The project is for OpenAI CLIP model learning, with diverse demo and test.
- clip-vit-large-patch14
precision recall f1-score support
airplane 0.9855 0.9490 0.9669 1000
automobile 0.9728 0.9640 0.9684 1000
bird 0.9034 0.9540 0.9280 1000
cat 0.9158 0.9460 0.9306 1000
deer 0.9319 0.9580 0.9448 1000
dog 0.9578 0.9540 0.9559 1000
frog 0.9943 0.8700 0.9280 1000
horse 0.9454 0.9870 0.9658 1000
ship 0.9807 0.9640 0.9723 1000
truck 0.9554 0.9850 0.9700 1000
accuracy 0.9531 10000
macro avg 0.9543 0.9531 0.9531 10000
weighted avg 0.9543 0.9531 0.9531 10000
- clip-vit-base-patch32
precision recall f1-score support
airplane 0.9504 0.9010 0.9251 1000
automobile 0.8785 0.9760 0.9247 1000
bird 0.8124 0.8880 0.8485 1000
cat 0.8190 0.8600 0.8390 1000
deer 0.9341 0.7650 0.8411 1000
dog 0.8508 0.8840 0.8671 1000
frog 0.9699 0.7740 0.8610 1000
horse 0.8127 0.9760 0.8869 1000
ship 0.9446 0.9550 0.9498 1000
truck 0.9688 0.9010 0.9337 1000
accuracy 0.8880 10000
macro avg 0.8941 0.8880 0.8877 10000
weighted avg 0.8941 0.8880 0.8877 10000
- clip-vit-large-patch14
precision recall f1-score support
airplane 0.9631 0.9660 0.9646 1000
automobile 0.9750 0.9760 0.9755 1000
bird 0.9412 0.9280 0.9345 1000
cat 0.8924 0.9040 0.8982 1000
deer 0.9266 0.9340 0.9303 1000
dog 0.9291 0.9170 0.9230 1000
frog 0.9467 0.9600 0.9533 1000
horse 0.9757 0.9630 0.9693 1000
ship 0.9770 0.9780 0.9775 1000
truck 0.9750 0.9750 0.9750 1000
accuracy 0.9501 10000
macro avg 0.9502 0.9501 0.9501 10000
weighted avg 0.9502 0.9501 0.9501 10000