This is actually pretty reasonable, but since I use valid_pct in databuilder, I don’t know where my valid folder is. But I’ve got top losses:
UPDATE:
I’ve found a way to get predictions from valid_pct and there seems to be no problems:
for resim in data.valid_ds.items[:10]:
model = learn.model
model = model.cuda()
img = open_image(resim)
prediction = learn.predict(img)
ad = os.path.basename(resim)
ad2 = os.path.join(‘/content/content/abece/’, ad[8], ad)
display(Image(filename=ad2))
print(prediction[0])
print(resim)
output:
ANOTHER UPDATE:
It may be indeed a normalization problem:
from matplotlib import image
from matplotlib import pyplot
trained_image = image.imread(‘/content/content/abece/D/cikar_D_D-8231.jpg’)
test_image = image.imread(‘/content/content/test/test1.jpg’)data = asarray(test_image)
data1 = asarray(trained_image)print(data)
print(“\n”)
print(data1)
output:
[[249 253 253 244 … 241 238 238 239]
[229 242 252 251 … 243 241 239 239]
[238 247 255 255 … 251 254 253 251]
[235 204 167 148 … 189 208 227 247]
…
[212 162 95 53 … 120 170 205 233]
[245 233 218 210 … 226 238 245 249]
[236 238 242 250 … 250 249 247 246]
[233 232 231 234 … 241 238 238 237]]
[[255 255 255 255 … 255 255 255 255]
[255 255 255 255 … 255 255 255 255]
[255 255 255 255 … 255 255 255 255]
[255 255 255 255 … 255 255 255 255]
…
[255 255 255 255 … 255 255 255 255]
[255 255 255 255 … 255 255 255 255]
[255 255 255 255 … 255 255 255 255]
[255 255 255 255 … 255 255 255 255]]