dataset to enable the training of detection models, and organized the accompanying DeepFake Detection Challenge (DFDC) Kaggle competition. Importantly, all recorded subjects agreed to participate in and have their likenesses modified during the construction of the face-swapped dataset. The DFDC dataset ...
open(img_name) label = self.labels[idx] if self.transforms: img = self.transforms(img) return img, label val_dataset = ASDataset(client_file="/kaggle/input/dfdcdfdc/TEST_CLIENT.txt", imposter_file="/kaggle/input/dfdcdfdc/TEST_IMPOSTER.txt", transforms=preprocess) train_dataset = ASDataset...
Kaggle Challenge Page Fake detection articles The Deepfake Detection Challenge (DFDC) Preview Dataset Deep Fake Image Detection Based on Pairwise Learning DeeperForensics-1.0: A Large-Scale Dataset for Real-World Face Forgery Detection DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detect...
Each directory is named $NETWORK_$DATASET where $NETWORK is the architecture name and $DATASET is the training dataset. In each directory, you can find bestval.pth which are the best network weights according to the validation set.Additionally, you can find notebooks for results computations in...
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After looking at a lot of datasets and different types of problems, we stumbled upon BBC News Classification dataset on Kaggle. This dataset was used in an inclass competition and can be accessed here. Let's take a look at this dataset: As we can see this is a classification...
The last thing we need to cover is the training dataset. As you probably know, the great strength of pretrained models like BERT or ALBERT is that you don't need an annotated dataset, but just a lot of texts. To train sahajBERT, we used the Bengali Wikipedia dump fr...