I am following (coding along) Dusty’s tutorial on image classification jetson-inference/pytorch-cat-dog.md at master · dusty-nv/jetson-inference · GitHub, but i get a really strange behaviour while testing it.
So, i did train the resnet18 on the dataset as he mentioned in the video, testing it on the 100 cats dataset provides the expected results, but when testing on the 100 dogs dataset, all of them are classified as cats.
At the beginning, I thought this happens because i trained the network only for 1 epoch, then i retrained it for anoter 10 epochs and the result is still the same, all dogs are classified as cats.
Any idea why? where to look into?
this is for instance the last dog in the testing set
[image] loaded ‘data/cat_dog/test/dog/100.jpg’ (500x391, 3 channels)
class 0000 - 0.704574 (cat)
class 0001 - 0.295426 (dog)
imagenet: 70.45742% class #0 (cat)
[image] saved ‘data/cat_dog/test_output_dog/99.jpg’ (500x391, 3 channels)