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README.md

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+---
+version: 1.0.0
+license: other
+license_name: proprietary
+task_categories:
+  - object-detection
+  - video-classification
+tags:
+  - sports
+  - soccer
+  - football
+  - ball-tracking
+  - tiny-object-detection
+annotations_creators:
+  - human-verified
+  - machine-generated
+pretty_name: Soccer Ball Tracking Dataset
+size_categories:
+  - 1K<n<10K
+---
+
+# Soccer Ball Tracking Dataset
+
+A curated dataset for detecting and tracking soccer balls in broadcast footage, specifically designed for **tiny object detection** challenges. This dataset supports the development of models robust to motion blur, long-shot scales, and occlusions.
+
+## Dataset Description
+
+This dataset consists of **7,152 frames** extracted from **54 video clips** of professional soccer broadcasts. The data is split into two categories based on ball visibility:
+
+- **Visible**: Frames where the ball is clearly visible and annotated with a bounding box.
+- **Not Visible**: Frames where the ball is occluded, out of frame, or otherwise not visible (negative samples).
+
+The dataset is designed to train models that can distinguish between the ball and ball-like objects (player heads, socks, logos) in low-resolution (SD/480p) scenarios.
+
+### Statistics
+
+| Category | Samples | Description |
+|----------|---------|-------------|
+| **Visible** | 4,307 | Frames with at least one ball bounding box |
+| **Not Visible** | 2,845 | Frames with no visible ball (hard negatives) |
+| **Total** | **7,152** | Total frames from 54 clips |
+
+### Source Data
+
+- **Domain**: Professional Soccer Broadcasts
+- **Resolution**: Varied (processed at 640px for training)
+- **Annotation Style**: YOLO format (normalized xywh)
+- **Labeling Method**: Active Learning Loop (Model Mining -> Pseudo-labeling -> Manual Verification)
+- **Anonymization**: Source video names have been replaced with UUIDs.
+
+## Dataset Structure
+
+The dataset is organized into two main folders based on visibility, with a metadata file providing detailed indexing.
+
+```
+infactory-ai/ball-tracking/
+├── README.md                    # Dataset card
+├── metadata.csv                 # Index with all metadata
+├── dataset_info.json            # Dataset statistics
+└── data/
+    ├── visible/                 # Frames with ball visible
+    │   ├── {uuid}_{frame}.jpg
+    │   └── {uuid}_{frame}.txt   # YOLO label
+    └── not_visible/             # Frames with no ball
+        └── {uuid}_{frame}.jpg
+```
+
+### Metadata Fields (`metadata.csv`)
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `file_path` | string | Relative path to the image file |
+| `video_source` | string | UUID of the source video clip |
+| `frame_index` | int | Frame number in the original clip |
+| `visibility` | string | `visible` or `not_visible` |
+| `bboxes_count` | int | Number of bounding boxes in the frame |
+
+## Model Architecture & Methodology
+
+This dataset was created to support a specific modeling approach:
+
+1.  **Architecture**: **YOLOv8-P2 (Custom)**
+    *   Based on Ultralytics YOLOv8 Nano.
+    *   **Custom P2 Head**: Added a stride-4 detection head to preserve high-resolution features for tiny objects.
+    *   **Input Resolution**: 640px (simulating 480p SD quality) for speed (>60fps) and robustness.
+
+2.  **Active Learning**:
+    *   Clips were mined from raw footage using a high-recall model.
+    *   "Hard negatives" (empty clips that looked like they had balls) were specifically targeted to reduce false positives.
+
+## Usage
+
+### Loading with Hugging Face Datasets
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("infactory-ai/ball-tracking", data_dir="data")
+
+# Filter for visible frames
+visible_frames = dataset.filter(lambda x: x["visibility"] == "visible")
+```
+
+### Parsing Labels
+
+Labels are in standard YOLO format:
+`<class_id> <x_center> <y_center> <width> <height>`
+
+*   `class_id`: 0 (ball)
+*   Coordinates are normalized to [0, 1].
+
+## License
+
+Proprietary. Usage restricted to Infactory R&D and authorized partners.
+
+## Citation
+
+```bibtex
+@dataset{ball_tracking_2026,
+  title={Soccer Ball Tracking Dataset},
+  author={Valentino Constantinou, Infactory AI Team},
+  year={2026},
+  publisher={Infactory},
+  url={https://huggingface.co/datasets/infactory-ai/ball-tracking}
+}
+```