elandau25

6 ms·

1.
by elandau25 3y ago
Good question! We are focused on vision at the moment, but we are indeed looking at text in the future. Happy to connect and have a chat around that if you are open as we would be curious to hear more about new text use cases
2.
by elandau25 3y ago
Thank you! It was great working with you and your team as well :)
3.
by elandau25 3y ago
Thanks Kyle, appreciate it! Has been very nice collaborating with you!
4.
Meta AI's Megabyte Model Architecture Explained(encord.com)
2 points by elandau25 3y ago|0 comments
5.
by elandau25 4y ago
Hello! I wrote the article so happy to answer this. It is partially feature engineering but partially not. It’s essentially using feature engineering to curate/correct a dataset, but a neural network as the actual end model without exp
6.
by elandau25 5y ago
It is important to note that these micro-models are only supposed to be used in the annotation process. During annotation there is a separate process for QA where there will be some form of human supervision. Micro-models are NOT supposed t
7.
by elandau25 5y ago
Thanks! I might do a follow-up article on the topic and will think about how to incorporate this in!
8.
by elandau25 5y ago
That's a good idea. It will likely be trickier to apply a method like this for text. Decomposition of the problem is less obvious than with vision tasks.
9.
by elandau25 5y ago
Fair enough, but target data in this sense IS a full distribution of Batmen. This approach is towards the goal of creating a broad dataset and fitting a full Batman model. We are training on a narrower subset of our actual target data and f
10.
by elandau25 5y ago
If we are taking wikipedia as ground truth, the next line is: An overfitted model is a statistical model that contains more parameters than can be justified by the data. Another definition from https://www.ibm.com/cloud&#x2F
11.
by elandau25 5y ago
I see what you are saying, but in that context then you lose what most people's intuitive definition of overfitting is. If I train a model on one image as my train set and then change one random pixel and run that model on this eval se
12.
by elandau25 5y ago
Thanks! You have the application correct, but there are many ways by which we use this. An example is if you have trying to build models that require sequentially annotated images(like action recognition). Another is creating many micro-mod
13.
by elandau25 5y ago
Hi everyone, I wrote the article. I do consider this overfitting because we are training on these frames way more time than would be normally advised for the size of the training set such that the error is essentially zero for these frames.
14.
How I learned to stop worrying and love overfitting models(eric-landau.medium.com)
4 points by elandau25 5y ago|0 comments
15.
Our Post Demo Day Fundraising Stats(Cord – YC W21)(ulrik-hansen.medium.com)
4 points by elandau25 5y ago|0 comments
16.
by elandau25 6y ago
Great question. Superb AI also seems like a great tool. I’m not sure if they have video annotation though. We are different from them in a bunch of ways, but the biggest one is that we are optimized for handling videos, sequential images, a
17.
by elandau25 6y ago
Thank you! We think Hasty is a pretty neat tool, I am just not sure they have annotation for video datasets? Our platform is really optimized for video and sequential image. A GI rotoscope is right up our alley. If you have any more of thos
18.
by elandau25 6y ago
You can do either! We offer a bunch of automation features directly through the Web App but people have also used the SDK to write their own algorithms. We have seen a lot of different annotation processes now so we can often direct people
19.
by elandau25 6y ago
As far as I can tell Roboflow is more focused on being an end to end platform for AI. The customers we work with generally want to retain more control over their model building process, we just help them with automating as much of the annot
20.
by elandau25 6y ago
Hi Eric from Cord here. Scale is a great company and they have done really well in AV especially. The issue with them is that they require you to send your data overseas to be annotated by a human workforce. They also probably have a bunch
21.
by elandau25 6y ago
Thank you!
22.
What I learned from my first month at Y Combinator(eric-landau.medium.com)
9 points by elandau25 6y ago|2 comments
23.
by elandau25 6y ago
I hear you, but I don't even think the labor intensiveness is a lost cause here. Labor intensiveness in mining insights from a dataset is worth way more than labor intensiveness in manual labelling. I think we are lazily giving up our
24.
by elandau25 6y ago
Hi Sriku, with regards to your first point, not necessarily. I mentioned in another comment, but the model you are using the labels to build and the labelling process are related but not the same, they have different fundamental constraints
25.
by elandau25 6y ago
Feature extraction from a pre-trained model? Sure, again depends how you use it. We have used feature extraction + clustering for some of these labelling tasks successfully in the past.
26.
by elandau25 6y ago
Really just depends on the task. For this particular case I used a Faster-RCNN model with weights pretrained on the COCO dataset
27.
by elandau25 6y ago
Yea, so that's more of a comment on the accuracy of the auto generated labels, because this approach doesn't assume a different representative set of data than with human labelled data, just that less of the data is human labelled
28.
by elandau25 6y ago
I see it a bit differently. I see it as two separate(but correlated) tasks. There is labelling the data and building a robust model. There is a nuanced gap between the two. The labelling task and the model task live a different constraint s
29.
by elandau25 6y ago
Definitely easier said than done, but the process at least makes labelling interesting. Sometimes you run into roadblocks where you can't get past just having a human doing some element of the labelling, but once you do have a few algo
30.
by elandau25 6y ago
Hi Imnimo, I wrote the article and definitely understand your concerns. The point is not the specific steps I took working in general for most datasets, but more the overall idea of using a more data science-y approach to labelling rather t

More ›