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）.zip

#### 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.