Why is ImageNet so popular for transfer learning?
Models pre-trained on the ImageNet datasets have been the de-facto choice for many years now. Many popular reasons as to why people think that ImageNet is so effective for transfer learning are the following:
ImageNet is a truly large-scale dataset that contains over 1 million images, each of which has a ...
In general, many of the parameters you mentioned are called hyperparameters. All hyperparameters are user-adjusted (or user-programmed) in training phase. Some hyperparameters are:
activation functions etc.
To answer your (a) part of your question, there are obsiously many frameworks and libraries, for ...
The .weights seems to be the extension for a framework called "darknet". You can read .h5 files with Keras.
However, if you really want to build an object detection framework, there is no necessity to stick to the darknet's .weights files. There are many pretrained models on the web. Or else you could fine-tune a pre-trained ImageNet model in Keras,...
To answer the question in the title, your enclosed method is a valid way to use 2d convs after a flattened feature vector. However, the bad results you experience could come from the structure of your model or from the way you train it. Regarding you last question, it is very hard to give you an advice without knowing your intentions in detail. Regardless, ...
I think you should use Keras embedding layer. It will be too easier than what you are doing.
Create Embedding Matrix
add matrix to embedding layer while building model.
You will find detailed article
Did you mean:
How do you use a pre-trained BERT model in a feature-based setting
to get pre-trained word contextual embeddings?
Here is the BERT paper. I highly recommend you read it.
Firstly, by sentences, we mean a sequence of word embedding representations of the words (or tokens) in the sentence. Word embeddings are the vectors that you mentioned,...
I am not sure if a pretrained machine learning model is actually protected by copyrights or not. Copyright protection exists to protect the creators of creative works from having their work "stolen", and I am not sure if training a ML model is an act of creativity.
That said, assuming that a pretrained ML model is actually protected by copyrights, then it ...