---
license: mit
datasets:
- Iris314/Food_tomatoes_dataset
language:
- en
metrics:
- accuracy
- f1
---


# Model Card: AutoML Neural Network Predictor for Tomato Images  



## Model Details  
- **Framework**: `AutoGluon`  
- **Task**: `Classification`

---

## Dataset  
- **Source**: [Iris314/Food_tomatoes_dataset](https://huggingface.co/datasets/Iris314/Food_tomatoes_dataset)  
- **Target**: `label`  
- **Splits**:  
  - **Augmented**: 490 rows
  - **Original**: 49 rows
- **Preprocessing Steps**:  
  - Stratify 'label' column.  
  - Train/test split (80%/20%).  

---

## Model  

| Name              | Type                            | Params | Mode  |
|-------------------|---------------------------------|--------|-------|
| model             | TimmAutoModelForImagePrediction | 11.2 M | train |
| validation_metric | MulticlassAccuracy              | 0      | train |
| loss_func         | CrossEntropyLoss                | 0      | train |

**Summary**
- Trainable params: **11.2 M**  
- Non-trainable params: **0**  
- Total params: **11.2 M**  
- Total estimated model params size: **44.710 MB**  
- Modules in train mode: **101**  
- Modules in eval mode: **0**
- Validation accuracy: 1
- Training time: ~49.5 seconds


---

## Training  
- **Framework**: [AutoGluon](https://auto.gluon.ai/stable/index.html)  
- **Preset**: `"medium_quality"`  
- **Image Size**: 224x224
- **Explored Models**: ResNet 18

---


## Results  

- **Test Split**:  
  - Accuracy: 0.9796
  - Weighted F1: 0.9796

---

## Notes  

Educational use only.
Used AutoML for training model, used ChatGPT and Gemini to debug, used ChatGPT to make table for model info.