#### This is a fork of [Can Zhang](https://github.com/zhang-can/ECO-pytorch)'s PyTorch implementation for the [paper](https://arxiv.org/pdf/1804.09066.pdf):
##### " ECO: Efficient Convolutional Network for Online Video Understanding, European Conference on Computer Vision (ECCV), 2018." By Mohammadreza Zolfaghari, Kamaljeet Singh, Thomas Brox
### Update
- **2019.3.05**: This is a major update. This update is more robust and we solved some problems in the previous version such as iter_size and ECO Full model definiation. Updating the training procedure (main.py) and providing the pretrained models for ECOLite and ECOFull. Please let us know if you found any problem or had suggestions to improve the code.
##### NOTE
* Trained models on Kinetics dataset for ECO Lite and C3D are provided.
* Pre-trained model for 2D-Net is provided by [tsn-pytorch](https://github.com/yjxiong/tsn-pytorch).
* **Stay tuned for more updates**
### Environment:
* Python 3.5.2
* PyTorch 0.4.1
* TorchVison: 0.2.1
### Clone this repo
```
git clone https://github.com/mzolfaghari/ECO-pytorch
```
### Generate dataset lists
```bash
python gen_dataset_lists.py <ucf101/something> <dataset_frames_root_path>
```
e.g. python gen_dataset_lists.py something ~/dataset/20bn-something-something-v1/
> The dataset should be organized as:<br>
> <dataset_frames_root_path>/<video_name>/<frame_images>
### Training
1. Download the initialization and trained models:
```Shell
ECO-Lite pretrained model on Kinetics: https://drive.google.com/open?id=1XNIq7byciKgrn011jLBggd2g79jKX4uD
ECO-Full pretrained model on Kinetics: https://drive.google.com/open?id=1ATuN_KctsbFAbcNgWDlETZVsy2vhxZay
```
Othe models:
```Shell
sh models/download_models.sh
```
* If you can not access Google Drive, please download the pretrained models from [BaiduYun](https://pan.baidu.com/s/1Hx52akJLR_ISfX406bkIog), and put them in "models" folder.
2. Command for training ECO Lite model:
```bash
./scripts/run_ECOLite_kinetics.sh local
```
3. For training C3D network use the following command:
```bash
./scripts/run_c3dres_kinetics.sh local
```
4. For finetuning on UCF101 use the following command:
```bash
sh run_demo_ECO_Lite.sh local
or
sh run_demo_ECO_Full.sh local
```
### NOTE
* If you want to train your model from scratch change the config as following:
```bash
--pretrained_parts scratch
```
* configurations explained in "opts.py"
#### TODO
1. Trained models on other datasets
#### Citation
If you use this code or ideas from the paper for your research, please cite our paper:
```
@inproceedings{ECO_eccv18,
author={Mohammadreza Zolfaghari and
Kamaljeet Singh and
Thomas Brox},
title={{ECO:} Efficient Convolutional Network for Online Video Understanding},
booktitle={ECCV},
year={2018}
}
```
#### Contact
[Mohammadreza Zolfaghari](https://github.com/mzolfaghari/ECO-pytorch), [Can Zhang](https://github.com/zhang-can/ECO-pytorch)
Questions can also be left as issues in the repository. We will be happy to answer them.
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智能化工业园区安防系统python源码(多传感数据监测融合系统、空地协同集群巡检、异常检测与小目标检测算法、后端云协同系统。 无人车集群、无人机集群和多种传感器等智能感知设备与定位技术对园区内的人、物、环境等全要素多源异构信息进行实时感知和监控).zip 智能化工业园区安防系统python源码(多传感数据监测融合系统、空地协同集群巡检、异常检测与小目标检测算法、后端云协同系统。 无人车集群、无人机集群和多种传感器等智能感知设备与定位技术对园区内的人、物、环境等全要素多源异构信息进行实时感知和监控).zip 智能化工业园区安防系统python源码(多传感数据监测融合系统、空地协同集群巡检、异常检测与小目标检测算法、后端云协同系统。 无人车集群、无人机集群和多种传感器等智能感知设备与定位技术对园区内的人、物、环境等全要素多源异构信息进行实时感知和监控).zip 智能化工业园区安防系统python源码(多传感数据监测融合系统、空地协同集群巡检、异常检测与小目标检测算法、后端云协同系统。 无人车集群、无人机集群和多种传感器等智能感知设备与定位技术对园区内的人、物、环境等全要素多源异构信息进行实时感知和监控).zip 【资源说明】 1、该资源内项目代码都是经过测试运行成功,功能正常的情况下才上传的,请放心下载使用。 2、适用人群:主要针对计算机相关专业(如计科、信息安全、数据科学与大数据技术、人工智能、通信、物联网、数学、电子信息等)的同学或企业员工下载使用,具有较高的学习借鉴价值。 3、不仅适合小白学习实战练习,也可作为大作业、课程设计、毕设项目、初期项目立项演示等,欢迎下载,互相学习,共同进步!
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