I have videos that are each about 30-40 mins long. With the first 5-10 mins (at 60fps, can be down-sampled to 5fps) are one type of activity that would be categorized by label-1 and the rest of the video as label-2. I started off by using CNN-LSTM to do this prediction (Resnet-50 + LSTM + FC-classifier).

Using Pytorch...

For training my initial approach was to treat this as an activity classification task. So, I split my videos into smaller segments with each segment having a label.

video1.mp4 (5 mins) --> label-1

Split into 30 seconds -->

video1_0001.mp4 --> label-1




video1_0010.mp4 --> label-1

But, with this strategy even after 100 epochs the network does not train. I can at the most fit about 40 frames on the 2-GPUs, but a 30 second segment of video @5fps has about 150 frames. Any further subsampling seems to not capture the essence of the video segment.

I also tried training without shuffling and with single thread, so a single stream is loaded continuously. But perhaps its not the right strategy.

I wanted to request some help on how to tackle the problem. I would really appreciate some insights into a training strategy for this problem.

  • Is using CNN-LSTM the right strategy here?

=== Update ===

After reading a few other posts on similar topics,

I feel that to get the network to see a larger part of the sequence, I will have to use more GPUs or resize images. However, since the pretrained Resnet accepts 224x224, I will need more GPUs. But I am curious; is there another strategy? Because the question could also be about what is the ideal segment length that would enable the network to learn.

From my perception, a 30 second video sampled @5fps at the bare minimum captures the context. From observation so far, going below this number hasn't allowed the network to learn.


1 Answer 1


Using CNN into an LSTM is definitely a valid option for your task. There are also papers on integrating the LSTM mechanism directly into the convolution layers (like RCNN), which would be an alternative to try out. As you already identified, these architectures require a lot of memory, because your classifier will depend on the full sequence of images and you have to store the gradient for each of them.

Ways to attenuate the problem with memory are:

  • Resizing the images
  • Choosing a smaller sequence length
  • Smaller networks
  • Preencoding the images (only works if the convolutional encoder isn't trained)
  • ...

Smaller networks might be an option for you. ResNet50 ist quite large, so you might want to explore some smaller convolutional encoders like EfficientNet. Below is a figure plotting number of parameters vs ImageNet top-1 accuracy for multiple architectures. Complementary, you can save even more parameters by substituting the LSTM with a GRU. The number of parameters in the GRU scales quadratically with layer size, so reducing layer size of LSTM/GRU is worth experimenting with.

Another option when you are using pretrained models is to freeze the weights of the pretrained model and preencode the images images in your dataset. So first, apply your ConvNet to each frame of your video dataset and store the embedding. This gives you a dataset with just the embeddings on which you can then train the LSTM independently.

Having solved the problem, you may be able to experiment with longer sequences and experiment a bit to find out what works. For more help you may want to include a bit more information about your data. It sounds like you are doing human action classification? If so you can explore paperswithcode a little to get inspiration.

Image Source

  • $\begingroup$ Thank you @Chillston, appreciate the insight. I agree with you regarding the options, Resizing the images - depends on the pretrained network. Choosing a smaller sequence length - This option hasn't yielded much benefit. Smaller networks - Will try it, I didn't think about it. Preencoding the images - This option cross my mind, but I was not sure if it would provide much benefit. My image domain is not natural image (such as imagenet). Using a GRU - I will read up about it, I was focused on LSTMs because they are the state-of-the-art. $\endgroup$
    – ekmungi
    Mar 19 at 16:39
  • $\begingroup$ My domain is not exactly action recognition, I would like to apply them to medical images. Hence to continue training the backbone feature extractor. $\endgroup$
    – ekmungi
    Mar 19 at 16:46
  • $\begingroup$ Do you think Resnet + LSTM is able to capture the temporal context sufficienty? From the link you suggested (thanks again for that suggestion), I came across spatio-temporal Resnets that include an additional temporal context from optical flow. My original consideration was to use the output of Flownet as an input to the LSTM network for further classification. What do you think? $\endgroup$
    – ekmungi
    Mar 19 at 17:16
  • $\begingroup$ Regarding GRU vs. LSTM: GRUs can outperform LSTMs, depending on the data (e.g. this paper). If you need SOTA temporal processing performance, then the self-attention is the way to go (e.g. Transformer). A general piece of advice is to keep things as simple as possible until you have something that works :) To give you my opinion on the Resnet + LSTM architecture, a few more details about the problem will be very helpful. What exactly is depicted in the images and whats their temporal relation? $\endgroup$
    – Chillston
    Mar 19 at 18:07
  • $\begingroup$ Thanks. The data I am looking at are endoscopic images. And I am trying to determine the direction of motion in these videos. $\endgroup$
    – ekmungi
    Mar 24 at 17:05

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