+- `train.lst`: The Metadata (411 MB) - Path/Label/Index map
+It also includes standard validation benchmarks:
+- `lfw.bin`, `agedb_30.bin`, `cfp_fp.bin`
+## 🚀 Usage
+You can download the zip file and extract it to your training environment.
+```python
+from huggingface_hub import hf_hub_download
+import zipfile
+# Download
+zip_path = hf_hub_download(repo_id="inanxr/prosopo-large-dataset", filename="prosopo-large-dataset.zip", repo_type="dataset")
+# Extract
+with zipfile.ZipFile(zip_path, 'r') as zip_ref:
+    zip_ref.extractall("./ms1m_dataset")
+```
+## 📜 Acknowledgements
+Original Data Source: **MS-Celeb-1M** (Cleaned by InsightFace/DeepGlint)
+> *InanXR/Prosopo re-hosting for reproducibility.*
+> *Guo, Yandong, et al. "Ms-celeb-1m: A dataset and benchmark for large-scale face recognition." ECCV 2016.*