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imbalance across size of boats, and whether at port or at sea. This inspection reveals some class imbalances in the data, i.e. There is also a medium sized cluster to the left of the plot which are container ships, whilst the largest cluser (right) are mainly smaller ships. A cluster plot of the embeddings shows several discrete clusters, with a cluseter of boats by the shore highlighted. Using lightly.ai we can visualise the data in feature space, here with embeddings generated using the default resnet18-simclr backbone. Viewing the dataset healthcheck we can see this would result in the majority of images being stretched slightly since the median image size is 510x493 pixels. For pre-processing I resized all images to 640圆40 since this is the expected size by the YOLOv5s model, which is the smallest and fasted of the YOLOv5 models. This increased the training image set size threefold to approximately 1400 images. I applied three augmentations to the training images which are appriate for aerial imager: a horizontal flip and 2x rotations. The dataset on kaggle consist of 794 images, and on uploading to Roboflow I rebalanced the dataset into train/validation/test splits with 550/183/61 images respectively, equating to approximately 70%/23%/7%. YOLOv5 requires images in a specific annotation format, so to transform the annotations from Pascal VOC to YOLOv5 format I uploaded the dataset to Roboflow, which provides a number of handy features including dataset insights & versioning, data pre and post processing (resizing & augmentations), and the ability to export datasets in the required YOLOv5 format (or many other formats).
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Each ship is annotated with a bounding box in Pascal VOC (XML) format.įor this project I wished to use YOLOv5 to perform object detection, as this is a model that is easy to use and has good performance.
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Some images are captured with ships close to the shoreline, or with multiple clustered boats. These images are typically 30 or 50cm resolution, and generally consist of a range of sizes of ship/boat against the blank ocean background. The dataset used is the kaggle Ships in Google Earth which consists of images extracted from Google Earth. Google Mars Yes, you can explore Mars, too.Applying YOLOv5 to Kaggle Ships in Google Earth dataset. Read about the historical imagery in Google’s Lat Long blog.Ĥ. Historical information Google Earth has incorporated old images, like from San Francisco in the 1940s. It does not make a video, but is an easier way to share information. The tour will run just as you recorded it, but it can be interrupted by the user to look around. Sharing Tours to share content, you can create, narrate and share tours that others can open in Google Earth 5.0. Here’s a video with Sylvia Earle exploring the new features.ĭuke’s Pat Halpin contributed to Google Ocean and participated in the launch.Ģ. Google Ocean – the sea floor is now mapped, and data, pictures, videos, user-generated content are all now available (for example, you can check the the water temperature in the Florida Keys ( 71 F)).
GOOGLE EARTH 5.0 FEATURES DOWNLOAD
Download the new version and explore underwater as you have explored land.ġ.