# 5. Getting Started with Pre-trained SlowFast Models on Kinetcis400¶

Kinetics400 is an action recognition dataset of realistic action videos, collected from YouTube. With 306,245 short trimmed videos from 400 action categories, it is one of the largest and most widely used dataset in the research community for benchmarking state-of-the-art video action recognition models.

SlowFast is a new 3D video classification model, aiming for best trade-off between accuracy and efficiency. It proposes two branches, fast branch and slow branch, to handle different aspects in a video. Fast branch is to capture motion dynamics by using many but small video frames. Slow branch is to capture fine apperance details by using few but large video frames. Features from two branches are combined using lateral connections.

In this tutorial, we will demonstrate how to load a pre-trained SlowFast model from gluoncv-model-zoo and classify a video clip from the Internet or your local disk into one of the 400 action classes.

## Step by Step¶

We will try out a pre-trained SlowFast model on a single video clip.

First, please follow the installation guide to install MXNet and GluonCV if you haven’t done so yet.

import matplotlib.pyplot as plt
import numpy as np
import mxnet as mx
from mxnet import gluon, nd, image
from mxnet.gluon.data.vision import transforms
from gluoncv.data.transforms import video
from gluoncv import utils
from gluoncv.model_zoo import get_model


Then, we download the video and extract a 64-frame clip from it. Note that SlowFast has two branches, which require different inputs. The fast branch needs more frames, which we sample every other frame (stride=2). The slow branch needs less frames, which we sample every 16th frame (stride=16). In the end, we have 32 frames as the input to the fast branch and 4 frames to the slow branch. Hence, the final input to the whole network is a clip of 36 frames.

from gluoncv.utils.filesystem import try_import_decord
decord = try_import_decord()

url = 'https://github.com/bryanyzhu/tiny-ucf101/raw/master/abseiling_k400.mp4'
fast_frame_id_list = range(0, 64, 2)
slow_frame_id_list = range(0, 64, 16)
frame_id_list = list(fast_frame_id_list) + list(slow_frame_id_list)
video_data = vr.get_batch(frame_id_list).asnumpy()
clip_input = [video_data[vid, :, :, :] for vid, _ in enumerate(frame_id_list)]


Now we define transformations for the video clip. This transformation function does three things: center crop each image to 224x224 in size, transpose it to num_channels*num_frames*height*width, and normalize with mean and standard deviation calculated across all ImageNet images.

transform_fn = video.VideoGroupValTransform(size=224, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
clip_input = transform_fn(clip_input)
clip_input = np.stack(clip_input, axis=0)
clip_input = clip_input.reshape((-1,) + (36, 3, 224, 224))
clip_input = np.transpose(clip_input, (0, 2, 1, 3, 4))


Out:

Video data is downloaded and preprocessed.


Next, we load a pre-trained SlowFast model with ResNet50 as backbone.

model_name = 'slowfast_4x16_resnet50_kinetics400'
net = get_model(model_name, nclass=400, pretrained=True)
print('%s model is successfully loaded.' % model_name)


Out:

slowfast_4x16_resnet50_kinetics400 model is successfully loaded.


Finally, we prepare the video clip and feed it to the model.

pred = net(nd.array(clip_input))

classes = net.classes
topK = 5
ind = nd.topk(pred, k=topK)[0].astype('int')
print('The input video clip is classified to be')
for i in range(topK):
print('\t[%s], with probability %.3f.'%
(classes[ind[i].asscalar()], nd.softmax(pred)[0][ind[i]].asscalar()))


Out:

The input video clip is classified to be
[abseiling], with probability 0.996.
[rock_climbing], with probability 0.004.
[ice_climbing], with probability 0.000.
[paragliding], with probability 0.000.
[climbing_a_rope], with probability 0.000.


We can see that our pre-trained model predicts this video clip to be abseiling action with high confidence.

## Next Step¶

If you would like to dive deeper into training SlowFast models on Kinetics400, feel free to read the next tutorial on Kinetics400.

Total running time of the script: ( 0 minutes 37.687 seconds)

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