Pretrained models for Moments in Time Dataset

We release the pre-trained models trained on Moments in Time.

Download the Models

• Clone the code from Github:

    git clone https://github.com/metalbubble/moments_models.git
    cd moments_models

Models

• RGB model in PyTorch (ResNet50 pretrained on ImageNet). Run the following script to download and run the test sample. The model is tested sucessfully in PyTorch 1.0 + python36.

    python test_model.py

To test the 2D model on your own video (frame-wise with temporal pooling), supply the path of an mp4 file to this script like so:

    python test_video.py --video_file path/to/video.mp4 --arch resnet50

• NEW (February 2019): 3D ResNet50 (inflated from 2D RGB model) trained on 16 frame inputs at 5 fps.

The 3D model can be downloaded and run using a similar command:

    python test_video.py --video_file path/to/video.mp4 --arch resnet3d50

• Dynamic Image model in Caffe: use the testing script.

• TRN models is at this repo. To use the TRN model trained on Moments:

Clone the TRN repo and Download the pretrained TRN model

git clone --recursive https://github.com/metalbubble/TRN-pytorch
cd TRN-pytorch/pretrain
./download_models.sh
cd ../sample_data
./download_sample_data.sh

Test the pretrained model on the sample video (Bolei is juggling ;-]!)

python test_video.py --arch InceptionV3 --dataset moments \
    --weight pretrain/TRN_moments_RGB_InceptionV3_TRNmultiscale_segment8_best.pth.tar \
    --frame_folder sample_data/bolei_juggling

RESULT ON sample_data/bolei_juggling
0.982 -> juggling
0.003 -> flipping
0.003 -> spinning

Reference

Mathew Monfort, Alex Andonian, Bolei Zhou, Kandan Ramakrishnan, Sarah Adel Bargal, Tom Yan, Lisa Brown, Quanfu Fan, Dan Gutfruend, Carl Vondrick, Aude Oliva. Moments in Time Dataset: one million videos for event understanding. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019. pdf, bib

Acknowledgements

The project is supported by MIT-IBM Watson AI Lab and IBM Research.