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--- |
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library_name: pytorch |
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pipeline_tag: image-classification |
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license: mit |
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tags: |
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- automl |
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- pytorch |
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- torchvision |
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- optuna |
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- early-stopping |
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model_name: Tomato vs Not-Tomato — AutoML (Compact CNN / Transfer Learning) |
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language: |
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- en |
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--- |
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# Tomato vs Not-Tomato — AutoML (Compact NN) |
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## Purpose |
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Course assignment to practice AutoML for neural networks on a small, real dataset. |
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We train a compact image classifier to predict whether an image **is a tomato (1) or not (0).** |
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## Dataset |
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- **Source:** classmate dataset on Hugging Face → `Iris314/Food_tomatoes_dataset` |
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- **Task:** Binary classification (`0 = not_tomato`, `1 = tomato`) |
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- **Splits:** Stratified **60/20/20** (train/val/test) created in the notebook |
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- **Size:** ~30 images total (very small) |
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- **Input resolution:** 224×224 |
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## Preprocessing & Augmentation |
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- **Normalization:** mean = [0.485, 0.456, 0.406], std = [0.229, 0.224, 0.225] |
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- **Train augmentations:** RandomResizedCrop, HorizontalFlip(0.5), ColorJitter |
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- **Eval transforms:** Resize → CenterCrop → Normalize |
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## AutoML Setup |
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- **Search framework:** Optuna (budgeted search with pruning) |
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- **Architectures:** `smallcnn` (from scratch), `resnet18`, `mobilenet_v3_small` |
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- **Hyperparams:** optimizer ∈ {adamw, sgd}, lr ∈ [1e-5, 5e-3] (log), weight_decay ∈ [1e-6, 1e-2] (log), |
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dropout ∈ [0, 0.6], batch_size ∈ {8, 12, 16}, `freeze_backbone` ∈ {True, False} (for pretrained) |
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- **Early stopping:** patience = 6 epochs on validation F1 |
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- **Budget:** 10 trials, max 20 epochs per trial, ~5 min wall-clock |
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- **Seed:** 42 |
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- **Compute:** Google Colab GPU runtime |
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## Best Model & Hyperparameters |
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```json |
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{ |
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"arch": "mobilenet_v3_small", |
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"freeze_backbone": false, |
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"dropout": 0.4761270681732692, |
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"optimizer": "adamw", |
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"lr": 1.1860369117967872e-05, |
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"weight_decay": 0.00043282443346186894, |
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"batch_size": 16 |
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} |
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``` |
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## Results on Held out Test |
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accuracy: 0.83, f1: 0.80 |
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## Training curves and Early Stopping |
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Validation F1 was tracked each epoch with patience = 6. Training stopped once performance plateaued, preventing overfitting. |
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## Reproducability |
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- Seed: 42 |
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- Python: 3.12 |
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- PyTorch: 2.4.1 |
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- TorchVision: 0.19.1 |
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- Optuna: 4.0.0 |
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- Compute: Google Colab GPU (T4) |
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## Limitations & Known Failure Modes |
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- Extremely small dataset → risk of overfitting and unstable metrics. |
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- Backgrounds and lighting variations can bias predictions. |
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- Out-of-distribution images (e.g., tomato cartoons, extreme angles) may fail. |
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## Ethics |
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- This model is for coursework demonstration only; not for production or consequential decisions. |
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## License |
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- Code & weights: MIT (adjust per course requirements) |
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- Dataset: follow the original dataset’s license/terms |
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## Acknowledgments |
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- Dataset: Iris314/Food_tomatoes_dataset |
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- AutoML: Optuna |
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- Backbones: torchvision models |
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- Trained in Google Colab |
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- GenAI tools assisted with boilerplate organization and documentation |
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