Awesome
Multi-Fiber Networks for Video Recognition
This repository contains the code and trained models of:
Yunpeng Chen, Yannis Kalantidis, Jianshu Li, Shuicheng Yan, Jiashi Feng. "Multi-Fiber Networks for Video Recognition" (PDF).
Implementation
We use MXNet @92053bd for image classification and PyTorch 0.4.0a0@a83c240 for video classification.
Normalization
The inputs are substrated by mean RGB = [ 124, 117, 104 ], and then multiplied by 0.0167.
Usage
Train motion from scratch:
python train_kinetics.py
Fine-tune with pre-trained model:
python train_ucf101.py
or
python train_hmdb51.py
Evaluate the trained model:
cd test
# the default setting is to test trained model on ucf-101 (split1)
python evaluate_video.py
Results
Image Recognition (ImageNet-1k)
Single Model, Single Crop Validation Accuracy:
| Model | Params | FLOPs | Top-1 | Top-5 | MXNet Model |
|---|---|---|---|---|---|
| ResNet-18 (reproduced) | 11.7 M | 1.8 G | 71.4 % | 90.2 % | GoogleDrive |
| ResNet-18 (MF embedded) | 9.6 M | 1.6 G | 74.3 % | 92.1 % | GoogleDrive |
| MF-Net (N=16) | 5.8 M | 861 M | 74.6 % | 92.0 % | GoogleDrive |
Video Recognition (UCF-101, HMDB51, Kinetics)
| Model | Params | Target Dataset | Top-1 |
|---|---|---|---|
| MF-Net (3D) | 8.0 M | Kinetics | 72.8 % |
| MF-Net (3D) | 8.0 M | UCF-101 | 96.0 %* |
| MF-Net (3D) | 8.0 M | HMDB51 | 74.6 %* |
* accuracy averaged on slip1, slip2, and slip3.
Trained Models
| Model | Target Dataset | PyTorch Model |
|---|---|---|
| MF-Net (2D) | ImageNet-1k | GoogleDrive |
| MF-Net (3D) | Kinetics | GoogleDrive |
| MF-Net (3D) | UCF-101 (split1) | GoogleDrive |
| MF-Net (3D) | HMDB51 (split1) | GoogleDrive |
Other Resources
ImageNet-1k Trainig/Validation List:
- Download link: GoogleDrive
ImageNet-1k category name mapping table:
- Download link: GoogleDrive
Kinetics Dataset:
- Downloader: GitHub
UCF-101 Dataset:
- Download link: Website
HMDB51 Dataset:
- Download link: Website
FAQ
Do I need to convert the raw videos to specific format?
- Our `dataiter' supports reading from raw videos and can tolerate corrupted videos.
How can I make the training faster?
- Decoding frames from compressed videos consumes quite a lot CPU resources which is the bottleneck for the speed. You can try to convert the downloaded videos to other format or reduce the quality of the video. For example:
# convet to sort_edge_length = 360
ffmpeg -y -i ${SRC_VID} -c:v mpeg4 -filter:v "scale=min(iw\,(360*iw)/min(iw\,ih)):-1" -b:v 640k -an ${DST_VID}
# or, convet to sort_edge_length = 256
ffmpeg -y -i ${SRC_VID} -c:v mpeg4 -filter:v "scale=min(iw\,(256*iw)/min(iw\,ih)):-1" -b:v 512k -an ${DST_VID}
# or, convet to sort_edge_length = 160
ffmpeg -y -i ${SRC_VID} -c:v mpeg4 -filter:v "scale=min(iw\,(160*iw)/min(iw\,ih)):-1" -b:v 240k -an ${DST_VID}
- Find another computer with better CPU.
- The group convolution may not be well optimized.
Citation
If you use our code/model in your work or find it is helpful, please cite the paper:
@inproceedings{chen2018multifiber,
title={Multi-Fiber networks for Video Recognition},
author={Chen, Yunpeng and Kalantidis, Yannis and Li, Jianshu and Yan, Shuicheng and Feng, Jiashi},
booktitle={European Conference on Computer Vision (ECCV)},
year={2018}
}