Initial dataset push

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Poles_LeanedStraight/Classification/README.md

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+# Classification — Leaned vs Straight vs Rejected
+
+- This dataset contains real-world images of electric utility poles labeled into three classes: **"Leaned"**, **"Straight"**, and **"Rejected"**. It is designed for **image-level classification tasks**.
+- The dataset complements a previously released object detection dataset focused on bounding-box annotations.
+- This module fine-tunes a DINOv2 ViT-B/14 model for **image-level classification** of poles.
+- GitHub repo link: [Here](https://github.com/sampath-balaji/Electrical-Lines-Defect-Detection/tree/main/Pole_LeanedStraight_Defect/Classification)
+
+---
+
+## Dataset Details
+
+
+### Dataset Description
+
+- **Task:** Image classification (multi-class)
+- **Classes:** `Leaned`, `Straight`, `Rejected`
+- **Image format:** `.jpg`
+- **Label format:** Folder-based (one folder per class)
+
+The images were collected by field linemen across three districts in Andhra Pradesh: **Visakhapatnam**, **Eluru**, and **Kakinada**. Each image was labeled by three annotators, with the final label selected via **majority vote**.
+
+- **Curated by:** Sampath Balaji & team, at APEPDCL
+- **Funded by:** Eastern Power Distribution Company of Andhra Pradesh Limited (APEPDCL)
+- **License:** CC BY 4.0
+- **Language(s):** Not applicable
+
+## Uses
+  
+### Direct Use
+
+This dataset is intended for training and evaluating machine learning models that classify pole alignment from real-world utility inspection photos.
+
+### Out-of-Scope Use
+
+- Surveillance, identity detection, or person-tracking
+- Facial recognition or biometrics
+- Applications requiring highly controlled camera perspectives
+
+## Dataset Structure
+
+The dataset is organized into pre-split folders:
+```
+Poles_LeanedStraight/Classification/
+├── train/
+│ ├── Leaned/
+│ ├── Straight/
+│ └── Rejected/
+├── val/
+│ ├── Leaned/
+│ ├── Straight/
+│ └── Rejected/
+├── test/
+│ ├── Leaned/
+│ ├── Straight/
+│ └── Rejected/
+```
+- `Rejected` indicates poles/images that were ambiguous or pole bottom or top was not visible or unusable for downstream tasks but still part of this classification problem.
+- No augmentations were applied.
+- Each split is approximately: **Train 75%**, **Val 15%**, **Test 10%**
+
+## Dataset Creation
+
+### Curation Rationale
+
+This dataset was created to build an ML pipeline for real-time defect detection in utility poles, starting with identifying leaned poles as a foundational task. The aim is to build an electrical line quality monitoring system to revolutionize maintenance workflows.
+
+### Source Data
+
+#### Data Collection and Processing
+
+Images were collected using mobile cameras by field linemen in Andhra Pradesh.
+
+#### Image Capture Protocol
+
+The images were captured on the ground by APEPDCL linemen following a field protocol designed to reduce bias and ensure consistency:
+
+- Linemen were instructed to **hold the phone upright and parallel to the pole** to avoid tilt or skew that could distort the pole's perceived angle.
+- Images were to include both the **top and bottom of the pole**, as full visibility is essential for lean classification.
+- **No filters or enhancements** were applied during capture.
+
+Despite this protocol, many images were captured with a **tilted horizon**, making it difficult to determine if a pole was leaned or not. Tools like **Google Photos** and **Adobe Lightroom** were used to **manually correct tilt** by straightening the horizon.
+
+To prevent this in future data, the team identified an open-source camera app, **[Open Camera](https://opencamera.org.uk/)**, which uses the phone’s **gyroscope sensor to auto-straighten images**. This app is now recommended for field use to ensure more consistent alignment.
+
+#### Observations and Future Protocol Improvements
+
+During annotation, a key insight emerged:
+
+- When images were taken **too close to the pole**, **perspective distortion** made it harder to determine pole alignment.
+- Going forward, linemen will be instructed to **stand approximately one pole height away** to reduce distortion and improve labeling accuracy.
+
+#### Who are the source data producers?
+
+Electric linemen from APEPDCL working in Visakhapatnam, Eluru, and Kakinada districts of Andhra Pradesh, India.
+
+### Annotations
+
+#### Labeling Process
+Images were individually labeled by 3 human labelers and saved in folder-based structure. **Majority vote** was used to determine the final class label. The team worked to maintain consistency, though **some labeling errors may persist due to the manual nature** of the task.
+
+#### Who are the annotators?
+
+The annotation team consisted of project members:  
+- Srija Vanapalli  
+- Krishna Sumwith  
+- Jyotsna Adari  
+Under the direction of the team lead.
+
+
+#### Personal and Sensitive Information
+
+No personal or sensitive data was intentionally included. However, some images may contain **people or faces captured incidentally** in public areas, as the data was collected in outdoor settings.
+
+## Bias, Risks, and Limitations
+
+- **Geographic bias:** All data comes from Andhra Pradesh, India — results may not generalize to other regions or environments.
+- **Scene/perspective bias:** Variation in camera angles and distances may affect model training.
+- **Privacy risk:** Images taken in public areas may contain identifiable people.
+
+### Recommendations
+
+- Apply **image normalization or augmentation** techniques to improve generalization.
+- Account for **perspective and camera angle** effects during training and evaluation.
+- Use ethically and responsibly, especially in systems that affect public infrastructure.
+
+## Validation Accuracy
+
+    - Best Val Acc: 84.14%
+    - Hardware used: RTX 4070 Ti SUPER on [JOHNAIC](https://von-neumann.ai)
+
+## Sample Visualizations
+<p align="center"> <img src="https://raw.githubusercontent.com/sampath-balaji/Electrical-Lines-Defect-Detection/refs/heads/main/Pole_LeanedStraight_Defect/Classification/assets/val_viz_ep40.png" width="600"/> </p>
+
+## More Information
+
+Follow-up datasets in this series will include other electrical line and pole defects as part of APEPDCL's Line Quality monitoring project.
+
+## Dataset Card Authors
+
+- [Sampath Balaji](https://github.com/sampath-balaji)
+
+## 📄 License
+- [Code](https://github.com/sampath-balaji/Electrical-Lines-Defect-Detection/tree/main/Pole_LeanedStraight_Defect/ObjectDetection): MIT License
+- [Dataset](https://huggingface.co/datasets/sampath-balaji/Electrical-Lines-Defect-Detection/tree/main/Poles_LeanedStraight/Classification): CC BY 4.0