Hi, thanks for your code, it helps me a lot. But it also got some problems for a newbie like me. Although I make the code run successfully now, I also make a lot of compromises to some errors. I combined the code from classical_training.ipynb and my_first_few_shot_classifier.ipynb.

I post all my code step by step and point out the problems I met. I am running Windows10. The environment is created by Anaconda. Cuda10.2, Cudnn 7.0, PyTorch 1.10.1

At last, great thanks for your code again. Let's discuss this together.

I want to train this model on custom data but I did not understand the split for CUB and I could not even get documentation on EasySet ? do you know where it is ? i just have 2 classes in my data btw

Hello, i'm new to few shot learning and want to make sure about classical training. When the backbone, after training, is evaluated with a picked method on new set of data, does the method get adjusted or learn from the new data?

Problem Thanks for your sharing about FSL, there is one problem: When I finished the tutorial 'Discovering Prototypical Networks' , I want to use my own photo data to build test_set, how can I do that and How should I construct my data's structure

The intention of adding this predictor is to help those who need to use the trained network for an image, obtaining as a return the inferred class and the tensor with the mean of Euclidean distances. Tests were performed with the PrototypicalNetworks and MatchingNetworks for a 5-way 6-shot dataset.

Problem I am trying to train backbone using classical training, and use Finetune in methods to fine-tune the model by episodic_training.ipynb. How should I implement it? I see that the episodic_training.ipynb you wrote has fixed the parameters for backbone, but when I import the pre-trained model for fintune, it does not work properly. Another question is, how much is n_validation_tasks generally set to? Is there a standard? Because the setting of this hyperparameter will affect the result. I look forward to your answer.

convolutional_network = resnet50(num_classes=2).to(DEVICE) convolutional_network.load_state_dict(torch.load('save_model/resnet50.pt')) few_shot_classifier = Finetune(convolutional_network).to(DEVICE)

1. The number of query set of each class has to be equal? Can i use random number of images of each class.
2. Training epoch by epoch still can be used in few-shot learning?

Problem (i am greenhand in this field, so i may ask a simple and silly question. Sorry for that) when i run the front part of "my_first_few_shot_classifier.ipynb": The following error messages appear：

How can we help How to solve this problem? I haven't found a practical solution

So i have trained your episodic training notebook on custom data. but I had a question about how would we view the output I got the accuracy also but how would we view the classification

Hi, Thanks for making such a simple and beautiful library for Few-Shot Learning. I have a query when we run a particular cell from your notebook for training meta-learning model, does it also train the ResNet18 Model on the given Dataset for generating a better representation of Feature Images like we do it while we do transfer learning when we train classifier model on our custom dataset using Imagenet pre-trained parameters or Does it only trains Prototype network?

Please, clarify this doubt. Thanks again.

Hi Thank you for your great work and sharing it with everyone.

I want to implement few shot learning for a task that I have. where I have collected few samples(10) each for both positive and negative class. How do I train the model on these novel classes using my custom dataset.

Thank you for your help

Hi,

I am using your classical and episodic Training notebooks, they are very helpful for my project , although I want to try different backbones like EfficientNet. I am new in this so do you have any idea if I can use different backbone than ResNet if yes what changes I will have to consider in the code?

Thanks in adavance

Hello, Thank you very much for your amazing work, its very helpful.

I have one question on getting prediction on custom dataset, so basically I am using Easyset for my custom dataset and classical training notebook and I want to see prediction/classification also , for example from which class my test image belongs to. I hope my quetsion is clear to you. Thanks in advance

    I created an example that might help you.

import torchvision.transforms as tt
import torch
from torchvision.datasets import ImageFolder
from easyfsl.methods import FewShotClassifier
from torch.utils.data import DataLoader

class FewShotPredictor :
    """

        This class aims to implement a predictor for a Few-shot classifier.

        The few shot classifiers need a support set that will be used for calculating the distance between the support set and the query image.

        To load the support we have used an ImageFolder Dataset, which needs to have the following structure:

        folder:
          |_ class_name_folder_1:
                 |_ image_1
                 |_  …
                 |_ image_n
          |_ class_name_folder_2:
                 |_ image_1
                 |_  …
                 |_ image_n

        The folder must contain the same number of images per class, being the total images (n_way * n_shot).

        There must be n_way folders with n_shot images per folder.

    """

    def __init__(self ,
                 classifier: FewShotClassifier,
                 device,
                 path_to_support_images,
                 n_way,
                 n_shot,
                 input_size=224):

        """
            :param classifier: created and loaded model
            :param device: device to be executed
            :param path_to_support_images: path to creating a support set
            :param n_way: number of classes
            :param n_shot: number of images on each class
            :param input_size: size of image

        """
        self.classifier = classifier
        self.device = device

        self.predict_transformation = tt.Compose([
            tt.Resize((input_size, input_size)),
            tt.ToTensor()
        ])

        self.test_ds = ImageFolder(path_to_support_images, self.predict_transformation)

        self.val_loader = DataLoader(
            self.test_ds,
            batch_size= (n_way*n_shot),
            num_workers=1,
            pin_memory=True
        )

        self.support_images, self.support_labels = next(iter(self.val_loader))



    def predict (self, tensor_normalized_image):
        """

        :param tensor_normalized_image:
        Example of normalized image:

            pil_img = PIL.Image.open(img_dir)

            torch_img = transforms.Compose([
                transforms.Resize((224, 224)),
                transforms.ToTensor()
            ])(pil_img)

            tensor_normalized_image = tt.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])(torch_img)[None]


        :return:

        Return

        predict = tensor with prediction (mean distance of query image and support set)
        torch_max [1] = predicted class index

        """

        with torch.no_grad():
           self.classifier.eval()
           self.classifier.to(self.device)
           self.classifier.process_support_set(self.support_images.to(self.device), self.support_labels.to(self.device))
           pre_predict = self.classifier(tensor_normalized_image.to(self.device))
           predict = pre_predict.detach().data
           torch_max = torch.max(predict,1)
           class_name = self.test_ds.classes[torch_max[1].item()]
           return predict, torch_max[1], class_name

#49

Originally posted by @diego91964 in https://github.com/sicara/easy-few-shot-learning/issues/17#issuecomment-1157091822

Good morning guys and many thanks for the awesome and very helpful code and the effort put to achieve that I have a question regarding novel image class prediction: Is there a way to calculate as in 'classical' classification the percentage/probability of a novel image to belonging to each class? Do you believe a softmax maybe at the 'return predict, torch_max[1], class_name' at the return tensor would have a meaning?

Thanks in advance

Problem I am new to FSL and have a simple problem in my scientific domain that I thought I would try as a learning example. I am trying to perform classical training for a 5 shot 2 way problem. When I am running the code from the tutorial notebook as it is after using EasySet to create a custom data object, I am getting the following error when I encounter the validation epoch during my training:

ValueError : Sample Larger than population or is negative

Considered solutions I've tried changing the batch size and n_workers so far, and neither have worked

How can we help I can't figure out what is going wrong here. I am very new to machine learning and would love to have your help in any way possible!

Hi. Thank you for your great work and sharing it with everyone.

I have a question. For custom datasets, how to divide the class? (train, val, test) Randomly select some classes as the training set, or? Do you have any tricks?

Hi @ebennequin, Thanks for this elegant code base, some questions(can be a feature request)

1. Can we add new backbones like (ViT, densenet, Convnext etc...)?
2. Building functionalities for model deployments?