color palletes

20230322_color_palettes_34_1ep_19er.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fe273e8178b13d1379acbde490bdc41e04f1f4a78545aa18db11987c98cd2454
+size 87529003

app.py

@@ -7,11 +7,11 @@ import numpy as np
 
 def is_cat(x): return x[0].isupper()
 
-learn = load_learner('20230321_wood_furnishes_34_10ep_22er_good.pkl')
+learn = load_learner('20230322_color_palettes_34_1ep_19er.pkl')
 
 #categories = ('cool-colors', 'earth-tones', 'jewel', 'neutral-colors', 'pastel', 'warm-colors')
 
-categories = ('blackstain', 'brass', 'brushednickel', 'chinoiserie', 'chrome', 'copper', 'darkwood', 'glass', 'goldleaf', 'lightwood', 'marble', 'oiledbronze', 'rattan', 'silverleaf', 'stainlesssteel')
+categories = ('cool', 'earth', 'jewel', 'neutral', 'pastel', 'warm')
 def classify_image(img):
     pred, idx, probs = learn.predict(img)
     return dict(zip(categories, map(float, probs)))