IG-65M PyTorch

Unofficial PyTorch (and ONNX) 3D video classification models and weights pre-trained on IG-65M (65MM Instagram videos).

IG-65M activations for the Primer movie trailer video; time goes top to bottom

IG-65M video deep dream: maximizing activations; for more see this pull request

Usage :computer:

The following describes how to use the model in your own project and how to use our conversion and extraction tools.

PyTorch Models

We provide convenient PyTorch Hub integration

>>> import torch
>>>
>>> torch.hub.list("moabitcoin/ig65m-pytorch")
['r2plus1d_34_32_ig65m', 'r2plus1d_34_32_kinetics', 'r2plus1d_34_8_ig65m', 'r2plus1d_34_8_kinetics']
>>>
>>> model = torch.hub.load("moabitcoin/ig65m-pytorch", "r2plus1d_34_32_ig65m", num_classes=359, pretrained=True)

Tools

We build and publish Docker images (see all tags) via Travis CI/CD for master and for all releases.

In these images we provide the following tools:

• convert - to convert Caffe2 blobs to PyTorch model and weights
• extract - to compute clip features for a video with a pre-trained model
• semcode - to visualize clip features for a video over time
• index-build - to build an approximate nearest neighbor index from clip features
• index-serve - to load an approximate nearest neighbor index and serve queries
• index-query- to make approximate nearest neighbor queries against an index server

Run these pre-built images via

docker run moabitcoin/ig65m-pytorch:latest-cpu --help

Example for running on CPUs:

docker run --ipc=host -v $PWD:/data moabitcoin/ig65m-pytorch:latest-cpu \
    extract /data/myvideo.mp4 /data/myfeatures.npy

Example for running on GPUs via nvidia-docker:

docker run --runtime=nvidia --ipc=host -v $PWD:/data moabitcoin/ig65m-pytorch:latest-gpu \
    extract /data/myvideo.mp4 /data/myfeatures.npy

Development

We provide CPU and nvidia-docker based GPU Dockerfiles for self-contained and reproducible environments. Use the convenience Makefile to build the Docker image and then get into the container mounting a host directory to /data inside the container:

make
make run datadir=/Path/To/My/Videos

By default we build and run the CPU Docker images; for GPUs run:

make dockerfile=Dockerfile.gpu
make gpu

The WebcamDataset requires exposing /dev/video0 to the container which will only work on Linux:

make
make webcam

PyTorch and ONNX Models :trophy:

We provide converted .pth and .pb PyTorch and ONNX weights, respectively.

Notes

• ONNX models provided here have not been optimized for inference.
• Models fine-tuned on Kinetics have 400 classes, the plain IG65 models 359 (32 clips), and 487 (8 clips) classes.
• For models fine-tuned on Kinetics you can use the labels from here.
• For plain IG65 models there is no label map available.
• Official Facebook Research Caffe2 models are here.

References :book:

1. D. Tran, H. Wang, L. Torresani, J. Ray, Y. LeCun and M. Paluri. A Closer Look at Spatiotemporal Convolutions for Action Recognition. CVPR 2018.
2. D. Tran, H. Wang, L. Torresani and M. Feiszli. Video Classification with Channel-Separated Convolutional Networks. ICCV 2019.
3. D. Ghadiyaram, M. Feiszli, D. Tran, X. Yan, H. Wang and D. Mahajan, Large-scale weakly-supervised pre-training for video action recognition. CVPR 2019.
4. VMZ: Model Zoo for Video Modeling
5. Kinetics & IG-65M

License

Copyright © 2019 MoabitCoin

Distributed under the MIT License (MIT).