Hi, Is lesson 4 not recorded fully? I’m only able to see 2hrs of content in Lesson 4 live link now.
There was a crash during the live recording, but as mentioned upthread, you can find the rest using these very helpful links: https://forums.fast.ai/t/lesson-4-links-to-different-parts-in-the-video/30338
It would be helpful if the course notebooks could have the top cell specify which version of fastai they were created with. I’m having a hard time getting the Planet notebook to work now, seeing this error:
ImageItemList is not defined
I am using fastai==1.0.22
it’s from 1.0.24
I only see 1.0.22 in the official releases?
Thanks though … I’ll try with an upgrade!
Maybe , I was updated a bit to early and my dataset and dataloader creation for test set was broken with 1.0.24. I will figure out ho to handle it later today.
Something related to this thread https://forums.fast.ai/t/error-when-adding-in-a-test-set-via-add-test/30413
Using 1.0.24 worked for me. Thanks @ademyanchuk.
Next question. Is there an interpretation class for multi-label classification? What’s the right way for us to do similar analyses here?
I would like to see confusion matrix, top losses, etc for my multi-label classification of pizza toppings, but the single image classification interp object doesn’t seem to be working for this.
I didn’t dig deep in that case. So, have nothing to say, sorry)
Even more helpful would have been to stick with a single version for a course, IMHO.
I got the reply from Jithin James. It worked.
The correct syntax is: data = (TextList.from_csv(path, ‘texts.csv’, col=‘text’) .split_from_df(col=2) .label_from_df(cols=0) .databunch()) This is for fastai version: import fastai fastai.version : ‘1.0.24’
On running the example bear classifier, I’m consistently classified as a black bear and my wife a teddy. Guess we know who to look out for
Running camvid segmentation notebook from lesson 3 as is from the repo but not able to get same results… The accuracy is going almost close to zero. Using fastai version 1.0.24
Anyone else facing this issue?
Anyone know why sometimes the training is super slow, with the same configuration but in 2 differences time, the training time is much different ? Sometimes I can fix it by reboot my computer but I don’t know why.
I found that if I restart the kernel so I can train much faster. I think the reason is because of the GPU memory. GPU memory not being freed after training is over . I will read to see how to free the memory without restarting the kernel
What human Human overfitting feels like
After banging my head around this for a lot of time, couldn’t identify the issue causing this unusual behaviour. Did a system reboot and looks like that has fixed it
Yeah I also have problems understanding it. I referred to the dev_nb in the fastai_old repo. But couldn’t get myself to understand it. It will help a lot if Jeremy tells about them in the coming lessons
I noticed that Lr_find() in version 1.0 is significantly slower than the previous one. I also noticed that it runs 4 times, so does that mean it reduces lr for each mini batch through 4 epochs? I’m confused
The lesson 3 head-pose notebook currently gives out an error when the data object is created
@jm0077 has created an error trace
The notebook worked fine until I pulled and conda installed the new updates (fastai version 1.0.27)
I pondered about YouTube’s auto-caption feature while watching lesson-4 (NLP).
As per my guess, I think they use speech to text ML models to generate those captions.
Is there any possibility that in-addition they might also be using NLP to enhance the prediction ?
I think speech-recognition + NPL would really further improve the auto-caption performance.