Model Card: AutoML Neural Network Predictor for Tomato Images
Model Details
- Framework:
AutoGluon - Task:
Classification
Dataset
- Source: 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
- 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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