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

• Clone the code from Github:

    git clone https://github.com/metalbubble/moments_models.git
    cd moments_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 PyTorch0.3 + python36.

    python test_model.py

To test the model on your own video, supply the path of an mp4 file to this script like so:

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

• 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

Mathew Monfort, Bolei Zhou, Sarah Adel Bargal, Alex Andonian, Tom Yan, Kandan Ramakrishnan, Lisa Brown, Quanfu Fan, Dan Gutfreund, Carl Vondrick, Aude Oliva. 'Moments in Time Dataset: one million videos for event understanding'. arXiv:1801.03150. pdf, bib

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