Update README.md
Browse files
README.md
CHANGED
@@ -17,20 +17,20 @@ datasets:
|
|
17 |
|
18 |
# Scientific Context
|
19 |
|
20 |
-
|
21 |
|
22 |
|
23 |
### Model Description
|
24 |
|
25 |
-
|
26 |
#
|
27 |
## Dataset description
|
28 |
|
29 |
-
|
30 |
#
|
31 |
### Model Selection
|
32 |
|
33 |
-
|
34 |
#
|
35 |
### Model implementation
|
36 |
|
|
|
17 |
|
18 |
# Scientific Context
|
19 |
|
20 |
+
Pinnipeds are abundent along the entire west coast, their populations and destributions can tell us about the health of our oceans and coastlines. Studying Pinnipeds is important because they are sentinals of the ocean, they are indicators of a healthy ocean and can help us better understand ecoosystems and fisheries that are critical to many communities.
|
21 |
|
22 |
|
23 |
### Model Description
|
24 |
|
25 |
+
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.
|
26 |
#
|
27 |
## Dataset description
|
28 |
|
29 |
+
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.
|
30 |
#
|
31 |
### Model Selection
|
32 |
|
33 |
+
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.
|
34 |
#
|
35 |
### Model implementation
|
36 |
|