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---
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.
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