CLASSIFICATION algorithmsPICTURE frames & framingThe article is about the classification of anomaly detection dataset-UCF videos obtained from Kaggle. The data set consists of 4-class video files. While three of them consist of different crimes and abnormal events such as Burglary, Explosion, ...
A new video based multi behavior dataset for cows, CBVD-5, is introduced in this paper. The dataset includes five cow behaviors: standing, lying down, foraging,rumination and drinking. The dataset comprises 107 cows from the entire barn, maintaining an 80% stocking density. Monitoring occurred ...
The YouTube-8M video classification challenge requires teams to classify 0.7 million videos into one or more of 4,716 classes. In this Kaggle competition, we placed in the top 3% out of 650 participants using released video and audio features. Beyond that, we extend the original competition by...
Tensorflow implementation of paper “Efficient Video Classification Using Fewer Frames”. Introduction To begin with, the paper is based on a kaggle competition2nd Youtube8M Video Understanding Challenge on Kaggle. It is a video classification task over theYouTube-8Mdataset, which contains 8 million ...
Further investigation may explore alternative feature engineering approach, evaluate ensemble learning techniques, or address the challenges associated with class imbalance in the dataset. Such advancements would foster the continual enhancement of comment classification systems, ensuring improved user engagement ...
With the fine-tuned VideoMAE checkpoint, it would be possible to evaluate the benchmark datast and also retraining would be possible on custom dataset. For end-to-end workflow, check this quick retraining.ipynb notebook. It supports both multi-gpu and tpu-vm retraining and evaluation. Some ...
Explore and run machine learning code with Kaggle Notebooks | Using data from multiple data sources
This dataset is perfect for your next exploratory data analysis (EDA) notebook. Clustering and Classification: Can you group similar games together based on ratings and features? Try building a classification model that categorizes games into different rating tiers (e.g., Top Rated, Mid Tier, ...
This feature vector is mapped to a single class by an additional classification layer. This single class corresponds to the individual’s ID shown in the dataset sample. Figure 4 shows the training procedure. Note that, for the image-based method, the instances of the dataset are single ...
A high AUC rate close to 1 indicates that the classification model is an excellent predictor and predicts the actual video as accurate and the fake video as fake, and vice versa. The chosen metrics provide insights into various aspects of the model’s performance, considering both the detection...