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README.md

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