README.md

TSN

Introduction

[ALGORITHM]

@inproceedings{wang2016temporal,
  title={Temporal segment networks: Towards good practices for deep action recognition},
  author={Wang, Limin and Xiong, Yuanjun and Wang, Zhe and Qiao, Yu and Lin, Dahua and Tang, Xiaoou and Van Gool, Luc},
  booktitle={European conference on computer vision},
  pages={20--36},
  year={2016},
  organization={Springer}
}

Model Zoo

UCF-101

config	gpus	backbone	pretrain	top1 acc	top5 acc	gpu_mem(M)	ckpt	log	json
tsn_r50_1x1x3_75e_ucf101_rgb [1]	8	ResNet50	ImageNet	83.03	96.78	8332	ckpt	log	json

[1] We report the performance on UCF-101 split1.

HMDB51

config	gpus	backbone	pretrain	top1 acc	top5 acc	gpu_mem(M)	ckpt	log	json
tsn_r50_1x1x8_50e_hmdb51_imagenet_rgb	8	ResNet50	ImageNet	48.95	80.19	21535	ckpt	log	json
tsn_r50_1x1x8_50e_hmdb51_kinetics400_rgb	8	ResNet50	Kinetics400	56.08	84.31	21535	ckpt	log	json
tsn_r50_1x1x8_50e_hmdb51_mit_rgb	8	ResNet50	Moments	54.25	83.86	21535	ckpt	log	json

Kinetics-400

config	resolution	gpus	backbone	pretrain	top1 acc	top5 acc	reference top1 acc	reference top5 acc	inference_time(video/s)	gpu_mem(M)	ckpt	log	json
tsn_r50_1x1x3_100e_kinetics400_rgb	340x256	8	ResNet50	ImageNet	70.60	89.26	x	x	4.3 (25x10 frames)	8344	ckpt	log	json
tsn_r50_1x1x3_100e_kinetics400_rgb	short-side 256	8	ResNet50	ImageNet	70.42	89.03	x	x	x	8343	ckpt	log	json
tsn_r50_dense_1x1x5_50e_kinetics400_rgb	340x256	8x3	ResNet50	ImageNet	70.18	89.10	69.15	88.56	12.7 (8x10 frames)	7028	ckpt	log	json
tsn_r50_320p_1x1x3_100e_kinetics400_rgb	short-side 320	8x2	ResNet50	ImageNet	70.91	89.51	x	x	10.7 (25x3 frames)	8344	ckpt	log	json
tsn_r50_320p_1x1x3_110e_kinetics400_flow	short-side 320	8x2	ResNet50	ImageNet	55.70	79.85	x	x	x	8471	ckpt	log	json
tsn_r50_320p_1x1x3_kinetics400_twostream [1: 1]*	x	x	ResNet50	ImageNet	72.76	90.52	x	x	x	x	x	x	x
tsn_r50_1x1x8_100e_kinetics400_rgb	short-side 256	8	ResNet50	ImageNet	71.80	90.17	x	x	x	8343	ckpt	log	json
tsn_r50_320p_1x1x8_100e_kinetics400_rgb	short-side 320	8x3	ResNet50	ImageNet	72.41	90.55	x	x	11.1 (25x3 frames)	8344	ckpt	log	json
tsn_r50_320p_1x1x8_110e_kinetics400_flow	short-side 320	8x4	ResNet50	ImageNet	57.76	80.99	x	x	x	8473	ckpt	log	json
tsn_r50_320p_1x1x8_kinetics400_twostream [1: 1]*	x	x	ResNet50	ImageNet	74.64	91.77	x	x	x	x	x	x	x
tsn_r50_video_320p_1x1x3_100e_kinetics400_rgb	short-side 320	8	ResNet50	ImageNet	71.11	90.04	x	x	x	8343	ckpt	log	json
tsn_r50_dense_1x1x8_100e_kinetics400_rgb	340x256	8	ResNet50	ImageNet	70.77	89.3	68.75	88.42	12.2 (8x10 frames)	8344	ckpt	log	json
tsn_r50_video_1x1x8_100e_kinetics400_rgb	short-side 256	8	ResNet50	ImageNet	71.79	90.25	x	x	x	21558	ckpt	log	json
tsn_r50_video_dense_1x1x8_100e_kinetics400_rgb	short-side 256	8	ResNet50	ImageNet	70.40	89.12	x	x	x	21553	ckpt	log	json

Here, We use [1: 1] to indicate that we combine rgb and flow score with coefficients 1: 1 to get the two-stream prediction (without applying softmax).

Kinetics-400 Data Benchmark (8-gpus, ResNet50, ImageNet pretrain; 3 segments)

In data benchmark, we compare:

1. Different data preprocessing methods: (1) Resize video to 340x256, (2) Resize the short edge of video to 320px, (3) Resize the short edge of video to 256px;
2. Different data augmentation methods: (1) MultiScaleCrop, (2) RandomResizedCrop;
3. Different testing protocols: (1) 25 frames x 10 crops, (2) 25 frames x 3 crops.

config	resolution	training augmentation	testing protocol	top1 acc	top5 acc	ckpt	log	json
tsn_r50_multiscalecrop_340x256_1x1x3_100e_kinetics400_rgb	340x256	MultiScaleCrop	25x10 frames	70.60	89.26	ckpt	log	json
x	340x256	MultiScaleCrop	25x3 frames	70.52	89.39	x	x	x
tsn_r50_randomresizedcrop_340x256_1x1x3_100e_kinetics400_rgb	340x256	RandomResizedCrop	25x10 frames	70.11	89.01	ckpt	log	json
x	340x256	RandomResizedCrop	25x3 frames	69.95	89.02	x	x	x
tsn_r50_multiscalecrop_320p_1x1x3_100e_kinetics400_rgb	short-side 320	MultiScaleCrop	25x10 frames	70.32	89.25	ckpt	log	json
x	short-side 320	MultiScaleCrop	25x3 frames	70.54	89.39	x	x	x
tsn_r50_randomresizedcrop_320p_1x1x3_100e_kinetics400_rgb	short-side 320	RandomResizedCrop	25x10 frames	70.44	89.23	ckpt	log	json
x	short-side 320	RandomResizedCrop	25x3 frames	70.91	89.51	x	x	x
tsn_r50_multiscalecrop_256p_1x1x3_100e_kinetics400_rgb	short-side 256	MultiScaleCrop	25x10 frames	70.42	89.03	ckpt	log	json
x	short-side 256	MultiScaleCrop	25x3 frames	70.79	89.42	x	x	x
tsn_r50_randomresizedcrop_256p_1x1x3_100e_kinetics400_rgb	short-side 256	RandomResizedCrop	25x10 frames	69.80	89.06	ckpt	log	json
x	short-side 256	RandomResizedCrop	25x3 frames	70.48	89.89	x	x	x

Kinetics-400 OmniSource Experiments

config	resolution	backbone	pretrain	w. OmniSource	top1 acc	top5 acc	inference_time(video/s)	gpu_mem(M)	ckpt	log	json
tsn_r50_1x1x3_100e_kinetics400_rgb	340x256	ResNet50	ImageNet	❌	70.6	89.3	4.3 (25x10 frames)	8344	ckpt	log	json
x	340x256	ResNet50	ImageNet	✔️	73.6	91.0	x	8344	ckpt	x	x
x	short-side 320	ResNet50	IG-1B [1]	❌	73.1	90.4	x	8344	ckpt	x	x
x	short-side 320	ResNet50	IG-1B [1]	✔️	75.7	91.9	x	8344	ckpt	x	x

[1] We obtain the pre-trained model from torch-hub, the pretrain model we used is resnet50_swsl

Kinetics-600

config	resolution	gpus	backbone	pretrain	top1 acc	top5 acc	inference_time(video/s)	gpu_mem(M)	ckpt	log	json
tsn_r50_video_1x1x8_100e_kinetics600_rgb	short-side 256	8x2	ResNet50	ImageNet	74.8	92.3	11.1 (25x3 frames)	8344	ckpt	log	json

Kinetics-700

config	resolution	gpus	backbone	pretrain	top1 acc	top5 acc	inference_time(video/s)	gpu_mem(M)	ckpt	log	json
tsn_r50_video_1x1x8_100e_kinetics700_rgb	short-side 256	8x2	ResNet50	ImageNet	61.7	83.6	11.1 (25x3 frames)	8344	ckpt	log	json

Something-Something V1

config	resolution	gpus	backbone	pretrain	top1 acc	top5 acc	reference top1 acc	reference top5 acc	gpu_mem(M)	ckpt	log	json
tsn_r50_1x1x8_50e_sthv1_rgb	height 100	8	ResNet50	ImageNet	18.55	44.80	17.53	44.29	10978	ckpt	log	json
tsn_r50_1x1x16_50e_sthv1_rgb	height 100	8	ResNet50	ImageNet	15.77	39.85	13.33	35.58	5691	ckpt	log	json

Something-Something V2

config	resolution	gpus	backbone	pretrain	top1 acc	top5 acc	reference top1 acc	reference top5 acc	gpu_mem(M)	ckpt	log	json
tsn_r50_1x1x8_50e_sthv2_rgb	height 240	8	ResNet50	ImageNet	32.97	63.62	30.56	58.49	10966	ckpt	log	json
tsn_r50_1x1x16_50e_sthv2_rgb	height 240	8	ResNet50	ImageNet	27.21	55.84	21.91	46.87	8337	ckpt	log	json

Moments in Time

config	resolution	gpus	backbone	pretrain	top1 acc	top5 acc	gpu_mem(M)	ckpt	log	json
tsn_r50_1x1x6_100e_mit_rgb	short-side 256	8x2	ResNet50	ImageNet	26.84	51.6	8339	ckpt	log	json

Multi-Moments in Time

config	resolution	gpus	backbone	pretrain	mAP	gpu_mem(M)	ckpt	log	json
tsn_r101_1x1x5_50e_mmit_rgb	short-side 256	8x2	ResNet101	ImageNet	61.09	10467	ckpt	log	json

ActivityNet v1.3

config	resolution	gpus	backbone	pretrain	top1 acc	top5 acc	gpu_mem(M)	ckpt	log	json
tsn_r50_320p_1x1x8_50e_activitynet_video_rgb	short-side 320	8x1	ResNet50	Kinetics400	73.93	93.44	5692	ckpt	log	json
tsn_r50_320p_1x1x8_50e_activitynet_clip_rgb	short-side 320	8x1	ResNet50	Kinetics400	76.90	94.47	5692	ckpt	log	json
tsn_r50_320p_1x1x8_150e_activitynet_video_flow	340x256	8x2	ResNet50	Kinetics400	57.51	83.02	5780	ckpt	log	json
tsn_r50_320p_1x1x8_150e_activitynet_clip_flow	340x256	8x2	ResNet50	Kinetics400	59.51	82.69	5780	ckpt	log	json

HVU

config[1]	tag category	resolution	gpus	backbone	pretrain	mAP	HATNet[2]	HATNet-multi[2]	ckpt	log	json
tsn_r18_1x1x8_100e_hvu_action_rgb	action	short-side 256	8x2	ResNet18	ImageNet	57.5	51.8	53.5	ckpt	log	json
tsn_r18_1x1x8_100e_hvu_scene_rgb	scene	short-side 256	8	ResNet18	ImageNet	55.2	55.8	57.2	ckpt	log	json
tsn_r18_1x1x8_100e_hvu_object_rgb	object	short-side 256	8	ResNet18	ImageNet	45.7	34.2	35.1	ckpt	log	json
tsn_r18_1x1x8_100e_hvu_event_rgb	event	short-side 256	8	ResNet18	ImageNet	63.7	38.5	39.8	ckpt	log	json
tsn_r18_1x1x8_100e_hvu_concept_rgb	concept	short-side 256	8	ResNet18	ImageNet	47.5	26.1	27.3	ckpt	log	json
tsn_r18_1x1x8_100e_hvu_attribute_rgb	attribute	short-side 256	8	ResNet18	ImageNet	46.1	33.6	34.9	ckpt	log	json
-	Overall	short-side 256	-	ResNet18	ImageNet	52.6	40.0	41.3	-	-	-

[1] For simplicity, we train a specific model for each tag category as the baselines for HVU.

[2] The performance of HATNet and HATNet-multi are from the paper Large Scale Holistic Video Understanding. The proposed HATNet is a 2 branch Convolution Network (one 2D branch, one 3D branch) and share the same backbone(ResNet18) with us. The inputs of HATNet are 16 or 32 frames long video clips (which is much larger than us), while the input resolution is coarser (112 instead of 224). HATNet is trained on each individual task (each tag category) while HATNet-multi is trained on multiple tasks. Since there is no released codes or models for the HATNet, we just include the performance reported by the original paper.

Notes:

1. The gpus indicates the number of gpu we used to get the checkpoint. It is noteworthy that the configs we provide are used for 8 gpus as default. 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. The inference_time is got by this benchmark script, where we use the sampling frames strategy of the test setting and only care about the model inference time, not including the IO time and pre-processing time. For each setting, we use 1 gpu and set batch size (videos per gpu) to 1 to calculate the inference time.
3. The values in columns named after "reference" are the results got by training on the original repo, using the same model settings.

For more details on data preparation, you can refer to

• preparing_ucf101
• preparing_kinetics
• preparing_sthv1
• preparing_sthv2
• preparing_mit
• preparing_mmit
• preparing_hvu
• preparing_hmdb51

Train

You can use the following command to train a model.

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

Example: train TSN model on Kinetics-400 dataset in a deterministic option with periodic validation.

python tools/train.py configs/recognition/tsn/tsn_r50_1x1x3_100e_kinetics400_rgb.py \
    --work-dir work_dirs/tsn_r50_1x1x3_100e_kinetics400_rgb \
    --validate --seed 0 --deterministic

For more details, you can refer to Training setting part in getting_started.

Test

You can use the following command to test a model.

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

Example: test TSN model on Kinetics-400 dataset and dump the result to a json file.

python tools/test.py configs/recognition/tsn/tsn_r50_1x1x3_100e_kinetics400_rgb.py \
    checkpoints/SOME_CHECKPOINT.pth --eval top_k_accuracy mean_class_accuracy \
    --out result.json

For more details, you can refer to Test a dataset part in getting_started.