I am developing an object detection model to detect ships using YOLO. I want to use the COCO dataset. Is there a way to download only the images that have ships with the ann
From what I personally know, if you're talking about the COCO dataset only, I don't think they have a category for "ships". The closest category they have is "boat". Here's the link to check the available categories: http://cocodataset.org/#overview
BTW, there are ships inside the boat category too.
If you want to just select images of a specific COCO category, you might want to do something like this (taken and edited from COCO's official demos):
# display COCO categories
cats = coco.loadCats(coco.getCatIds())
nms=[cat['name'] for cat in cats]
print('COCO categories: \n{}\n'.format(' '.join(nms)))
# get all images containing given categories (I'm selecting the "bird")
catIds = coco.getCatIds(catNms=['bird']);
imgIds = coco.getImgIds(catIds=catIds);
To download images from a specific category, you can use the COCO API. Here's a demo notebook going through this and other usages. The overall process is as follows:
Now here's an example on how we could download a subset of the images containing a person
and saving it in a local file:
from pycocotools.coco import COCO
import requests
# instantiate COCO specifying the annotations json path
coco = COCO('...path_to_annotations/instances_train2014.json')
# Specify a list of category names of interest
catIds = coco.getCatIds(catNms=['person'])
# Get the corresponding image ids and images using loadImgs
imgIds = coco.getImgIds(catIds=catIds)
images = coco.loadImgs(imgIds)
Which returns a list of dictionaries with basic information on the images and its url. We can now use requests
to GET
the images and write them into a local folder:
# Save the images into a local folder
for im in images:
img_data = requests.get(im['coco_url']).content
with open('...path_saved_ims/coco_person/' + im['file_name'], 'wb') as handler:
handler.write(img_data)
Note that this will save all images from the specified category. So you might want to slice the images
list to the first n
.