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README.md

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+---
+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
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+Bean leaf disease classifier trained with self-improving Classifier Lab pipeline.
+
+- **Developed by:** Graham Anderson (Embry OS)
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** en
+- **License:** apache-2.0
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** https://github.com/grahamaco/embry-os
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+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.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+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]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+
+## 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