SMEDIUM MEME HOW TO
SMEDIUM MEME GENERATOR

SMEDIUM MEME GENERATOR

The language module is going to use fasttext embeddings as input, computed as the text_transform in our data generator (we'll keep the embeddings fixed for simplicity, although they are fit to our training data). For the language and vision module definitions, see the _build_model method.Then we'll concatenate these feature representations and treat them as a new feature vector, and send it through a final fully connected layer for classification. In our LanguageAndVisionConcat architecture, we'll run our image data mode through an image model, taking the last set of feature representations as output, then the same for our language mode.Refer to this guide and try to implement it. To create this custom fusion we can once again thank to PyTorch as it allows us to create custom modules according to our respective need.

The above design is called mid-level concat fusion. So in this approach we will be extracting features from our image_transform and text_transform and will be fusing them together thus making 1 feature vector and further passing it as input to our NN classifier for prediction. The multimodal features are then passed through a classifier. In mid-level fusion by concatenation, input data modes pass through their respective modules after which their features are concatenated. Image_transform was introduced above, and text_transform will be the 'sentence vector' created by our fastText model. We want the dataset to return data ready for model input, that means we will need to prepare : Lucky for us PyTorch already has a built-in Dataset Class, You can go through this short tutorial to understand the architecture of this class.Īmazing isn’t it ? 🧙‍♂️You have no idea what else PyTorch has to offer! Its the combination of both to understand the meaning behind it. Now we have to independently work on both image and text data as the solution can not solely depend on only image or text. The images given in the dataset are of different size we will be using ansform to resize all the given image to dim (224*224*3) as different size images are generally difficult to process.Also we can make our own OCR model to do the same. We will be using some of the best OCR tools available online to grab the text from the given image. Above is a given meme image in order to work on this, we first have to extract the text from the given image.Image taken from Facebook AI Meme Classification challenge dataset. Without any further delay lets see how we are going to approach this problem! ✌ Welcome to this field of enhancement 🐱‍🚀, Chronologically we have applied some of the best practiced techniques available at global platform for our research field.

SMEDIUM MEME HOW TO

Data Analyst from different part of the worlds are trying to solve the problem of identifying such memes.Īlthough the problem of Meme Classification can be Multiclass but for simplicity and to give you a glimpse of how to handle vision and language data we will be determining first whether the given meme is hateful or not?(Binary Classification) A lot of students read such memes and may accept that idea is acceptable. Ideas and statements of such memes should be stopped before it is too late. Such memes try to make fun of a target individual or group. However, a lot of people are sharing distasteful memes and encouraging their ideas on social media sites. Reading memes is very common in the life of a college student. Since the reach of content in social media is limited to no one. Some people also deliberately create a meme whose salient purpose is to spread hate towards a community or a person. But a content which is hilarious to one can also be a loathe to another.

Memes can be a great content to have a laugh or two.

Introduction to Meme Classification using PyTorch and fastText

Smedium meme