---
datasets:
- AI-Lab-Makerere/beans
language: en
library_name: timm
license: apache-2.0
pipeline_tag: image-classification
tags:
- image-classification
- classifier-lab
- embry-os
- automated-ml
- beans
- plant-disease
model-index:
- name: efficientnet_b0
  results:
  - task:
      type: image-classification
    dataset:
      name: Beans
      type: AI-Lab-Makerere/beans
    metrics:
    - type: f1
      value: 0.9218
      name: Macro F1
    - type: accuracy
      value: 0.9219
---

# Model Card for grahamaco/beans-classifier

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## Model Details

### Model Description

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Bean leaf disease classifier trained with self-improving Classifier Lab pipeline.

- **Developed by:** Graham Anderson (Embry OS)
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- **Model type:** [More Information Needed]
- **Language(s) (NLP):** en
- **License:** apache-2.0
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### Model Sources [optional]

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- **Repository:** https://github.com/grahamaco/embry-os
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## Uses

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### Direct Use

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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### Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

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## Training Details

### Training Data

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### Training Procedure

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#### Preprocessing [optional]

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#### Training Hyperparameters

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## Evaluation

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### Results

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#### Summary



## Model Examination [optional]

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## Environmental Impact

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]

### Model Architecture and Objective

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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## Model Card Contact

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## Results (Verified on Held-Out Test Set)

**128 test images, never seen during training or validation.**

| Metric | Value |
|--------|-------|
| **Macro F1** | **0.9218** |
| **Accuracy** | **0.9219** |
| **Holdout Gate** | **PASSED >= 0.90** |

### Per-Class Metrics

| Class | Precision | Recall | F1 | Support |
|-------|-----------|--------|----|---------|
| angular_leaf_spot | 0.92 | 0.84 | 0.88 | 43.0 |
| bean_rust | 0.87 | 0.95 | 0.91 | 43.0 |
| healthy | 0.98 | 0.98 | 0.98 | 42.0 |


### Confusion Matrix

| | angular_leaf_spot | bean_rust | healthy |
|---|---|---|---|
| **angular_leaf_spot** | 36 | 6 | 1 |
| **bean_rust** | 2 | 41 | 0 |
| **healthy** | 1 | 0 | 41 |


## Self-Improvement Loop

The classifier was trained iteratively until the holdout gate (F1 >= 0.90) was met.
Each round adjusts hyperparameters and augmentation strategy based on prior failures.

| Round | Epochs | LR | Augment | Val F1 | Test F1 | Gate |
|-------|--------|----|---------|--------|---------|------|
| 1 | 10 | 0.0002 | 1 | 0.9328 | 0.8762 | FAILED |
| 2 | 15 | 0.0001 | 2 | 0.9240 | 0.9218 | PASSED |


**Winning configuration**: Round 2

## Architecture

- **Backbone**: efficientnet_b0 (pretrained on ImageNet)
- **Framework**: PyTorch + timm
- **Classes**: angular_leaf_spot, bean_rust, healthy
- **Image size**: 224x224

## Training Pipeline (Classifier Lab)

1. **Research** -- Identified EfficientNet B0 as candidate
2. **Data** -- Beans dataset from HuggingFace (1034 train, 133 val, 128 test)
3. **Self-improvement loop** -- Round 1 failed gate (F1 0.876), adjusted LR and augmentation, Round 2 passed (F1 0.922)
4. **Evaluate** -- Held-out test set (128 images, never seen during training)
5. **Promote** -- Gate passed, pushed to HuggingFace with model checkpoint

## License

Apache 2.0