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5.3 Writing a Model Card¶
A model card provides essential information about a machine learning model, including its intended use, performance metrics, limitations, and potential biases. It promotes transparency and helps users understand whether the model is suitable for their specific use case. Here’s a guide on how to write an effective model card, using the NOAA AFSC Marine Mammal Lab YOLO26 Ice Seal Object Detection Model as an example.
Model Card Structure¶
1. Model Overview¶
Model Name: NOAA AFSC Marine Mammal Lab YOLO26 Ice Seal Object Detection Model
Description:
Accurate identification of ice-associated seals in aerial imagery is essential for monitoring population dynamics and assessing ecological trends in Arctic and sub-Arctic environments. To facilitate the fast and accurate detection of these seals, I have developed a deep learning-based object detection model utilizing YOLO26n, a lightweight neural network architecture optimized for high-performance image analysis. The model, comprising 319 layers and 2,591,400 parameters, was trained on a diverse dataset containing 7,671 high-resolution images, which were tiled into 30,684 smaller images to enhance feature recognition.
2. Intended Use¶
Primary Use Case: Object detection of ice-associated seals in aerial imagery.
Specific Applications:
Monitoring population dynamics.
Assessing ecological trends in Arctic and sub-Arctic environments.
ROI detector for imaging surveys.
3. Factors¶
Relevant Factors:
Species: Bearded, ribbon, ringed, and spotted seals (both pup and adult stages).
Imagery Conditions: High-resolution aerial images.
4. Metrics¶
Overall mAP: 0.4833
Class-Specific mAP:
Bearded Seals: 0.9
Ringed Seals: 0.833
Ribbon Seals: 0.464
Precision: 0.76783
Recall: 0.43806
5. Evaluation¶
Dataset Details:
7,671 high-resolution images tiled into 30,684 smaller images.
8,948 total annotations.
Training Configuration:
Image Size: 1024 × 1024
Batch Size: 113
Optimizer: SGD (lr=0.01, momentum=0.9)
Hardware: NVIDIA A100 GPU
Augmentations:
Blur, MedianBlur, ToGray, CLAHE
6. Recommendations¶
Optimal Performance: Detecting ringed, spotted, and bearded seals.
Underrepresented Classes: Misclassification is more common, though false negatives remain rare.
Review Pipeline: Intended for manual verification in certain workflows.
Downstream Tasks: Clustering, two-shot fine-tuning, or in-depth analytics.
Real-Time Inference: Best to use the YOLO model.
7. Caveats and Limitations¶
Pup Classes: Consistently underperformed due to smaller size and limited representation.
Misclassification: Underrepresented classes tend to be misclassified rather than completely missed.
8. Bias, Risks, and Harms¶
Potential Biases:
Imbalanced class distribution may affect performance on underrepresented classes.
Mitigation Strategies:
Rebalance Train/Val splits.
Ensure enough representative classes in the dataset.
9. Environmental Impact¶
Compute Location: Google Cloud Platform (GCP), northamerica-northeast1 region
Carbon Efficiency: 0.03 kgCO₂eq/kWh
Hardware: A100 PCIe 40/80GB (TDP of 250W)
Compute Duration: ~10 hours
Total Emissions: ~0.07 kgCO₂eq
10. How to Use¶
Inference Code Example:
from ultralytics import YOLO # Load your custom-trained weights model = YOLO("path/to/NOAA_AFSC_MML_Iceseals_31K.pt") # Inference on a still image results_image = model.predict( source="path/to/your_image.jpg", conf=0.01, # Confidence threshold iou=0.45, # IoU threshold device=0 # Use GPU 0 if available; set to 'cpu' if no GPU )
11. Dataset and Preprocessing Details¶
Source Imagery:
Original resolution: 6048 × 4032
Total source images: 7,671 (including 156 null examples)
Resolution range: ~12.21 MP to ~24.39 MP
Tiling:
Each source image was split into 4 tiles (2 rows × 2 columns) at 1024 × 1024.
Post-tiling: 30,684 images.
Annotations:
8,948 total annotations.
~1.2 annotations per image on average.
Class Distribution:
Class Name Total Count Training Count Validation Count Test Count ringed_seal 3180 2190 674 316 bearded_seal 1922 1392 359 171 spotted_seal 1662 1142 344 176 unknown_seal 812 585 153 74 bearded_pup 420 300 77 43 unknown_pup 392 275 63 54 spotted_pup 238 174 46 18 ribbon_seal 232 154 45 33 ringed_pup 54 37 14 3 ribbon_pup 36 28 5 3
12. Training Configuration Details¶
Parameters:
Layers: 319
Parameters: 2,591,400
Gradients: 2,591,384
GFLOPs: 6.4
13. Metrics (Epoch 64)¶
| epoch | time | train/box_loss | train/cls_loss | train/dfl_loss | metrics/precision(B) | metrics/recall(B) | metrics/mAP50(B) | metrics/mAP50-95(B) | val/box_loss | val/cls_loss | val/dfl_loss | lr/pg0 | lr/pg1 | lr/pg2 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 64 | 23230.9 | 1.34616 | 1.18894 | 0.89475 | 0.76783 | 0.43806 | 0.4671 | 0.30454 | 1.37059 | 1.77372 | 0.90735 | 0.00993763 | 0.00993763 | 0.00993763 |
Key Considerations¶
Transparency: Provide as much detail as possible about the model and its development process.
Accessibility: Use clear and straightforward language to make the model card accessible to a broad audience.
Completeness: Cover all relevant aspects of the model, including its intended use, performance, limitations, and ethical considerations.
By following this structure, you can create a comprehensive model card that promotes transparency and enables users to make informed decisions about using your model.