Awesome

C3D-tensorflow

This is a repository trying to implement C3D-caffe on tensorflow,useing models directly converted from original C3D-caffe.
Be aware that there are about 5% video-level accuracy margin on UCF101 split1 between our implement in tensorflow and the original C3D-caffe.

Requirements:

1. Have installed the tensorflow >= 1.2 version
2. You must have installed the following two python libs: a) tensorflow b) Pillow
3. You must have downloaded the UCF101 (Action Recognition Data Set)
4. Each single avi file is decoded with 5FPS (it's depend your decision) in a single directory.
   • you can use the ./list/convert_video_to_images.sh script to decode the ucf101 video files
   • run ./list/convert_video_to_images.sh .../UCF101 5
5. Generate {train,test}.list files in list directory. Each line corresponds to "image directory" and a class (zero-based). For example:
   • you can use the ./list/convert_images_to_list.sh script to generate the {train,test}.list for the dataset
   • run ./list/convert_images_to_list.sh .../dataset_images 4, this will generate test.list and train.list files by a factor 4 inside the root folder

database/ucf101/train/ApplyEyeMakeup/v_ApplyEyeMakeup_g01_c01 0
database/ucf101/train/ApplyEyeMakeup/v_ApplyEyeMakeup_g01_c02 0
database/ucf101/train/ApplyEyeMakeup/v_ApplyEyeMakeup_g01_c03 0
database/ucf101/train/ApplyLipstick/v_ApplyLipstick_g01_c01 1
database/ucf101/train/ApplyLipstick/v_ApplyLipstick_g01_c02 1
database/ucf101/train/ApplyLipstick/v_ApplyLipstick_g01_c03 1
database/ucf101/train/Archery/v_Archery_g01_c01 2
database/ucf101/train/Archery/v_Archery_g01_c02 2
database/ucf101/train/Archery/v_Archery_g01_c03 2
database/ucf101/train/Archery/v_Archery_g01_c04 2
database/ucf101/train/BabyCrawling/v_BabyCrawling_g01_c01 3
database/ucf101/train/BabyCrawling/v_BabyCrawling_g01_c02 3
database/ucf101/train/BabyCrawling/v_BabyCrawling_g01_c03 3
database/ucf101/train/BabyCrawling/v_BabyCrawling_g01_c04 3
database/ucf101/train/BalanceBeam/v_BalanceBeam_g01_c01 4
database/ucf101/train/BalanceBeam/v_BalanceBeam_g01_c02 4
database/ucf101/train/BalanceBeam/v_BalanceBeam_g01_c03 4
database/ucf101/train/BalanceBeam/v_BalanceBeam_g01_c04 4
...

Usage

1. python train_c3d_ucf101.py will train C3D model. The trained model will saved in models directory.
2. python predict_c3d_ucf101.py will test C3D model on a validation data set.
3. cd ./C3D-tensorflow-1.0 &&python Random_clip_valid.py will get the random-clip accuracy on UCF101 test set with provided sports1m_finetuning_ucf101.model.
4. C3D-tensorflow-1.0/Random_clip_valid.py code is compatible with tensorflow 1.0+ , with a little bit different with the old repository
5. IMPORTANT NOTE: when you load the sports1m_finetuning_ucf101.model,you should use the tranpose operation like: pool5 = tf.transpose(pool5, perm=[0,1,4,2,3]),or in Random_clip_valid.py looks like:["transpose", [0, 1, 4, 2, 3]], but if you load conv3d_deepnetA_sport1m_iter_1900000_TF.model or c3d_ucf101_finetune_whole_iter_20000_TF.model,you don't need tranpose operation,just comment that line code.

Experiment result:

• Note:
  1.All report results are done specific on UCF101 split1 (train videos:9537,test videos:3783).
  2.ALL the results are video-level accuracy,unless stated otherwise.
  3.We follow the same way to extract clips from video as the C3D paper saying:'To extract C3D feature, a video is split into 16 frame long clips with a 8-frame overlap between two consecutive clips.These clips are passed to the C3D network to extract fc6 activations. These clip fc6 activations are averaged to form a 4096-dim video descriptor which is then followed by an L2-normalization'

• C3D as feature extractor:

• finetune C3D network on UCF101 split1 with pre-trained model:

• Note:
  1.the tensorflow-A model corresponding to the original C3D model pre-trained on UCF101 provided by @ hx173149 .
  2.the tensorflow-B model is just freeze the conv layers in tensorflow-A and finetuning four more epochs on fc layers with learning rate=1e-3.
  3.the random-clip column means random choose one clip from each video in UCF101 test split 1 ,so the result are not so robust.But according to the Law of Large Numbers,we may assume this items is positive correlated to your video-level accuracy.
  4.it's obvious that it if you do more finetuning work based on c3d_ucf101_finetune_whole_iter_20000_TF.model,and you may achieve better performance,i didn't do it because of time limit.
  5.with no doubt that you can get better result by appropriately finetuning the network

Trained models:

Other verions

• C3D-estimator-sagemaker: Use sagemaker to train C3D model.

References:

• Thanks the author Du tran's code: C3D-caffe
• C3D: Generic Features for Video Analysis