Update README.md

README.md

@@ -18,7 +18,7 @@ Behavioral observation and data collection is an integral part of maintaining an
 
 Currently, there are a few major products that utilize machine learning methods for behavioral observation in captivity. These models are insightful, but lack the accessibility and customization of an open source model. 
 
-My dataset, consisting of Southern Sea Otter enclosure live feeds, will serve as an example of how scientists could use my model to gain behavioral information. From the model outputs, users can track, county, and study long term species patterns across multiple enclosures. These results can then aid in making care-related decisions.
+My dataset, consisting of Southern Sea Otter enclosure live feeds, will serve as an example of how scientists could use my model to gain behavioral information. From the model outputs, users can track, count, and study long term species patterns across multiple enclosures. These results can then aid in making care-related decisions.
 
 
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@@ -82,15 +82,15 @@ https://huggingface.co/OceanCV/Southern_Sea_Otter_Tracking/blob/main/F1_curve.pn
 ![F1 graph from final run](https://huggingface.co/OceanCV/Southern_Sea_Otter_Tracking/resolve/main/F1_curve.png?download=true)
 
 ### Object Detection Model Output
-My final object detection output video was a key metric in assessing the performance of my model. I bounced between looking at the output video, assessing how accurate the bounding boxes and identifications were, and rerunning the model with modified parameters. My final model output was successful at identifying sea otters in both land and water, with minimal misclassifications or missed detections. The final video can be found on my [repository](https://huggingface.co/OceanCV/Southern_Sea_Otter_Tracking/resolve/main/object_detection_final.avi?download=true)
+My final object detection output video was a key metric in assessing the performance of my model. I bounced between looking at the output video, assessing how accurate the bounding boxes and identifications were, and rerunning the model with modified parameters. My final model output was successful at identifying sea otters in both land and water, with minimal misclassifications or missed detections. The final video can be found in my [repository.](https://huggingface.co/OceanCV/Southern_Sea_Otter_Tracking/resolve/main/object_detection_final.avi?download=true)
 
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 # Model Use-case
-**Example Proposal:** Utilize the Behavioral Tracking Model to track and analyze the movement patterns of Sea Otters between water, land, and secluded zones to assess their interactions with their enclosure.
+**Example Proposal:** Utilize the Behavioral Tracking Model to track and analyze the movement patterns of sea otters between water, land, and secluded zones to assess their interactions with their enclosure.
 
 **Example Hypothesis:** Southern Sea Otters will congregate in the water zone most frequently and for the most time, and will rarely enter the seclusion zone.
 
-**Example Impact:** Researchers found that sea otters spend the majority of their resting time on land, hidden behind objects (rocks, toy structures) and in the seclusion zone. They then modified the enclosure to provide more private spaces, such as large toys and structures, to provide more secluded spaces. 
+**Example Impact:** Researchers found that sea otters spend the majority of their resting time on land, hidden behind objects (rocks, toy structures, etc.) and in the seclusion zone. They then modified the enclosure to provide more private spaces, such as large toys and structures, to create more secluded spaces. 
 
 **Model Justification:** My model would be a reasonable tool for initial data collection, providing species interaction data across zones, animal frequency, and areas of interest within the enclosure.