prats226

7 ms·

61.
by prats226 2y ago
Yeah this new benchmark is kind of inspired by these existing benchmarks, things that are missing wrt automation
62.
Show HN: OCR Benchmark Focusing on Automation(nanonets.com)
58 points by prats226 2y ago|21 comments
63.
by prats226 2y ago
Traditional OCR's usually have detection + recognition pipeline. So they will detect every word and then try to predict the text for every word. Errors obviously can happen in both parts, eg some words not detected which will get misse
64.
by prats226 2y ago
In most multimodal archiectures, images and text is fed as input and they live in same latent space, however major difference is most networks are trained to decode latent vector into text but never an image (Atleast in same network). This
65.
Ask HN: Any truly multi-modal transformer architectures?
3 points by prats226 2y ago|4 comments
66.
Deep learning behind document processing(nanonets.com)
49 points by prats226 5y ago|0 comments
67.
The unreasonable effectiveness of language models in document processing(nanonets.com)
4 points by prats226 5y ago|0 comments
68.
The unreasonable effectiveness of language models in document processing(nanonets.com)
16 points by prats226 5y ago|0 comments
69.
How does document processing work with multiple languages?(nanonets.com)
5 points by prats226 5y ago|0 comments
70.
by prats226 7y ago
None. I just got curious about it. For my startup, we have full time employees on board.
71.
Ask HN: How do people verify time put in by contractors?
11 points by prats226 7y ago|14 comments
72.
by prats226 7y ago
"CNNs versus GCNs" is not necessarily correct? You will need to apply GCN on top of CNN to get the structure out of otherwise unstructured text?
73.
by prats226 7y ago
To get this personal information, asking for government-issued ID gives them one more data point on you and reinforcement of identity. So a win for them as well I guess?
74.
by prats226 7y ago
Yeah nowadays with post-training quantization techniques as well as things like squeezenet which is quantization aware training technique, models are becoming fairly small to be able to run on phone smoothly
75.
by prats226 7y ago
If it was in google IO, it will be some consumer use case probably? Althought there are a lot of commercial use cases of this technology. For example, you can train a simple classifier on top of pose which will help you record time spent on
76.
by prats226 7y ago
They should work in real time. Atleast the architecture looks like end to end neural network so just like other CNN based models, this should as well work in real time after quantization etc
77.
Ask HN: How do you hire a front-end developer?
2 points by prats226 8y ago|1 comments
78.
How to Easily Do Object Detection on Drone Imagery Using Deep Learning(blog.nanonets.com)
10 points by prats226 8y ago|1 comments
79.
by prats226 8y ago
Hey guys, we are building https://nanonets.com/drone We were working with a customer from South Africa to monitor construction progress using drone imagery and wanted to share case study based on our experience here. Hope i
80.
by prats226 8y ago
Is it just heating of brain required or blood flow? One crazy way to test would be to artificially heat the brain!
81.
Nanonets – Deep Learning Platform (YC W17) Is Hiring Software Engineers in India(angel.co)
1 points by prats226 8y ago
82.
by prats226 9y ago
There is need of custom model in a lot of businesses like you want to identify only a specific kind of product from rest of similar looking ones or find only defective pieces and where you cannot collect 10's of thousands of images fro
83.
by prats226 9y ago
I think what OP meant was ASIC specific to deep learning like TPU's. However as I see, current frameworks are not matured enough to support GPU's and TPU's with exact same code. Also there are no standards so every big org is
84.
by prats226 9y ago
We do provide docker option as well. Federated learning looks like a good way of edge computing + deep learning to offer personalized models as well as use them to improve general model.
85.
by prats226 9y ago
We also use transfer learning a lot. Using transfer learning is but tricky because sometimes you might upset generalised weights of pretrained network with bad hyperparameters. We had written a blog before for using transfer learning as wel
86.
by prats226 9y ago
Added a section in blog to explain end to end usage
87.
by prats226 9y ago
Hey, good suggestion to benchmark against other SOC's as well. I heard raspberry pi recently added support for external graphics card. Haven't tried yet.
88.
by prats226 9y ago
> Your "sorta-answer" suggests "yes", but the title "How to easily Detect Objects with Deep Learning on Raspberry Pi" suggests that your answer should be "no". How am I suggesting "yes"?
89.
by prats226 9y ago
Off-device and on-device are alternatives to do deep learning inference on pi, both with pros and cons. For an example, with on-device inference, you will need to run a smaller architecture to get decent FPS and will also be dependent on ha
90.
by prats226 9y ago
Yeah its total runtime of script. However you can get upto 3-4 FPS with more optimizations. We are going to try more quantization options soon with release of tensorflow 1.7 and will report our findings (Will post updates in blog). Also pi

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