Update README.md

README.md

@@ -10,198 +10,193 @@ base_model:
 pipeline_tag: image-classification
 tags:
 - medical
+- cervical-cancer
+- histopathology
+- undersampling
 ---
-# Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
+# Model Card: EfficientNet-B7 for Cervical Cancer Image Classification
 
-This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
+This model fine-tunes **EfficientNet-B7** for the task of binary cervical cancer image classification (Negative vs. Positive). It was trained using undersampling to handle class imbalance.
 
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
+---
 
-<!-- Provide the basic links for the model. -->
+## Model Details
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Developed by:** Beijuka / Pathogen Lab
+- **Funded by:** STI
+- **Model type:** Convolutional Neural Network (CNN)
+- **Input type:** Histopathology images (600x600, RGB)
+- **Output type:** Binary classification (Negative, Positive)
+- **License:** MIT
+- **Finetuned from:** `google/efficientnet-b7`
+
+<!-- ### Model Sources
+- **Repository:** [Your HF Repo URL]
+- **Paper [optional]:** [If you want to link e.g., EfficientNet or related research]
+- **Demo [optional]:** [Streamlit/Gradio app if you plan one]
+ -->
+---
 
 ## 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
+- classification of cervical cancer images into Negative vs Positive cases.  
 
-<!-- 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]
+### Downstream Use
+- Could be integrated into diagnostic support pipelines.  
+- Adapted for related medical imaging classification tasks.
 
 ### Out-of-Scope Use
+- **Not** a replacement for professional medical diagnosis.  
+- Should not be deployed clinically without regulatory approval.  
+- Not suitable for non-cervical images.
 
-<!-- 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]
+- The dataset was undersampled → may affect generalizability.  
+- Model performance varies by threshold (see below).  
+- Limited dataset size (19 test images) means results may not generalize.  
+- Potential domain shift if applied to different staining/preparation protocols.
 
 ### Recommendations
+- Validate on larger, more diverse datasets.  
+- Carefully calibrate decision threshold depending on application (screening vs confirmatory).  
+- Use alongside clinical expertise, not as a standalone tool.
 
-<!-- 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
 
-## How to Get Started with the Model
+```python
+from huggingface_hub import hf_hub_download
+from tensorflow import keras
 
-Use the code below to get started with the model.
+model_path = hf_hub_download(
+    "Beijuka/cancer-efficientnetb7-undersampling",
+    "cancer_efficientnetB7_undersampling.keras"
+)
+model = keras.models.load_model(model_path)
+````
 
-[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. -->
+* Histopathology images of cervical cancer (size 600x600, RGB).
+* Class imbalance addressed via **undersampling**:
 
-[More Information Needed]
+  * Positive: 84 images
+  * Negative: 100 images
+* Preprocessing: Normalization + resizing.
 
 ### Training Procedure
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+* Optimizer: Adam
+* Loss: Binary Crossentropy
+* Batch size: 8
+* Learning rate: 1e-3 (initial), 1e-5 (fine-tuning)
+* Epochs: 50 (initial), 20 (fine-tuning)
+* EarlyStopping and ModelCheckpoint callbacks used.
 
-#### Preprocessing [optional]
+### Data Splits
 
-[More Information Needed]
+* **Training:** 128 images (70 Negative, 29 Positive Post-stained, 29 Positive Pre-stained)
+* **Validation:** 37 images (20 Negative, 8 Positive Post-stained, 9 Positive Pre-stained)
+* **Test:** 19 images (10 Negative, 5 Positive Post-stained, 4 Positive Pre-stained)
 
+### Hardware
 
-#### Training Hyperparameters
+* GPU: Tesla T4 (14GB)
+* CUDA Version: 12.4
+* Software: TensorFlow/Keras
 
-- **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
+### Testing Data
 
-<!-- This should link to a Dataset Card if possible. -->
+* Independent test set: 19 images (10 Negative, 9 Positive)
 
-[More Information Needed]
+### Metrics at Threshold 0.5
 
-#### Factors
+* **Accuracy:** 0.7368
+* **Precision (Positive):** 0.8333
+* **Recall (Positive):** 0.5556
+* **F1-Score (Positive):** 0.6667
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+#### Confusion Matrix
 
-[More Information Needed]
+```
+[[9, 1],
+ [4, 5]]
+```
 
-#### Metrics
+#### Sensitivity / Specificity
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+* Negative: Sensitivity 0.90, Specificity 0.56
+* Positive: Sensitivity 0.56, Specificity 0.90
 
-[More Information Needed]
+### Threshold Analysis
 
-### Results
+* Best balance observed near 0.45–0.50
+* Lower thresholds → higher recall, more false positives
+* Higher thresholds (>0.65) → model collapses to predicting only one class
 
-[More Information Needed]
+| Threshold | Accuracy | Precision | Recall | F1     |
+| --------- | -------- | --------- | ------ | ------ |
+| 0.00      | 0.4737   | 0.4737    | 1.0000 | 0.6429 |
+| 0.05      | 0.4737   | 0.4737    | 1.0000 | 0.6429 |
+| 0.10      | 0.5263   | 0.5000    | 1.0000 | 0.6667 |
+| 0.15      | 0.5263   | 0.5000    | 0.8889 | 0.6400 |
+| 0.20      | 0.6316   | 0.5714    | 0.8889 | 0.6957 |
+| 0.25      | 0.6316   | 0.5833    | 0.7778 | 0.6667 |
+| 0.30      | 0.6316   | 0.6250    | 0.5556 | 0.5882 |
+| 0.35      | 0.6316   | 0.6250    | 0.5556 | 0.5882 |
+| 0.40      | 0.6842   | 0.7143    | 0.5556 | 0.6250 |
+| 0.45      | 0.7368   | 0.8333    | 0.5556 | 0.6667 |
+| 0.50      | 0.7368   | 0.8333    | 0.5556 | 0.6667 |
+| 0.55      | 0.6842   | 0.8000    | 0.4444 | 0.5714 |
+| 0.60      | 0.6842   | 1.0000    | 0.3333 | 0.5000 |
+| 0.65      | 0.5263   | 0.0000    | 0.0000 | 0.0000 |
+| 0.70      | 0.5263   | 0.0000    | 0.0000 | 0.0000 |
+| 0.75      | 0.5263   | 0.0000    | 0.0000 | 0.0000 |
+| 0.80      | 0.5263   | 0.0000    | 0.0000 | 0.0000 |
+| 0.85      | 0.5263   | 0.0000    | 0.0000 | 0.0000 |
+| 0.90      | 0.5263   | 0.0000    | 0.0000 | 0.0000 |
+| 0.95      | 0.5263   | 0.0000    | 0.0000 | 0.0000 |
 
-#### Summary
 
+### Comparison of performance on Pre vs Post-stained images
 
+| Comparison                     | Accuracy | F1-Score |
+| ------------------------------ | -------- | -------- |
+| Pre-stained Prediction  | 0.6087   | 0.2703   |
+| Post-stained Prediction | 0.7474   | 0.3441   |
 
-## 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]
+## Technical Specifications
 
-### Model Architecture and Objective
+### Model Architecture
 
-[More Information Needed]
+* EfficientNet-B7 backbone
+* Final Dense layer with sigmoid activation for binary classification
 
 ### Compute Infrastructure
 
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
+* **Hardware:** Tesla T4 GPU
+* **Software:** TensorFlow/Keras
 
-<!-- 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]
+## Environmental Impact
 
-## Model Card Contact
+* **Hardware Type:** Tesla T4
 
-[More Information Needed]
+---