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I would recommend using pretrained text extractors, like amazon textract or tesseract, and then train a network on the extracted text. The solution is going to be simpler and the learning is going to be easier for the NN.


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Just like all other Class, you have different methods i.e.fit, transform, fit_transform fit(self, X[, y]) Fit the model with X. fit_transform(self, X[, y]) - Fit the model with X and apply the dimensionality reduction on X. transform(self, X) - Apply dimensionality reduction to X. X is projected on the first principal components previously extracted from a ...


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Referring to a previous question, there is no reason to tackle imbalance unless your model is not learning properly with the imbalanced dataset. Besides, 1:7 is not that big of an imbalance.


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I suppose you want something like this: image_generator = ImageDataGenerator().flow_from_directory('test_data_path', target_size=(224, 224), shuffle=False) true_labels = image_generator.classes pred_probs = model.predict(image_generator) preds = pred_probs.argmax(axis=-1) print (sum(preds[:,0] ==true_labels)/len(true_labels))


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In absolute, it is one CNN wich takes 3 inputs images. You could see it as 3 separate features extractors (CNN) which merge their results while trained together. The author obtain 3 2D input from a 3D images by keeping 3 2D images; one in each plane. Each of these images has multiple channel because they slices the input among the respective axis. It is ...


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It reminds me of this question, the training loss is decreasing faster than the validation loss. I understand there is some overfitting, as the model is learning some patterns that are only in the training set, but the model is still learning some patterns that are more general, as the validation loss is decreasing as well. To me it would be more of an issue ...


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YOLO's network was trained to run on 608x608 images. If you are testing this data on a different size image--for example, the car detection dataset had 720x1280 images--this step rescales the boxes so that they can be plotted on top of the original 720x1280 image. Since you are using a pre-trained model. It will resize your image to the size it was trained ...


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There are a few points that I would like to mention and I believe that will serve as an answer to the question - 1. SVM work only the way we know i.e. finding the maximum margin support. So it will treat the image like a "1 x N" dimensional data just like any other data. 2. It performs well with sparse high dimension data (when data volume is small)...


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model.predict(img) requires a batch axis, i.e. img needs to have a shape of (BS, H, W, C). Try model.predict(np.expand_dims(img,0)).


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A quick look (this post, for example) suggests that to use SVM for image classification, you need to extract features beforehand, and not run it directly on the image data.


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It is possible to build that kind of a CNN. It is important to maintain uniform distribution for both the classes ('cat' and 'not cat'). That is you should have an almost equal number of samples for each of these classes to avoid biasing your model to the 'non-cat' class just because it has huge number of examples. The number of non-cat examples can be ...


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It will not It's about using a model which was trained on thousands of Classes on Millions of images of ImageNet. Chances are very high that most of the classes you have in your dataset is already there. In general, if you trained a model on a super-class (e.g. vehicle), then you may reuse it to classify the Car variant(Utilizing its initial layers). Point ...


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