Hi, instead of fine-tuning a pretrained model (like resnet34 that was trained on the 1000 classes of ImageNet), I would like to use the pretrained model without modification in order to get predictions through model.predict(img).
Note: I’ve already posted this question into another thread but I think it deserves its own one.
If it is easy to get the weights of a pretrained model with model = models.resnet34(pretrained=True) for example, how to get the labels of the 1000 ImageNet classes and their structure (for example: 0 is car, 1 is plant, etc.).
The fastai v1 way for that is creating a (Image)databunch which gives the labels from the stored data (images here) thanks to the data block API, and then build a learner with create_cnn(data, model) for example but I do not want to download the 138 GB of Imagenet training dataset
Thanks @jithinrocs. I rewrote the Pytorch code with the fastai v1 one in the following notebook.
Just one thing is missing: how can I export the model through model.export() (see tutorial on inference) as my model does not have a databunch?
Note: there is only one point to improve. In order to create the DataBunch data, I used single_from_classes which is now obsolete. If someone could give me the code to use instead, we could close this thread.
# Get transforms
tfms = [ , [crop_pad()] ] # tfms = get_transforms() is possible too
# Get databunch with the ImageNet classes
data = ImageDataBunch.single_from_classes(path, classes, tfms=tfms, size=224).normalize(imagenet_stats)
# Get and save learner
learn = Learner(data, model)
# Load learner
learn = load_learner(path)
# Get prediction
cat, indice, preds = learn.predict(img)
Eh, I think you might have found the one application where this function is still useful. I’ll leave the deprecation warning as I don’t want people to think it’s the right way to do inference (learn.export is) but we won’t remove the function.
Thanks Sylvain for leaving single_from_classes (although deprecated) as this solves the problem of creating a DataBunch (and then, a learner) from a list of classes but without having any data (the regular fastai v1 way is more to recover the labels from the data we have).