You can deploy this model and start earning money today!

nitishmehta/
DeepSolarEye-cleaning-quality

Name: DeepSolarEye-cleaning-quality
Availability: InStock

1710.03811

2210.09226

2511.09604

Views

No views yet

DeepSolarEye - Solar Panel Cleaning Quality & Vendor Rating

🔬 Overview

A complete pipeline for assessing solar rooftop panel cleaning quality and rating cleaning vendor performance. Based on the architecture from:

"DeepSolarEye: Power Loss Prediction and Weakly Supervised Soiling Localization via Fully Convolutional Networks for Solar Panels"
Mehta et al., 2017 (arxiv:1710.03811)

📦 What's Included

File	Description
`model.pth`	Trained model weights (49MB)
`deepsolar_config.json`	Model config with classes & quality ratings
`inference.py`	Single-image prediction script
`auto_label.py`	Auto-label unlabeled images using SigLIP zero-shot + vendor report
`train.py`	Full training script (data generation + training + Hub push)
`DeepSolarEye_Training.ipynb`	Google Colab notebook — train on free T4 GPU

🚀 Quick Start: 3-Step Pipeline

Step 1: Auto-Label Your Images (if unlabeled)

Uses Google's SigLIP model to automatically classify your solar panel images — no training data needed.

bash

pip install transformers torch pillow tqdm

python auto_label.py \
    --input_dir /path/to/your/solar/images/ \
    --output_dir /path/to/labeled/ \
    --vendor_name "SolarClean Pro"

Output:

labeled/train/ — Auto-labeled images organized by class (ready for training)
labeled/review/ — Low-confidence images for manual review
vendor_report.json — Cleaning quality grade (A+ to F)

Step 2: Train on Your Data (Google Colab)

Open In Colab

Open the Colab notebook
Upload your labeled images (zip)
Train on free T4 GPU (50 epochs, ~30 min)
Push trained model to Hub

Step 3: Rate a Cleaning Vendor

python

python auto_label.py \
    --input_dir /photos/after_vendor_cleaning/ \
    --output_dir /reports/vendor_abc/ \
    --vendor_name "ABC Solar Cleaning"

Sample Output:

🏢 VENDOR CLEANING QUALITY REPORT
==================================
  Vendor: ABC Solar Cleaning
  Grade: B
  Panels Inspected: 150
  
  📊 Quality Score: 3.67/5.0
  ✅ Clean Panels: 45.3%
  ✅ Acceptable (Clean + Light-Dust): 72.0%
  
  ⚠️ Issues Found:
    - Heavy-Dust: 12 panels
    - Bird-Dropping: 8 panels
  
  💰 Recommendation: Good cleaning. Minor issues to address.

🏗️ Architecture

ResNet-18 + BiDIAF (Bi-Directional Input-Aware Fusion) — 12.8M parameters

Input Image (224×224×3)
    │
    ├── ResNet Stem (conv→bn→relu→maxpool)
    ├── Stage 1 (64ch)  + BiDIAF Block 1 + Spatial Dropout
    ├── Stage 2 (128ch) + BiDIAF Block 2 + Spatial Dropout  
    ├── Stage 3 (256ch) + BiDIAF Block 3 + Spatial Dropout
    ├── Stage 4 (512ch) + BiDIAF Block 4 + Spatial Dropout
    ├── Global Average Pooling
    └── FC Classifier → 6 classes → Quality Rating (1-5⭐)

Key design decisions from the paper:

BiDIAF reinforces input image at every spatial level — preserves color/texture features critical for soiling
Random weight init (not ImageNet) — learns domain-specific features
No color augmentation — color IS the signal for soiling detection
Inverse class-frequency weighted loss — handles imbalanced cleaning data

📊 Classes & Quality Ratings

Class	Rating	Stars	Vendor Action
Clean	5/5	⭐⭐⭐⭐⭐	Approve payment
Light-Dust	4/5	⭐⭐⭐⭐☆	Acceptable, note for next visit
Bird-Dropping	3/5	⭐⭐⭐☆☆	Deduct, request spot-clean
Partial-Shadow	3/5	⭐⭐⭐☆☆	Flag obstruction for removal
Heavy-Dust	2/5	⭐⭐☆☆☆	Reject, request re-cleaning
Snow-Covered	1/5	⭐☆☆☆☆	Immediate attention required

Vendor Grading Scale:

Avg Score	Grade	Action
≥ 4.5	A+	Excellent — approve full payment
≥ 4.0	A	Great — approve payment
≥ 3.5	B	Good — minor issues to discuss
≥ 3.0	C	Acceptable — needs improvement
≥ 2.0	D	Poor — deduct payment
< 2.0	F	Unacceptable — request re-clean

💻 Usage: Single Image Inference

python inference.py --image panel_photo.jpg

Or in Python:

python

from inference import load_model, predict

model = load_model()  # Downloads from Hub automatically
result = predict(model, "panel.jpg")

print(f"Condition: {result['predicted_class']}")
print(f"Rating: {result['quality_rating']}/5")
print(f"Confidence: {result['confidence']:.1%}")

📈 Training on Your Own Data

Option A: Google Colab (recommended)

Open DeepSolarEye_Training.ipynb in Colab
Free T4 GPU, ~30 min for 50 epochs

Option B: HF Jobs (requires Pro)

bash

# After getting HF Pro subscription
pip install huggingface_hub
python train.py

Option C: Local GPU

bash

pip install torch torchvision transformers datasets evaluate accelerate scikit-learn
python train.py  # Modify DATA_DIR to point to your data

🔗 References

DeepSolarEye (WACV 2018) — Core architecture
CNN Fault Detection for PV Panels — Multi-class PV classification
PV-DDPM — Synthetic defect generation for PV

📝 License

MIT

nitishmehta/DeepSolarEye-cleaning-quality

DeepSolarEye - Solar Panel Cleaning Quality & Vendor Rating

🔬 Overview

📦 What's Included

🚀 Quick Start: 3-Step Pipeline

Step 1: Auto-Label Your Images (if unlabeled)

Step 2: Train on Your Data (Google Colab)

Step 3: Rate a Cleaning Vendor

🏗️ Architecture

📊 Classes & Quality Ratings

💻 Usage: Single Image Inference

📈 Training on Your Own Data

🔗 References

📝 License

nitishmehta/
DeepSolarEye-cleaning-quality