OceanCV
/

Pinniped_Model

@@ -4,192 +4,53 @@ base_model:
 - Ultralytics/YOLO11
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. --> This model detects California Sea Lions, and Northern Fur Seals, and a veriety of age classes. It is designed to help streamline population counts and help scientists study Pinniped population health.
-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).
-## Model Details
 ### Model Description
-<!-- Provide a longer summary of what this model is. --> This model uses images from an aerial survey of the california coast by NOAA's Marine Mammal Lab. It uses YOLOv11 as a base model for inferences. Model weights are updated to the most recent model run.
-- **Developed by:** [Christopher Moon]
 ## Dataset description
-<!-- Provide the basic links for the model. --> NOAA Marine Mammal Lab Aerial surveys Summer 2024. The images are large aerial images with varying substrates and pinniped species. All images annotated by hand and resized to 640x640 by me. Albumentations are the default ones set by Yolov11.
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
 ## Model Selection
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> I used Yolov11 because it was the most up to date Yolo at the time, using object detection.
-### Model Assesment
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> /Users/christophermoon/Downloads/confusion_matrix_normalized (2).png
-![F1-Assesment](https://huggingface.co/OceanCV/Pinniped_Model/raw/main/F1_curve%20(4).png)
-![Normalized confusion matrrix](https://huggingface.co/OceanCV/Pinniped_Model/blob/main/confusion_matrix_normalized%20(2).png)
-[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]

 - Ultralytics/YOLO11
 ---
+# Scientific Context
+<!-- Provide a quick summary of what the model is/does. --> Pinnipeds are abundent along the entire west coast, their populations and destributions can tell us about the health of our oceans and coastlines. Studying Pinniped is important because they are seentinals of the ocean, they are indicators of a helathy ocean and can help us better understand ecoosystems and fisheries that arre critical to many communities.
 ### Model Description
+<!-- Provide a longer summary of what this model is. --> This model detects California Sea Lions, and Northern Fur Seals, and a veriety of age classes. It is designed to help streamline population counts and help scientists study Pinniped populations. This model uses images from an aerial survey of the California coast by NOAA's Marine Mammal Lab. It uses YOLOv11 as a base model for inferences. Model weights are updated to the most recent model run.
 ## Dataset description
+<!-- Provide the basic links for the model. --> NOAA Marine Mammal Lab Aerial surveys conducted Summer 2024. The images are large aerial images with varying substrates and pinniped species. All images annotated by hand and resized to 640x640 by me. Albumentations are default ones set by Yolov11.
 ## Model Selection
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> I used Yolov11 because it was the most up to date Yolo at the time. I used object detectiono because it would allow me to both collect data no population numbers for multiple classes, but it also allows me to see destribtions on specific beaches and landforms.
+## Model implementation
+'''python
+### Model Assesment
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+![F1-confidence curve](https://huggingface.co/OceanCV/Pinniped_Model/raw/main/F1_curve%20(4).png)
+F1-Confidence Curve:
+F1-confidence curves shows us the ideal balance between precision and recall and how that changes with confidence.  My model shows decent F1 scores for CU_non_pup and ZC_non_pup around 0.3 confidence but struggles with all of the other classes.
+![Normalized confusion matrrix](https://huggingface.co/OceanCV/Pinniped_Model/blob/main/confusion_matrix_normalized%20(2).png)
+Normalized Confusion Matrix
 [More Information Needed]
+### Model Use Case
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->