Spaces:
Sleeping
Sleeping
| title: Solar Panel Segmentation | |
| emoji: 📊 | |
| colorFrom: blue | |
| colorTo: green | |
| sdk: streamlit | |
| sdk_version: "1.36.0" | |
| app_file: app.py | |
| pinned: false | |
| # Solar Panel Segmentation — Model Zoo | |
| Aerial / satellite imagery → binary mask of photovoltaic (PV) panels. | |
| This repo trains and compares **five** semantic-segmentation models on the same solar-panel dataset, ships their checkpoints, runs batch inference over a large image pool, and serves an interactive HTML tool to compare model outputs side-by-side. | |
| --- | |
| ## What's in here | |
| ``` | |
| seg_models/ | |
| ├── final_data/ # Training / validation dataset | |
| │ ├── train/{images,masks} # 5,325 image+mask pairs | |
| │ └── val/{images,masks} # 1,331 image+mask pairs | |
| ├── final_data.zip # Zipped dataset (~31 MB) | |
| │ | |
| ├── pv_panel_models/ # 4 "lightweight" baselines + tooling | |
| │ ├── cnn_model/ # SegNet (encoder-decoder w/ MaxUnpool) | |
| │ ├── unet_model/ # Classic U-Net | |
| │ ├── vit_model/ # SegFormer mit-b0 (transformer) | |
| │ ├── segformer_b5_model/ # SegFormer mit-b5 (large transformer) | |
| │ ├── predict_all.py # Runs all 4 models over a folder of images | |
| │ ├── predictions/ # Per-model PNG masks + manifest.json | |
| │ ├── source_images -> .../filtered_images # Symlink to inference inputs | |
| │ ├── comparison_tool.html # Interactive viewer (per-image, all models) | |
| │ ├── model_comparison_dashboard.html # Training-curve dashboard | |
| │ ├── model_architectures.html # Architecture explainer page | |
| │ ├── generate_dashboard.py # Builds the dashboard from training_logs.txt | |
| │ └── serve.sh # `python -m http.server` wrapper | |
| │ | |
| └── hybrid_20_epochs_4ep_stop/ # 5th, heavier model (separate experiment) | |
| ├── hybrid_solar_segmenter.py # Hybrid Attention-UNet (ResNet34 + SE + AG) | |
| ├── hybrid_solar_segmenter.pth # Trained weights (~335 MB) | |
| ├── README.md / WORKFLOW.md # Detailed write-up of this model | |
| ├── training_curves.png | |
| └── prediction_*.png # Sample qualitative outputs | |
| ``` | |
| --- | |
| ## Dataset | |
| Binary segmentation masks for solar panels, stored as `*.jpg` images paired with `*_mask.png` ground truth. Splits: | |
| | Split | Samples | | |
| |------|--------| | |
| | Train | 5,325 | | |
| | Val | 1,331 | | |
| The 4 baselines train on **128×128** crops; the hybrid model trains on **256×256**. Augmentations: H/V flips, 90° rotations, brightness/contrast jitter. | |
| Loss is consistent across all models: **`0.5 * BCE + 0.5 * Dice`** (`CombinedLoss`), with Adam / AdamW optimizers and `ReduceLROnPlateau` or `CosineAnnealingWarmRestarts` schedulers. | |
| --- | |
| ## The five models | |
| | Model | Architecture | Params | Best Val Dice | | |
| |------|------|------:|------:| | |
| | **SegNet (CNN)** | VGG-style encoder/decoder with stored MaxPool indices for unpooling | 11.8 M | 0.9161 | | |
| | **U-Net** | Classic encoder/decoder with skip-concatenation | ~31 M | 0.9332 | | |
| | **SegFormer mit-b0** | HuggingFace hierarchical transformer (small) | 3.7 M | 0.9251 | | |
| | **SegFormer mit-b5** | HuggingFace hierarchical transformer (large) | 84.6 M | 0.9334 | | |
| | **Hybrid Attention-UNet** | ResNet34 (ImageNet) encoder + SE blocks + Attention Gates + UNet decoder, 256×256 | ~24 M | see `hybrid_20_epochs_4ep_stop/README.md` | | |
| Each baseline directory follows the same layout: | |
| ``` | |
| <model>/ | |
| ├── <model>.py # nn.Module definition | |
| ├── dataset.py # SolarPanelDataset + augmentations + DataLoaders | |
| ├── train.py # Training loop, metrics, best-checkpoint saving | |
| ├── requirements.txt | |
| ├── checkpoints/ | |
| │ ├── best_model.pth # State-dict by best val Dice | |
| │ └── training_logs.txt # Per-epoch loss / Dice / IoU / Precision / Recall | |
| └── venv/ # Per-model virtualenv (not all dirs) | |
| ``` | |
| Metrics tracked: Loss, Accuracy, Precision, Recall, **Dice**, **IoU**. | |
| --- | |
| ## Inference & comparison tooling | |
| ### `predict_all.py` | |
| Loads all four baseline checkpoints, runs each over every JPG in | |
| `pv_panel_models/source_images/` (symlink → `filtered_images`), and writes: | |
| - `predictions/<model_short_name>/<stem>.png` — binary mask resized to original image dimensions | |
| - `predictions/manifest.json` — index used by the web tool | |
| Currently contains predictions for **15,423 images** per model. | |
| ```bash | |
| cd pv_panel_models | |
| python predict_all.py | |
| ``` | |
| ### Web viewer | |
| ```bash | |
| ./pv_panel_models/serve.sh # default port 8000 | |
| # then open: | |
| # http://localhost:8000/comparison_tool.html # per-image side-by-side viewer | |
| # http://localhost:8000/model_comparison_dashboard.html # training curves | |
| # http://localhost:8000/model_architectures.html # architecture explainer | |
| ``` | |
| The dashboard is regenerated from training logs: | |
| ```bash | |
| python pv_panel_models/generate_dashboard.py | |
| ``` | |
| --- | |
| ## Reproducing a model | |
| Each baseline is self-contained. From its directory: | |
| ```bash | |
| pip install -r requirements.txt | |
| python train.py | |
| ``` | |
| Train scripts hard-code dataset paths to the original author's `~/Desktop/seg_models/...` location — update the `train_dir` / `val_dir` (or `train_images` / `train_masks` in the hybrid script) to point at this repo's `final_data/` before running. | |
| The hybrid model additionally requires `scikit-learn` and `matplotlib` and downloads ImageNet ResNet34 weights on first run. | |
| --- | |
| ## Notes | |
| - The `.venv/` at the repo root and the per-model `venv/` directories are local environments — not portable; recreate from `requirements.txt`. | |
| - `final_data.zip` is the same dataset as `final_data/` packaged for distribution. | |
| - Hard-coded absolute paths under `/home/mohamed-ennhiri/Desktop/...` appear in several scripts; expect to patch them when running on a fresh machine. | |