| --- |
| title: Intel Scene CNN |
| emoji: 🏔️ |
| colorFrom: blue |
| colorTo: green |
| sdk: gradio |
| sdk_version: 5.9.1 |
| app_file: app.py |
| pinned: false |
| license: mit |
| --- |
| |
| # Intel Scene Classification: a CNN built from scratch |
|
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| A plain convolutional neural network (no ResNet, no pretrained weights) trained on the |
| [Intel Image Classification](https://www.kaggle.com/datasets/puneet6060/intel-image-classification) |
| dataset: ~25k natural scene photos in 6 classes: **buildings, forest, glacier, mountain, sea, street**. |
|
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| This is the follow-up to my EuroSAT satellite CNN. It tests whether the same architecture |
| generalizes to street-level photos, where orientation matters and the classes overlap visually. |
|
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| **Live demo:** upload an image, get the predicted class and a Grad-CAM heatmap, plus the test metrics. |
|
|
| ## Results |
|
|
| | Metric | Value | |
| |---|---| |
| | Test accuracy (seg_test, 3,000 images) | **89.87%** | |
| | Macro F1 | **0.900** | |
| | Best validation accuracy | **90.98%** | |
| | Parameters | **1,206,886** | |
| |
| | Class | Precision | Recall | F1 | Test images | |
| |---|---|---|---|---| |
| | buildings | 0.913 | 0.860 | 0.886 | 437 | |
| | forest | 0.979 | 0.985 | 0.982 | 474 | |
| | glacier | 0.846 | 0.866 | 0.856 | 553 | |
| | mountain | 0.876 | 0.830 | 0.852 | 525 | |
| | sea | 0.895 | 0.937 | 0.916 | 510 | |
| | street | 0.897 | 0.918 | 0.907 | 501 | |
| |
| **Where it fails:** mountain → glacier (65), buildings → street (46), glacier → mountain (46), |
| street → buildings (29). Almost all errors come from two pairs: snow-covered peaks vs. glaciers, |
| and urban scenes where a photo of a street is also a photo of buildings. Forest is near-perfect |
| (98% F1) because its texture and color are unique. Several high-confidence "mistakes" are |
| labeling errors in the dataset itself. |
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| ## Dataset |
| |
| | Split | Images | |
| |---|---| |
| | seg_train → train (85%) | ~11,900 | |
| | seg_train → validation (15%, stratified) | ~2,100 | |
| | seg_test (held-out test) | 3,000 | |
| | seg_pred (unlabeled) | ~7,300 | |
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| ## Architecture |
| |
| ```mermaid |
| flowchart LR |
| A[Input 3×128×128] --> B1[Block 1<br/>32ch → 64×64] |
| B1 --> B2[Block 2<br/>64ch → 32×32] |
| B2 --> B3[Block 3<br/>128ch → 16×16] |
| B3 --> B4[Block 4<br/>256ch → 8×8] |
| B4 --> G[Global Avg Pool<br/>256] |
| G --> F1[Dropout 0.4 → FC 128 → ReLU] |
| F1 --> F2[FC 6 → softmax] |
| ``` |
| |
| Each block is `Conv3×3 → BN → ReLU → Conv3×3 → BN → ReLU → MaxPool 2×2 → Dropout2d(0.1)`. |
| |
| | Stage | Output shape | |
| |---|---| |
| | Input | 3 × 128 × 128 | |
| | Block 1 | 32 × 64 × 64 | |
| | Block 2 | 64 × 32 × 32 | |
| | Block 3 | 128 × 16 × 16 | |
| | Block 4 | 256 × 8 × 8 | |
| | Global average pool | 256 | |
| | FC + ReLU | 128 | |
| | FC (logits) | 6 | |
| |
| ## Training setup |
| |
| - **Optimizer:** AdamW (lr 1e-3, weight decay 1e-2), cosine LR schedule, 30 epochs |
| - **Loss:** cross-entropy with label smoothing 0.1 |
| - **Image size:** 128×128, normalized with per-channel stats computed from the training data |
| - **Augmentation:** random resized crop (0.75–1.0), horizontal flip, ±10° rotation, mild color jitter |
| - **No vertical flips or large rotations.** Scenes have gravity (sky on top), unlike satellite images. |
| - **Model selection:** best validation accuracy checkpoint |
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| ## What the CNN learns |
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| **Feature maps before vs. after training** |
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| **First-layer filters before vs. after training** |
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| **Grad-CAM: where the network looks** |
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| **Confidence and mistakes** |
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| ## Run locally |
| |
| ```bash |
| pip install -r requirements.txt gradio |
| python app.py |
| ``` |
| |
| ## Files |
| |
| | File | Purpose | |
| |---|---| |
| | `app.py` | Gradio app: upload, prediction, Grad-CAM, metrics, plots | |
| | `model.py` | `SceneCNN` architecture + Grad-CAM | |
| | `cnn_intel.pth` | Trained weights (state_dict) | |
| | `cnn_intel_meta.json` | Class names, normalization stats, width, image size | |
| | `metrics.json` | Test metrics shown in the app | |
| | `images/` | All plots from training and evaluation | |
| |
| ## Author |
| |
| Chandra Prakash Bathula, Adjunct Faculty, Saint Louis University (School for Professional Studies) |
