Readme for video models

torchvision/models/video/README.md

@@ -0,0 +1,60 @@
+## Video classification models
+
+Starting with version `0.4.0` we have introduced support for basic video tasks and video classification modelling.
+At the moment, our pretraining consists of base implementation of popular resnet-based video models [0], together with their
+basic variant pre-trained on Kinetics400 [1]. Although this is a standard benchmark pre-training, we are always considering what is the best for the community.
+
+Additional documentation can be found [here](https://pytorch.org/docs/stable/torchvision/models.html#video-classification). 
+
+### Kinetics400 dataset pretraining parameters
+
+See reference training script [here](https://github.com/pytorch/vision/blob/master/references/video_classification/train.py):
+
+- input size: [3, 16, 112, 112]
+- input space: RGB
+- input range: [0, 1]
+- mean: [0.43216, 0.394666, 0.37645]
+- std: [0.22803, 0.22145, 0.216989]
+- number of classes: 400
+
+Input data augmentations at training time (with optional parameters):
+
+0. ToTensor
+1. Resize (128, 171)
+2. Random horizontal flip (0.5)
+3. Normalization (mean, std, see values above)
+4. Random Crop (112, 112)
+
+Input data augmentations at validation time (with optional parameters):
+
+0. ToTensor
+1. Resize (128, 171)
+2. Normalization (mean, std, see values above)
+3. Center Crop (112, 112)
+
+This translates in the following set of command-line arguments (please note that learning rate and batch size end up being scaled by the number of GPUs; all our models were trained on 8 nodes with 8 V100 GPUs each for a total of 64 GPUs):
+```
+# number of frames per clip
+--clip_len 16 \ 
+# allow for temporal jittering
+--clips_per_video 5 \
+--batch-size 24 \
+--epochs 45 \
+--lr 0.01 \
+# we use 10 epochs for linear warmup
+--lr-warmup-epochs 10 \
+# learning rate is decayed at 20, 30, and 40 epoch by a factor of 10
+--lr-milestones 20, 30, 40 \
+--lr-gamma 0.1 
+```
+
+### Additional video modelling resources
+
+- [Video Model Zoo](https://github.com/facebookresearch/VMZ)
+- [PySlowFast](https://github.com/facebookresearch/SlowFast)
+
+### References
+
+[0] _D. Tran, H. Wang, L. Torresani, J. Ray, Y. LeCun and M. Paluri_: A Closer Look at Spatiotemporal Convolutions for Action Recognition. _CVPR 2018_ ([paper](https://research.fb.com/wp-content/uploads/2018/04/a-closer-look-at-spatiotemporal-convolutions-for-action-recognition.pdf))
+
+[1] _W. Kay, J. Carreira, K. Simonyan, B. Zhang, C. Hillier, S. Vijayanarasimhan, F. Viola, T. Green, T. Back, P. Natsev, M. Suleyman, A. Zisserman_: The Kinetics Human Action Video Dataset ([paper](https://arxiv.org/abs/1705.06950))