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README.md

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 license: apache-2.0
-language:
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+---
+
+# Model Card for AntID-Tutor Genus Classifier
+
+This model card documents the EfficientNet-B4 model used by the AntID Tutor platform to classify ant genus from user-submitted images. It supports educational outreach in myrmecology and assists students in exploring ant biodiversity.
+
+## Model Details
+
+### Model Description
+
+This model is a fine-tuned EfficientNet-B4 convolutional neural network trained to classify ant images into one of 42 known genera. It powers the genus identification feature in the AntID Tutor application.
+
+* **Developed by:** Terry Stilwell
+* **Funded by:** Old Dominion University (as part of CS895 AI for Health & Life Sciences project)
+* **Shared by:** tstilwel ([https://huggingface.co/tstilwel/antID-tutor-genus](https://huggingface.co/tstilwel/antID-tutor-genus))
+* **Model type:** Image classification (CNN)
+* **Language(s) (NLP):** N/A (Vision task)
+* **License:** Apache-2.0
+* **Finetuned from model:** torchvision.models.efficientnet\_b4 (pretrained on ImageNet)
+
+### Model Sources
+
+* **Repository:** [https://github.com/tstilwell91/cs895\_project\_ants](https://github.com/tstilwell91/cs895_project_ants)
+* **Demo:** [http://localhost:5002](http://localhost:5002) (local Flask web app)
+
+## Uses
+
+### Direct Use
+
+This model is used in a web-based learning platform where students upload ant specimen images to receive genus-level predictions along with interpretive educational content.
+
+### Downstream Use
+
+Can be embedded into broader biodiversity platforms or entomological mobile apps with species distribution mapping or educational content.
+
+### Out-of-Scope Use
+
+* Not suitable for species-level identification.
+* Not intended for medical, pest control, or legal decision-making.
+* May not generalize well to highly distorted or poorly illuminated images.
+
+## Bias, Risks, and Limitations
+
+The model is trained on a curated subset of labeled ant images and may underperform on genera not present in the training set. Misclassification may occur with rare, damaged, or low-resolution samples.
+
+### Recommendations
+
+* Use high-quality, centered images.
+* Combine predictions with expert validation.
+* Future work may include species-level training and regional ecological integration.
+
+## How to Get Started with the Model
+
+You can test the model locally on the ODU Wahab cluster using the included inference script:
+
+```bash
+# Request GPU node (if needed)
+salloc -p gpu --gres gpu:1
+
+# Load PyTorch module
+module load container_env pytorch-gpu/2.5.1
+
+# Run inference
+cd inference/genus
+crun -p ~/envs/myrmecid python inference.py \
+  --image casent0901862_h_1_med.jpg \
+  --model genus_best_model_full.pth \
+  --classes classes.json
+```
+
+## Training Details
+
+### Training Data
+
+* Curated ant images with labeled genus from AntWeb and ODU image sets.
+* 42 ant genera.
+* Balanced sampling and augmentations applied (resizing, normalization).
+
+### Training Procedure
+
+* Framework: PyTorch
+* Architecture: EfficientNet-B4
+* Loss: Cross-Entropy
+* Optimizer: Adam
+* Epochs: 100
+* Batch size: 64
+* Learning Rate: 0.0005
+
+## Evaluation
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+Held-out validation set from the same sources as training.
+
+#### Factors
+
+Image quality, angle (head/dorsal/profile), lighting.
+
+#### Metrics
+
+* Top-1 Accuracy
+* Confusion Matrix
+* SHAP Visual Explanations
+
+### Results
+
+* Accuracy: \~92%
+
+## Environmental Impact
+
+* **Hardware Type:** A100 GPU (university cluster)
+* **Cloud Provider:** None (on-prem HPC)
+* **Compute Region:** USA (Old Dominion University)
+
+## Technical Specifications
+
+### Model Architecture and Objective
+
+* EfficientNet-B4 with modified classifier head
+* Objective: multi-class genus prediction (42 classes)
+
+### Compute Infrastructure
+
+* **Hardware:** NVIDIA A100 GPU
+* **Software:** PyTorch 2.1.0, torchvision, Python 3.9
+
+## Citation
+
+**APA:**
+Stilwell, T. (2025). *AntID Tutor: A Genus-Level Classifier for Ant Images Using EfficientNet-B4*. Old Dominion University.
+
+**BibTeX:**
+
+```bibtex
+@misc{stilwell2025antid,
+  author = {Terry Stilwell},
+  title = {AntID Tutor: A Genus-Level Classifier for Ant Images Using EfficientNet-B4},
+  year = {2025},
+  howpublished = {\url{https://huggingface.co/tstilwel/antID-tutor-genus}}
+}
+```
+
+## Model Card Contact
+
+* Terry Stilwell
+* GitHub: [https://github.com/tstilwell91](https://github.com/tstilwell91)
+* Email: [tstilwel@cs.odu.edu](mailto:tstilwel@cs.odu.edu)