I’m working on Bear Classification (as shown in chapter 2), but instead using search_images_bing
, I’m using search_images_ddg
(I think I saw them doing in Video). After training for cleaning data, I plotted a confusion matrix and the results were surprisingly really bad. Has anyone using search_images_ddg
found similar results? When I do a web search using ddg, the results are not bad. But I guess API results are not good enough?
bear_types = "grizly", "black", "teddy"
path = Path("bears")
# create a directory named bears. exist_ok=False; means don't raise exception if
# directory already exists
path.mkdir(exist_ok=True)
for bear_type in bear_types:
dest = path/bear_type
dest.mkdir(exist_ok=True)
images = search_images_ddg(f"{bear_types} bear")
download_images(dest, urls=images)
bears = bears.new(
item_tfms=RandomResizedCrop(224, min_scale=0.5),
batch_tfms=aug_transforms())
dls = bears.dataloaders(path)
learn = vision_learner(dls, resnet18, metrics=error_rate)
learn.fine_tune(4)