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AVA

Introduction

@inproceedings{gu2018ava,
  title={Ava: A video dataset of spatio-temporally localized atomic visual actions},
  author={Gu, Chunhui and Sun, Chen and Ross, David A and Vondrick, Carl and Pantofaru, Caroline and Li, Yeqing and Vijayanarasimhan, Sudheendra and Toderici, George and Ricco, Susanna and Sukthankar, Rahul and others},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={6047--6056},
  year={2018}
}
@article{duan2020omni,
  title={Omni-sourced Webly-supervised Learning for Video Recognition},
  author={Duan, Haodong and Zhao, Yue and Xiong, Yuanjun and Liu, Wentao and Lin, Dahua},
  journal={arXiv preprint arXiv:2003.13042},
  year={2020}
}
@inproceedings{feichtenhofer2019slowfast,
  title={Slowfast networks for video recognition},
  author={Feichtenhofer, Christoph and Fan, Haoqi and Malik, Jitendra and He, Kaiming},
  booktitle={Proceedings of the IEEE international conference on computer vision},
  pages={6202--6211},
  year={2019}
}

Model Zoo

AVA2.1

Model Modality Pretrained Backbone Input gpus Resolution mAP log json ckpt
slowonly_kinetics_pretrained_r50_4x16x1_20e_ava_rgb RGB Kinetics-400 ResNet50 4x16 8 short-side 256 20.1 log json ckpt
slowonly_omnisource_pretrained_r50_4x16x1_20e_ava_rgb RGB OmniSource ResNet50 4x16 8 short-side 256 21.8 log json ckpt
slowonly_nl_kinetics_pretrained_r50_4x16x1_10e_ava_rgb RGB Kinetics-400 ResNet50 4x16 8 short-side 256 21.75 log json ckpt
slowonly_nl_kinetics_pretrained_r50_8x8x1_10e_ava_rgb RGB Kinetics-400 ResNet50 8x8 8x2 short-side 256 23.79 log json ckpt
slowonly_kinetics_pretrained_r101_8x8x1_20e_ava_rgb RGB Kinetics-400 ResNet101 8x8 8x2 short-side 256 24.6 log json ckpt
slowonly_omnisource_pretrained_r101_8x8x1_20e_ava_rgb RGB OmniSource ResNet101 8x8 8x2 short-side 256 25.9 log json ckpt
slowfast_kinetics_pretrained_r50_4x16x1_20e_ava_rgb RGB Kinetics-400 ResNet50 32x2 8x2 short-side 256 24.4 log json ckpt
slowfast_context_kinetics_pretrained_r50_4x16x1_20e_ava_rgb RGB Kinetics-400 ResNet50 32x2 8x2 short-side 256 25.4 log json ckpt
slowfast_kinetics_pretrained_r50_8x8x1_20e_ava_rgb RGB Kinetics-400 ResNet50 32x2 8x2 short-side 256 25.5 log json ckpt

AVA2.2

Model Modality Pretrained Backbone Input gpus mAP log json ckpt
slowfast_kinetics_pretrained_r50_8x8x1_cosine_10e_ava22_rgb RGB Kinetics-400 ResNet50 32x2 8 26.1 log json ckpt
slowfast_temporal_max_kinetics_pretrained_r50_8x8x1_cosine_10e_ava22_rgb RGB Kinetics-400 ResNet50 32x2 8 26.4 log json ckpt
slowfast_temporal_max_focal_alpha3_gamma1_kinetics_pretrained_r50_8x8x1_cosine_10e_ava22_rgb RGB Kinetics-400 ResNet50 32x2 8 26.8 log json ckpt
  • Notes:
  1. The gpus indicates the number of gpu we used to get the checkpoint. According to the Linear Scaling Rule, you may set the learning rate proportional to the batch size if you use different GPUs or videos per GPU, e.g., lr=0.01 for 4 GPUs x 2 video/gpu and lr=0.08 for 16 GPUs x 4 video/gpu.
  2. Context indicates that using both RoI feature and global pooled feature for classification, which leads to around 1% mAP improvement in general.

For more details on data preparation, you can refer to AVA in Data Preparation.

Train

You can use the following command to train a model.

python tools/train.py ${CONFIG_FILE} [optional arguments]

Example: train SlowOnly model on AVA with periodic validation.

python tools/train.py configs/detection/ava/slowonly_kinetics_pretrained_r50_8x8x1_20e_ava_rgb.py --validate

For more details and optional arguments infos, you can refer to Training setting part in getting_started .

Train Custom Classes From Ava Dataset

You can train custom classes from ava. Ava suffers from class imbalance. There are more then 100,000 samples for classes like stand/listen to (a person)/talk to (e.g., self, a person, a group)/watch (a person), whereas half of all classes has less than 500 samples. In most cases, training custom classes with fewer samples only will lead to better results.

Three steps to train custom classes:

  • Step 1: Select custom classes from original classes, named custom_classes. Class 0 should not be selected since it is reserved for further usage (to identify whether a proposal is positive or negative, not implemented yet) and will be added automatically.
  • Step 2: Set num_classes. In order to be compatible with current codes, plase make sure num_classes == len(custom_classes) + 1.
    • The new class 0 corresponds to original class 0. The new class i(i > 0) corresponds to original class custom_classes[i-1].
    • There are three num_classes in ava config, model -> roi_head -> bbox_head -> num_classes, data -> train -> num_classes and data -> val -> num_classes.
    • If num_classes <= 5, input arg topk of BBoxHeadAVA should be modified. The default value of topk is (3, 5), and all elements of topk must be smaller than num_classes.
  • Step 3: Make sure all custom classes are in label_file. It is worth mentioning that there are two label files, ava_action_list_v2.1_for_activitynet_2018.pbtxt(contains 60 classes, 20 classes are missing) and ava_action_list_v2.1.pbtxt(contains all 80 classes).

Take slowonly_kinetics_pretrained_r50_4x16x1_20e_ava_rgb as an example, training custom classes with AP in range (0.1, 0.3), aka [3, 6, 10, 27, 29, 38, 41, 48, 51, 53, 54, 59, 61, 64, 70, 72]. Please note that, the previously mentioned AP is calculated by original ckpt, which is trained by all 80 classes. The results are listed as follows.

training classes mAP(custom classes) config log json ckpt
All 80 classes 0.1948 slowonly_kinetics_pretrained_r50_4x16x1_20e_ava_rgb log json ckpt
custom classes 0.3311 slowonly_kinetics_pretrained_r50_4x16x1_20e_ava_rgb_custom_classes log json ckpt
All 80 classes 0.1864 slowfast_kinetics_pretrained_r50_4x16x1_20e_ava_rgb.py log json ckpt
custom classes 0.3785 slowfast_kinetics_pretrained_r50_4x16x1_20e_ava_rgb_custom_classes log json ckpt

Test

You can use the following command to test a model.

python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [optional arguments]

Example: test SlowOnly model on AVA and dump the result to a csv file.

python tools/test.py configs/detection/ava/slowonly_kinetics_pretrained_r50_8x8x1_20e_ava_rgb.py checkpoints/SOME_CHECKPOINT.pth --eval mAP --out results.csv

For more details and optional arguments infos, you can refer to Test a dataset part in getting_started .