I didnt have internet turned on 
got it working now.
Next in the tutorial it instructs me to build an image uploader
import ipywidgets as widgets
from IPython.display import display
from PIL import Image
uploader = widgets.FileUpload(accept='image/*', multiple=False)
display(uploader)
Iāve tried a few versions but I always get a
Error displaying widget: model not found
Are you able to make an image uploader on kaggle?
This is how Iāve done it:
import ipywidgets as widgets
uploader = widgets.FileUpload()
uploader
Hello,
Jeremy uses forest to discriminate what is a bird and what is not in the lesson. Could he have used any other object, such as a chair, a construction site, or really anything else ?
Iām asking because I donāt know if the object to compare to the thing we want to recognize in the image classifier has somehow to be related to this object: e.g. we would compare birds and forests because they are in the same ādomainā ā¦ does it matter at all ?
thanks in advance !
It could be similar, or it could be totally different. It doesnāt matter!
Try adding the following parameter and argument to your DataBlock:
batch_tfms=aug_transforms()
What this essentially does is twist, warp, crop, and rotate your images in various ways so your model can learn to āseeā them in various ways. It helps improve accuracy by quite a bit.
thx ! and also thx for the tip below 
Yes, it could have been whatever.
Hi everybody! Iām running the Kaggle notebook for the first lesson, but when running line 20:
from fastdownload import download_url
dest = 'bird.jpg'
download_url(urls[0], dest, show_progress=False)
from fastai.vision.all import *
im = Image.open(dest)
im.to_thumb(256,256)
I get a truly enormous error consisting of the below followed by (what looks like) the html for a webpage or several webpages about birds, which is labelled as āHTTP error 404, ===ERROR BODY===ā
HTTPError Traceback (most recent call last)
/tmp/ipykernel_17/3449441972.py in <module>
1 from fastdownload import download_url
2 dest = 'bird.jpg'
----> 3 download_url(urls[0], dest, show_progress=False)
4
5 from fastai.vision.all import *
/opt/conda/lib/python3.7/site-packages/fastdownload/core.py in download_url(url, dest, timeout, show_progress)
21 pbar.total = tsize
22 pbar.update(count*bsize)
---> 23 return urlsave(url, dest, reporthook=progress if show_progress else None, timeout=timeout)
24
25 # Cell
/opt/conda/lib/python3.7/site-packages/fastcore/net.py in urlsave(url, dest, reporthook, headers, timeout)
182 dest = urldest(url, dest)
183 dest.parent.mkdir(parents=True, exist_ok=True)
--> 184 nm,msg = urlretrieve(url, dest, reporthook, headers=headers, timeout=timeout)
185 return nm
186
/opt/conda/lib/python3.7/site-packages/fastcore/net.py in urlretrieve(url, filename, reporthook, data, headers, timeout)
147 def urlretrieve(url, filename=None, reporthook=None, data=None, headers=None, timeout=None):
148 "Same as `urllib.request.urlretrieve` but also works with `Request` objects"
--> 149 with contextlib.closing(urlopen(url, data, headers=headers, timeout=timeout)) as fp:
150 headers = fp.info()
151 if filename: tfp = open(filename, 'wb')
/opt/conda/lib/python3.7/site-packages/fastcore/net.py in urlopen(url, data, headers, timeout, **kwargs)
106 if not isinstance(data, (str,bytes)): data = urlencode(data)
107 if not isinstance(data, bytes): data = data.encode('ascii')
--> 108 try: return urlopener().open(urlwrap(url, data=data, headers=headers), timeout=timeout)
109 except HTTPError as e:
110 e.msg += f"\n====Error Body====\n{e.read().decode(errors='ignore')}"
/opt/conda/lib/python3.7/urllib/request.py in open(self, fullurl, data, timeout)
529 for processor in self.process_response.get(protocol, []):
530 meth = getattr(processor, meth_name)
--> 531 response = meth(req, response)
532
533 return response
/opt/conda/lib/python3.7/urllib/request.py in http_response(self, request, response)
639 if not (200 <= code < 300):
640 response = self.parent.error(
--> 641 'http', request, response, code, msg, hdrs)
642
643 return response
/opt/conda/lib/python3.7/urllib/request.py in error(self, proto, *args)
567 if http_err:
568 args = (dict, 'default', 'http_error_default') + orig_args
--> 569 return self._call_chain(*args)
570
571 # XXX probably also want an abstract factory that knows when it makes
/opt/conda/lib/python3.7/urllib/request.py in _call_chain(self, chain, kind, meth_name, *args)
501 for handler in handlers:
502 func = getattr(handler, meth_name)
--> 503 result = func(*args)
504 if result is not None:
505 return result
/opt/conda/lib/python3.7/urllib/request.py in http_error_default(self, req, fp, code, msg, hdrs)
647 class HTTPDefaultErrorHandler(BaseHandler):
648 def http_error_default(self, req, fp, code, msg, hdrs):
--> 649 raise HTTPError(req.full_url, code, msg, hdrs, fp)
650
651 class HTTPRedirectHandler(BaseHandler):
Any ideas whatās going on here? I tried a Chromium browser (Opera) just in case Firefox was breaking something, but no luck. Pretty stumped, as I only started learning python at all very recently.
I guess the URL stored in url[0] is a broken one.
The previous lines might be the following:
urls = search_images('bird photos', max_images=1)
urls[0]
Make the following modifications and check if it works:
urls = search_images('bird photos', max_images=5)
urls[3]
Try with some other numbers as well.
Thanks! That worked perfectly.
Is there a way to source data for segmentation task? Like a way to create segmentation mask from self sourced data?
@Ifeanyi, you could try https://segment-anything.com/, it was released not so long ago (1 week ago?) and seems to be pretty good at segmenting anythingā¦ which I guess you could use to generate training data.
Hello all,
In this first lesson as I understand it, the model is fine-tuned to recognize if something is a bird OR a tree (no other option of not-tree / not-bird).
I trained the model and then downloaded and picture of a cow and received the following results:
Code
label,index,probs = learn.predict(PILImage.create('cow.jpg'))
print(f"This is a: {label}.")
print(f"This is a: {index} label.")
print(f"Probability it's a bird: {probs[0]:.4f}")
Output
This is a: bird.
This is a: 0 label.
Probability it's a bird: 0.9228
Questions:
Hey there! It seems like Iām taking the course a year late, and Iām not even sure if this topic is still active, but Iāll give it a try. I was experimenting with the Kaggle notebook and got a few questions Iād appreciate help with!
Question 1: when we search for the sequence of 400 images of bird and forest, where exactly are they downloaded and stored? As in: in the cloud where Iām running my colab notebook or somewhere in my computer and Iām just not seeing?
Question 2: how does the verify_images() function actually work? What is it verifying, and how does it do that?
Question 3: suppose Iām downloading images of birds and forests, but it turns out several of my bird images are corrupted/cannot be used. How does this imbalance in the dataset affect the predictions my model makes? Will it be biased to predict more often the category with the greatest number of valid images or something like that?
Question 4: how do I know the ideal number of fine-tuning cycles to use? In other words, how do you differentiate making the model better and overfitting?
Thanks a lot for the help!
Hey there! A few answers from someone also trying to figure it all out:
Yes, the model can only predict based on the two categories you trained it to. If you feed anything else to it (such as the photo of a cow), it will see how close the cow is to the two categories you trained it for and will return to you its best guess. To get more labels in the output, you need to train it for that.
I believe itās not much about the bird and the cow being animals, but instead: how similar are the features of the cow photo similar to the features of the bird and forest categories? The shapes, the colors, the combinations of pixelsā¦ I might be wrong here, but I think the 92% means something along the lines of āwe can see a 92% match between the characteristics of the cow photo and the characteristics we usually see in cow photosā. But when we say ācharacteristicsā, again, think of it in abstract terms, such as shapes and colors present in each one
Is it possible to understand what weights might look like for a particular DL task? Understanding how exactly it would impact the model? Or is it hard and we should treat it as a black box- something which is hidden inside a NN?
For an image classifier what would the weights look like? For tabular tasks how would it look like? and so onā¦
An example from chapter 1:
For instance, in Samuelās checkers program, different values of the
weights would result in different checkers-playing strategies
How to know this for other tasks?
We explored 5 different tasks in Lesson 1 (lecture and the chapter):
Out of these 5, we can use only 1 and 3 to test for our own input data right?!
The others are sort of locked in themselves based on the data used in training them. Like I canāt use the segmentation thing to segment my own images as it was done for the particular set of CAMVID dataset. Likewise, I canāt input anything in the tabular analysis thing as it is specific for what we did. Same for Collaborative filtering.
The only reason I am asking is because I wanted to try something with my own data and hence, want to know where it is possible and where it is not, at least as of now in Lesson 1.
Good day everybody,
Just finished Lesson 1 and trying to train my own model. It has two categories, just like the forest and a bird but, in my case, I have uploaded the dataset to kaggle directly and I am not using the functions to download images from the web.
I am getting stuck on how to load the images from the input/dataset directory.
Should I be reading them from input/dataset or should I copy them the output/working/my-dataset ?
Iāve tried both, but getting the same error when I create the DataBlock
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[82], line 9
1 dls = DataBlock(
2 blocks=(ImageBlock, CategoryBlock),
3 get_items=get_image_files,
(...)
6 item_tfms=[Resize(192, method='squish')]
7 ).dataloaders(path, bs=32)
----> 9 dls.show_batch(max_n=6)
File /opt/conda/lib/python3.10/site-packages/fastai/data/core.py:149, in TfmdDL.show_batch(self, b, max_n, ctxs, show, unique, **kwargs)
147 old_get_idxs = self.get_idxs
148 self.get_idxs = lambda: Inf.zeros
--> 149 if b is None: b = self.one_batch()
150 if not show: return self._pre_show_batch(b, max_n=max_n)
151 show_batch(*self._pre_show_batch(b, max_n=max_n), ctxs=ctxs, max_n=max_n, **kwargs)
File /opt/conda/lib/python3.10/site-packages/fastai/data/load.py:171, in DataLoader.one_batch(self)
170 def one_batch(self):
--> 171 if self.n is not None and len(self)==0: raise ValueError(f'This DataLoader does not contain any batches')
172 with self.fake_l.no_multiproc(): res = first(self)
173 if hasattr(self, 'it'): delattr(self, 'it')
ValueError: This DataLoader does not contain any batches
before the DataBlock, I have the code:
path = Path('/kaggle/working/carnival-dataset/carnival')
failed = verify_images(get_image_files(path))
failed.map(Path.unlink)
len(failed)```
which returns zero.
Could anyone point to what I am doing wrong, please? Thank you!I"ve worked it out. /input is readonly, so I copied the directory to /working and can progress further 
If you want to load images from your dataset on Kaggle, you can use the ImageDataLoaders.from_folder method from the fastai library. Just give it the path to your dataset and itāll create a DataLoaders object for you. Hereās some example code:
from fastai.vision.all import *
path = Path(āā¦/input/datasetā)
data = ImageDataLoaders.from_folder(path)
Just make sure your dataset is organized into separate folders for each category and youāre good to go!