TransNet V2: Shot Boundary Detection Neural Network

Overview

TransNet V2: Shot Boundary Detection Neural Network

This repository contains code for TransNet V2: An effective deep network architecture for fast shot transition detection.

Our reevaluation of other publicly available state-of-the-art shot boundary methods (F1 scores):

Model ClipShots BBC Planet Earth RAI
TransNet V2 (this repo) 77.9 96.2 93.9
TransNet (github) 73.5 92.9 94.3
Hassanien et al. (github) 75.9 92.6 93.9
Tang et al., ResNet baseline (github) 76.1 89.3 92.8

🎥 USE IT ON YOUR VIDEO

➡️ See inference folder and its README file. ⬅️

REPLICATE THE WORK

Note the datasets for training are tens of gigabytes in size, hundreds of gigabytes when exported.

You do not need to train the network, use code and instructions in inference folder to detect shots in your videos.

This repository contains all that is needed to run any experiment for TransNet V2 network including network training and dataset creation. All experiments should be runnable in this NVIDIA DOCKER file.

In general these steps need to be done in order to replicate our work (in training folder):

  1. Download RAI and BBC Planet Earth test datasets (link). Download ClipShots train/test dataset (link). Optionally get IACC.3 dataset.
  2. Edit and run consolidate_datasets.py in order to transform ground truth from all the datasets into one common format.
  3. Take some videos from ClipShotsTrain aside as a validation dataset.
  4. Run create_dataset.py to create all train/validation/test datasets.
  5. Run training.py ../configs/transnetv2.gin to train a model.
  6. Run evaluate.py /path/to/run_log_dir epoch_no /path/to/test_dataset for proper evaluation.

CREDITS

If found useful, please cite us;)

Owner
Tomáš Souček
Currently Research Fellow at Charles University, Machine Learning Researcher at SANEZOO and Avast
Tomáš Souček
Self-Learning - Books Papers, Courses & more I have to learn soon

Self-Learning This repository is intended to be used for personal use, all rights reserved to respective owners, please cite original authors and ask

Achint Chaudhary 968 Jan 02, 2022
[v1 (ISBI'21) + v2] MedMNIST: A Large-Scale Lightweight Benchmark for 2D and 3D Biomedical Image Classification

MedMNIST Project (Website) | Dataset (Zenodo) | Paper (arXiv) | MedMNIST v1 (ISBI'21) Jiancheng Yang, Rui Shi, Donglai Wei, Zequan Liu, Lin Zhao, Bili

683 Dec 28, 2022
MLP-Numpy - A simple modular implementation of Multi Layer Perceptron in pure Numpy.

MLP-Numpy A simple modular implementation of Multi Layer Perceptron in pure Numpy. I used the Iris dataset from scikit-learn library for the experimen

Soroush Omranpour 1 Jan 01, 2022
[ICML 2021] Break-It-Fix-It: Learning to Repair Programs from Unlabeled Data

Break-It-Fix-It: Learning to Repair Programs from Unlabeled Data This repo provides the source code & data of our paper: Break-It-Fix-It: Unsupervised

Michihiro Yasunaga 86 Nov 30, 2022
Official codebase for ICLR oral paper Unsupervised Vision-Language Grammar Induction with Shared Structure Modeling

CLIORA This is the official codebase for ICLR oral paper: Unsupervised Vision-Language Grammar Induction with Shared Structure Modeling. We introduce

Bo Wan 32 Dec 23, 2022
PyTorch implementation of the Value Iteration Networks (VIN) (NIPS '16 best paper)

Value Iteration Networks in PyTorch Tamar, A., Wu, Y., Thomas, G., Levine, S., and Abbeel, P. Value Iteration Networks. Neural Information Processing

LEI TAI 75 Nov 24, 2022
This is the official Pytorch-version code of FlatGCN (Flattened Graph Convolutional Networks for Recommendation).

FlatGCN This is the official Pytorch-version code of FlatGCN (Flattened Graph Convolutional Networks for Recommendation, submitted to ICASSP2022). Req

Dreamer 2 Aug 09, 2022
Sample code and notebooks for Vertex AI, the end-to-end machine learning platform on Google Cloud

Google Cloud Vertex AI Samples Welcome to the Google Cloud Vertex AI sample repository. Overview The repository contains notebooks and community conte

Google Cloud Platform 560 Dec 31, 2022
The Multi-Mission Maximum Likelihood framework (3ML)

PyPi Conda The Multi-Mission Maximum Likelihood framework (3ML) A framework for multi-wavelength/multi-messenger analysis for astronomy/astrophysics.

The Multi-Mission Maximum Likelihood (3ML) 62 Dec 30, 2022
Oriented Response Networks, in CVPR 2017

Oriented Response Networks [Home] [Project] [Paper] [Supp] [Poster] Torch Implementation The torch branch contains: the official torch implementation

ZhouYanzhao 217 Dec 12, 2022
NaturalCC is a sequence modeling toolkit that allows researchers and developers to train custom models

NaturalCC NaturalCC is a sequence modeling toolkit that allows researchers and developers to train custom models for many software engineering tasks,

159 Dec 28, 2022
Implementation of Bagging and AdaBoost Algorithm

Bagging-and-AdaBoost Implementation of Bagging and AdaBoost Algorithm Dataset Red Wine Quality Data Sets For simplicity, we will have 2 classes of win

Zechen Ma 1 Nov 01, 2021
In this project we combine techniques from neural voice cloning and musical instrument synthesis to achieve good results from as little as 16 seconds of target data.

Neural Instrument Cloning In this project we combine techniques from neural voice cloning and musical instrument synthesis to achieve good results fro

Erland 127 Dec 23, 2022
MlTr: Multi-label Classification with Transformer

MlTr: Multi-label Classification with Transformer This is official implement of "MlTr: Multi-label Classification with Transformer". Abstract The task

程星 38 Nov 08, 2022
Code for "Retrieving Black-box Optimal Images from External Databases" (WSDM 2022)

Retrieving Black-box Optimal Images from External Databases (WSDM 2022) We propose how a user retreives an optimal image from external databases of we

joisino 5 Apr 13, 2022
BigDetection: A Large-scale Benchmark for Improved Object Detector Pre-training

BigDetection: A Large-scale Benchmark for Improved Object Detector Pre-training By Likun Cai, Zhi Zhang, Yi Zhu, Li Zhang, Mu Li, Xiangyang Xue. This

290 Dec 29, 2022
A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population

DeepKE is a knowledge extraction toolkit supporting low-resource and document-level scenarios for entity, relation and attribute extraction. We provide comprehensive documents, Google Colab tutorials

ZJUNLP 1.6k Jan 05, 2023
Project dự đoán giá cổ phiếu bằng thuật toán LSTM gồm: code train và code demo

Web predicts stock prices using Long - Short Term Memory algorithm Give me some start please!!! User interface image: Choose: DayBegin, DayEnd, Stock

Vo Thuong Truong Nhon 8 Nov 11, 2022
A way to store images in YAML.

YAMLImg A way to store images in YAML. I made this after seeing Roadcrosser's JSON-G because it was too inspiring to ignore this opportunity. Installa

5 Mar 14, 2022
A curated list of long-tailed recognition resources.

Awesome Long-tailed Recognition A curated list of long-tailed recognition and related resources. Please feel free to pull requests or open an issue to

Zhiwei ZHANG 542 Jan 01, 2023