Recent comments in /f/deeplearning
sEi_ t1_je6yhig wrote
Reply to comment by artsybashev in AI Startup Cerebras releases open source ChatGPT-like alternative models by Time_Key8052
I do not know their model, but playing with a 13B model, albeit small is fun on my potato PC (Alpaca 13B). Fun, but nothing more than that.
viertys OP t1_je6oxpk wrote
Reply to comment by Seahorsejockey in Improvements/alternatives to U-net for medical images segmentation? by viertys
512x512, but I can modify their dimensions
Seahorsejockey t1_je6m3oa wrote
How Big are your images (resolution HxW)?
I_will_delete_myself t1_je6h8xr wrote
Reply to comment by artsybashev in AI Startup Cerebras releases open source ChatGPT-like alternative models by Time_Key8052
The domain name and the prefix also doesn't make it seem sketch at all whatsoever. tistory.com and gpt4chat makes me think it's trying to abuse SEO
artsybashev t1_je65qs7 wrote
13B model is quite small. Given that the company is focusing in AI hardware, the dataset and other parts of the model might be lagging a bit. Lack of comparison to other models also suggests that the performance is not that good.
AI-without-data OP t1_je5078l wrote
Reply to comment by deepForward in Training only Labelled Bbox for Object Detection. by AI-without-data
I see. I think changing the threshold of confidence score and probaility is good idea. I should try the ways step by step. Thank you!
sEi_ t1_je4obfb wrote
Reply to Noob question: is there a site where you can provide you equipment to help process data and gain credits for further use? by Fonsecafsa
Something like https://stablehorde.net/ comes to mind.
I like your mindset. Keep it up.
usesbinkvideo t1_je14wgm wrote
Reply to Noob question: is there a site where you can provide you equipment to help process data and gain credits for further use? by Fonsecafsa
This is gonna sound like a shill, but throwing it out there anyway for your edification: check out the Golem Network
--dany-- t1_je134t7 wrote
Reply to Noob question: is there a site where you can provide you equipment to help process data and gain credits for further use? by Fonsecafsa
The idea has been explored by vast.so and runpod. Not exactly credit but real cash.
deepForward t1_je0ktqx wrote
Reply to comment by AI-without-data in Training only Labelled Bbox for Object Detection. by AI-without-data
Try the easy way first :
Build a model that only learns chairs, with all labeled chairs you have and ignore anything else at first.
Try also image data augmentations and see if it helps.
You are not looking at having the best score, actually you dont care about your score as long as you can label new chairs.
You mostly want to tune the model so that you don't have false positives (and introduce noise in your labels). False negatives are OK, and will occur if you tune the model so that FP are zero. You can tune for instance the threshold on a confidence score or class probability (check the model you're using).
You can also build a basic image validation tool with jupyter notebook widgets, steamlit, or your favorite tool, if you want to validate quickly by hand that they are no false positives. It's a very good exercise.
Good luck !
Runkli t1_je0gtaf wrote
Reply to Noob question: is there a site where you can provide you equipment to help process data and gain credits for further use? by Fonsecafsa
Not exactly what you're asking for, but related: BOINC Project by Berkeley, and Folding@Home, and other @home projects
jcoffi t1_je0epsy wrote
Reply to Noob question: is there a site where you can provide you equipment to help process data and gain credits for further use? by Fonsecafsa
I've also thought about this but didn't find anything. I love the idea.
GradientDescenting t1_jdzujdt wrote
Reply to Noob question: is there a site where you can provide you equipment to help process data and gain credits for further use? by Fonsecafsa
There is free GCP compute on kaggle.com
qphyml t1_jdzg13b wrote
Reply to comment by AI-without-data in Training only Labelled Bbox for Object Detection. by AI-without-data
Good luck! Would be great to hear how it goes and which insights you get!
AI-without-data OP t1_jdzb8fd wrote
Reply to comment by qphyml in Training only Labelled Bbox for Object Detection. by AI-without-data
Ok I appreciate! I'm trying to filter out images.
qphyml t1_jdz7ndt wrote
Reply to comment by AI-without-data in Training only Labelled Bbox for Object Detection. by AI-without-data
I think you can do it both ways (with or without filtering) and compare. Just speculating now, but the filtering could potentially affect the performance on the other classes (since you change the model’s training path for those classes ). But my guess is that that should not be a big issue, so I’d probably go about it the way you described if I had to pick one strategy.
AI-without-data OP t1_jdy05d6 wrote
Reply to comment by stuv_x in Training only Labelled Bbox for Object Detection. by AI-without-data
Ok I will try to modify loss function as you say. Thank you!
stuv_x t1_jdxwmwm wrote
It might not affect training as much as you think - you could also modify your loss function so it doesn’t penalise false positives as much as false negatives
BellyDancerUrgot t1_jdx6w01 wrote
Reply to comment by ChingChong--PingPong in Do we really need 100B+ parameters in a large language model? by Vegetable-Skill-9700
The implication was, most of accessible textual data. Which is true. The exaggeration was such cuz it’s a language model first and foremost and previous iterations like gpt3 and 3.5 were not multimodal. Also , as far as accounts go, that’s a huge ‘?’ atm. Especially going by tweets like these
https://twitter.com/katecrawford/status/1638524011876433921?s=46&t=kwpwSgfnJvGe6J-1CEe_5Q
The reality is , we and you don’t have the slightest clue regarding what it was trained on and msft has sufficient compute to train on all of the text data on the internet.
When it comes to multimodal media we don’t really need to train a model on the same amount of data required for text.
ChingChong--PingPong t1_jdwfooc wrote
Reply to comment by BellyDancerUrgot in Do we really need 100B+ parameters in a large language model? by Vegetable-Skill-9700
It was not trained on basically the entire internet. Not even close. Even if they trained it on all the pages Google has indexed, that's not even close to the entire internet, and I'm not even talking about the dark web. Toss in all the data behind user accounts, paywalls, intranets. Then toss on all the audio and video on all the social media and audio/video platforms and OpenAI couldn't afford to train, much less optimize, much less host a model of that size.
[deleted] t1_jdvn3rn wrote
Reply to comment by AI-without-data in Training only Labelled Bbox for Object Detection. by AI-without-data
[deleted]
AI-without-data OP t1_jdviqi9 wrote
Reply to comment by thebruce87m in Training only Labelled Bbox for Object Detection. by AI-without-data
I got it. Thank you so much for your answer.
thebruce87m t1_jdvhv7r wrote
Reply to comment by AI-without-data in Training only Labelled Bbox for Object Detection. by AI-without-data
If you want it to be good at detecting books then yes, all books should be labelled.
If they are not, what are the implications? Perhaps book detections will have lower confidence than they should? Maybe it will ignore some styles of books?
AI-without-data OP t1_jdvhnjr wrote
Reply to comment by deepForward in Training only Labelled Bbox for Object Detection. by AI-without-data
Thank you. But I don't understand it clearly.
Do people train the model in that way as well? In the COCO dataset, some images contain objects that are not labeled but are listed in the classes.
If people follow your suggested method for training the model, they would need to first filter out images with perfectly labeled objects (no missed labels) from the COCO dataset and use that filtered data to train the model. Then they would need to run the model on the remaining data to obtain labels for objects that are not included in the dataset, and update the entire dataset accordingly. Is this correct?
cma_4204 t1_je79lg9 wrote
Reply to Improvements/alternatives to U-net for medical images segmentation? by viertys
SMP has a lot of different choices for architecture other than unet, and a ton of different encoders. I like deeplabv3+/unet with regnety encoder, works well for most things https://github.com/qubvel/segmentation_models.pytorch