Getting data/Kaggle

In order to enter a Kaggle competition, or even just work with the images, we must first get the data.

Getting data

There are a couple of ways to get data.

Option 1: Kaggle-cli

To download the official Kaggle datasets, the easiest way is to use the Kaggle cli.

This is a screenscraper, so often needs to be updated to catch website updates.

To install/upgrade: pip install kaggle-cli --upgrade

To get data: kg download -u [username] -p [password] -c [competition-name]

The competition name is the part after the main URL.

E.g., in https://www.kaggle.com/c/planet-understanding-the-amazon-from-space, the competition name is planet-understanding-the-amazon-from-space.

Note: You must go to the competition in Kaggle and click download first, in order to accept the competition terms and conditions.

Option 2: Chrome wget extension

Another option is to use CurlWget, a Chrome extension.

With this, simply click the download link to get what you want, and it will generate a wget command that you can paste into your Ubuntu console to download the data.

This approach has the advantage that you can download just specific things. For example, instead of downloading all of the contest data, you can just download the folder containing JPG images.

In Ubuntu, you can create symbolic links (like shortcuts). For example, you can make the "data folder" in your Jupyter notebook directory point to a top level directory called data.

To view symbolic links, use the ls -l [directory] command. E.g., ls -l courses/dl1. Symbolic links can be identified by ->.

Dog Breeds

We will enter the dog breed identification contest.

First get the data using the steps above.

In [1]:
# Do our usual import and setup
%reload_ext autoreload
%autoreload 2
%matplotlib inline
In [2]:
# Set up our imports
from fastai.imports import *
from fastai.torch_imports import *
from fastai.transforms import *
from fastai.conv_learner import *
from fastai.model import *
from fastai.dataset import *
from fastai.sgdr import *
from fastai.plots import *
In [3]:
# Set up our paths, size, architecture, batch size, etc.
PATH="data/dogbreed/"
size=224
architecture=resnext101_64
batch_size=58

After doing some initial set up, we do some data exploration to see what the downloaded data looks like.

In [4]:
label_csv = f'{PATH}labels.csv'

# num of rows -1 (to account for the header)
n = len(list(open(label_csv)))-1

# random 20% of rows to use as the validation set
# get cross validation indexes
val_idxs = get_cv_idxs(n)
In [5]:
val_idxs
Out[5]:
array([2882, 4514, 7717, ..., 8922, 6774,   37])
In [6]:
# Take a look and see the files we have
!ls {PATH}
labels.csv  sample_submission.csv  test  tmp  train
In [7]:
# Take a look inside labels using a pandas dataframe
label_df = pd.read_csv(label_csv)
label_df.head()
Out[7]:
id breed
0 000bec180eb18c7604dcecc8fe0dba07 boston_bull
1 001513dfcb2ffafc82cccf4d8bbaba97 dingo
2 001cdf01b096e06d78e9e5112d419397 pekinese
3 00214f311d5d2247d5dfe4fe24b2303d bluetick
4 0021f9ceb3235effd7fcde7f7538ed62 golden_retriever
In [8]:
label_df.count()
Out[8]:
id       10222
breed    10222
dtype: int64
In [9]:
# We then get the number of breeds
label_df.pivot_table(index='breed', aggfunc=len).sort_values('id', ascending=False)
Out[9]:
id
breed
scottish_deerhound 126
maltese_dog 117
afghan_hound 116
entlebucher 115
bernese_mountain_dog 114
shih-tzu 112
great_pyrenees 111
pomeranian 111
basenji 110
samoyed 109
airedale 107
tibetan_terrier 107
leonberg 106
cairn 106
beagle 105
japanese_spaniel 105
australian_terrier 102
blenheim_spaniel 102
miniature_pinscher 102
irish_wolfhound 101
lakeland_terrier 99
saluki 99
papillon 96
whippet 95
siberian_husky 95
norwegian_elkhound 95
pug 94
chow 93
italian_greyhound 92
pembroke 92
... ...
german_short-haired_pointer 75
boxer 75
bull_mastiff 75
borzoi 75
pekinese 75
cocker_spaniel 74
american_staffordshire_terrier 74
doberman 74
brittany_spaniel 73
malinois 73
standard_schnauzer 72
flat-coated_retriever 72
redbone 72
border_collie 72
curly-coated_retriever 72
kuvasz 71
chihuahua 71
soft-coated_wheaten_terrier 71
french_bulldog 70
vizsla 70
tibetan_mastiff 69
german_shepherd 69
giant_schnauzer 69
walker_hound 69
otterhound 69
golden_retriever 67
brabancon_griffon 67
komondor 67
briard 66
eskimo_dog 66

120 rows × 1 columns

In [10]:
# We're going to use data augmentation, so let's set up our transforms
transforms = tfms_from_model(architecture, size, aug_tfms=transforms_side_on, max_zoom=1.1)

# This time we're reading from a CSV instead of from a path
# We also use the validation indexes we generated
# Because the file names in the id column of the csv don't have jpg on the end,
# we pass in .jpeg as a suffix as well.
data = ImageClassifierData.from_csv(PATH, 'train', f'{PATH}labels.csv', test_name='test',
                                   val_idxs=val_idxs, suffix='.jpg', tfms=transforms, bs=batch_size)
In [11]:
# Let's check out a file
file_name = PATH+data.trn_ds.fnames[0]
file_name
Out[11]:
'data/dogbreed/train/001513dfcb2ffafc82cccf4d8bbaba97.jpg'
In [12]:
img = PIL.Image.open(file_name)
img
Out[12]:
In [13]:
img.size
Out[13]:
(500, 375)
In [14]:
size_d = {k: PIL.Image.open(PATH+k).size for k in data.trn_ds.fnames}
In [15]:
# Take a look at all the file sizes
size_d
Out[15]:
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 'train/1f825609b43784e073b718ec559ed397.jpg': (300, 245),
 'train/1f913ada2a4fdb5b237112014087d5dc.jpg': (500, 500),
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 'train/1fbe78c5135f73d021aaa756ca69fe95.jpg': (500, 375),
 ...}
In [16]:
# Get the row size and column size from the list above
row_size, column_size = list(zip(*size_d.values()))
In [17]:
row_size=np.array(row_size)
column_size=np.array(column_size)
In [18]:
row_size[:5]
Out[18]:
array([500, 500, 500, 500, 500])
In [19]:
column_size[:5]
Out[19]:
array([375, 375, 500, 375, 470])
In [20]:
# See how big most of our images are
# Most are 500 wide 
plt.hist(row_size)
Out[20]:
(array([3023., 5024.,   92.,   15.,    5.,    3.,   13.,    2.,    0.,    1.]),
 array([  97. ,  413.7,  730.4, 1047.1, 1363.8, 1680.5, 1997.2, 2313.9, 2630.6, 2947.3, 3264. ]),
 <a list of 10 Patch objects>)
In [21]:
# We can take out some of the outliers to get a more detailed view
plt.hist(row_size[row_size<1000])
Out[21]:
(array([ 135.,  592., 1347., 1164., 4599.,  128.,   76.,   62.,   14.,   11.]),
 array([ 97. , 185.5, 274. , 362.5, 451. , 539.5, 628. , 716.5, 805. , 893.5, 982. ]),
 <a list of 10 Patch objects>)

We could do the same for the columns. This is just to make sure we don't have any super wide/big images, etc.

In [22]:
# Get the size of the training and test data sets
len(data.trn_ds), len(data.test_ds)
Out[22]:
(8178, 10357)
In [23]:
# Get the num of classes
len(data.classes), data.classes[:5]
Out[23]:
(120,
 ['affenpinscher',
  'afghan_hound',
  'african_hunting_dog',
  'airedale',
  'american_staffordshire_terrier'])

Setting up the model

Now that we've done some data exploration, we can set up the model.

In [24]:
# Convenience method
def get_data(size, batch_size):
    transforms = tfms_from_model(architecture, size, aug_tfms=transforms_side_on, max_zoom=1.1)
    data = ImageClassifierData.from_csv(PATH, 'train', f'{PATH}labels.csv', test_name='test', num_workers=4,
                                       val_idxs=val_idxs, suffix='.jpg', tfms=transforms, bs=batch_size)
    return data if size>300 else data.resize(340, 'tmp')

Precompute

Set precompute to do and do our initial training for the final layers.

In [25]:
data = get_data(size, batch_size)

In [26]:
learn = ConvLearner.pretrained(architecture, data, precompute=True)
In [27]:
learn.fit(1e-2, 5)
[0.      0.92608 0.39313 0.90853]                            
[1.      0.43449 0.36583 0.91092]                            
[2.      0.30892 0.30343 0.91331]                            
[3.      0.25296 0.31098 0.91906]                            
[4.      0.19217 0.30561 0.91667]                            

Augment

Next we turn precompute off so we can use data augmentations (transformations).

In [28]:
from sklearn import metrics
In [29]:
data = get_data(size, batch_size)

In [30]:
learn = ConvLearner.pretrained(architecture, data, precompute=True, ps=0.5)
In [31]:
learn.fit(1e-2, 2)
[0.      1.13687 0.4544  0.90079]                           
[1.      0.54595 0.39578 0.91236]                            

In [32]:
learn.precompute=False
In [33]:
# Use the learning rate finder to check the learning rate is optimal
# Tried 5 epochs to check the accuracy was getting better, then stuck with that
learn.fit(1e-2, 5, cycle_len=1)
[0.      0.4454  0.27167 0.92241]                            
[1.      0.40716 0.26172 0.92672]                            
[2.      0.38718 0.25009 0.92522]                            
[3.      0.35889 0.23875 0.93343]                            
[4.      0.33432 0.23403 0.93151]                            

In [34]:
# Save our weights
learn.save('224_pre')
In [35]:
learn.load('224_pre')

Increase size

Then we increase the size of the images.

A tip Jeremy has is to train on smaller images, then train on larger images, so the images are different. It prevents overfitting. Also, training on the small images is faster, so do it first.

In [36]:
learn.set_data(get_data(299, batch_size))
learn.freeze()

In [37]:
# If the training loss is greater than the validation loss, we're underfiting
# We can up the cycle_mult parameter to correct this
# (We'd do 3 epochs normally)
learn.fit(1e-2, 1, cycle_len=1, cycle_mult=2)
[0.      0.34207 0.22987 0.93343]                            

In [39]:
# Then try using test time augmentation to see if that gives better results
log_preds, y = learn.TTA()
                                             
In [40]:
probs = np.mean(np.exp(log_preds), 0)
Out[40]:
0.23592007515597008
In [42]:
accuracy_np(probs, y)
Out[42]:
0.9339530332681018

We didn't unfreeze and try training more. This data is from image-net, that tuning the layers that were already pretrained on image-net anyway didn't make any difference.

Create the submission

Now that we've trained our model, we want to submit it to Kaggle. In the evaluation section of the competition, you can see the requested format:

id,affenpinscher,afghan_hound,..,yorkshire_terrier 000621fb3cbb32d8935728e48679680e,0.0083,0.0,...,0.0083 etc.

In [43]:
submission_csv = f'{PATH}sample_submission.csv'
submission_df = pd.read_csv(submission_csv)
submission_df.head()
Out[43]:
id affenpinscher afghan_hound african_hunting_dog airedale american_staffordshire_terrier appenzeller australian_terrier basenji basset ... toy_poodle toy_terrier vizsla walker_hound weimaraner welsh_springer_spaniel west_highland_white_terrier whippet wire-haired_fox_terrier yorkshire_terrier
0 000621fb3cbb32d8935728e48679680e 0.008333 0.008333 0.008333 0.008333 0.008333 0.008333 0.008333 0.008333 0.008333 ... 0.008333 0.008333 0.008333 0.008333 0.008333 0.008333 0.008333 0.008333 0.008333 0.008333
1 00102ee9d8eb90812350685311fe5890 0.008333 0.008333 0.008333 0.008333 0.008333 0.008333 0.008333 0.008333 0.008333 ... 0.008333 0.008333 0.008333 0.008333 0.008333 0.008333 0.008333 0.008333 0.008333 0.008333
2 0012a730dfa437f5f3613fb75efcd4ce 0.008333 0.008333 0.008333 0.008333 0.008333 0.008333 0.008333 0.008333 0.008333 ... 0.008333 0.008333 0.008333 0.008333 0.008333 0.008333 0.008333 0.008333 0.008333 0.008333
3 001510bc8570bbeee98c8d80c8a95ec1 0.008333 0.008333 0.008333 0.008333 0.008333 0.008333 0.008333 0.008333 0.008333 ... 0.008333 0.008333 0.008333 0.008333 0.008333 0.008333 0.008333 0.008333 0.008333 0.008333
4 001a5f3114548acdefa3d4da05474c2e 0.008333 0.008333 0.008333 0.008333 0.008333 0.008333 0.008333 0.008333 0.008333 ... 0.008333 0.008333 0.008333 0.008333 0.008333 0.008333 0.008333 0.008333 0.008333 0.008333

5 rows × 121 columns

In [44]:
# Take a look at the different classes we have.
# They can be accessed from our data object.
data.classes
Out[44]:
['affenpinscher',
 'afghan_hound',
 'african_hunting_dog',
 'airedale',
 'american_staffordshire_terrier',
 'appenzeller',
 'australian_terrier',
 'basenji',
 'basset',
 'beagle',
 'bedlington_terrier',
 'bernese_mountain_dog',
 'black-and-tan_coonhound',
 'blenheim_spaniel',
 'bloodhound',
 'bluetick',
 'border_collie',
 'border_terrier',
 'borzoi',
 'boston_bull',
 'bouvier_des_flandres',
 'boxer',
 'brabancon_griffon',
 'briard',
 'brittany_spaniel',
 'bull_mastiff',
 'cairn',
 'cardigan',
 'chesapeake_bay_retriever',
 'chihuahua',
 'chow',
 'clumber',
 'cocker_spaniel',
 'collie',
 'curly-coated_retriever',
 'dandie_dinmont',
 'dhole',
 'dingo',
 'doberman',
 'english_foxhound',
 'english_setter',
 'english_springer',
 'entlebucher',
 'eskimo_dog',
 'flat-coated_retriever',
 'french_bulldog',
 'german_shepherd',
 'german_short-haired_pointer',
 'giant_schnauzer',
 'golden_retriever',
 'gordon_setter',
 'great_dane',
 'great_pyrenees',
 'greater_swiss_mountain_dog',
 'groenendael',
 'ibizan_hound',
 'irish_setter',
 'irish_terrier',
 'irish_water_spaniel',
 'irish_wolfhound',
 'italian_greyhound',
 'japanese_spaniel',
 'keeshond',
 'kelpie',
 'kerry_blue_terrier',
 'komondor',
 'kuvasz',
 'labrador_retriever',
 'lakeland_terrier',
 'leonberg',
 'lhasa',
 'malamute',
 'malinois',
 'maltese_dog',
 'mexican_hairless',
 'miniature_pinscher',
 'miniature_poodle',
 'miniature_schnauzer',
 'newfoundland',
 'norfolk_terrier',
 'norwegian_elkhound',
 'norwich_terrier',
 'old_english_sheepdog',
 'otterhound',
 'papillon',
 'pekinese',
 'pembroke',
 'pomeranian',
 'pug',
 'redbone',
 'rhodesian_ridgeback',
 'rottweiler',
 'saint_bernard',
 'saluki',
 'samoyed',
 'schipperke',
 'scotch_terrier',
 'scottish_deerhound',
 'sealyham_terrier',
 'shetland_sheepdog',
 'shih-tzu',
 'siberian_husky',
 'silky_terrier',
 'soft-coated_wheaten_terrier',
 'staffordshire_bullterrier',
 'standard_poodle',
 'standard_schnauzer',
 'sussex_spaniel',
 'tibetan_mastiff',
 'tibetan_terrier',
 'toy_poodle',
 'toy_terrier',
 'vizsla',
 'walker_hound',
 'weimaraner',
 'welsh_springer_spaniel',
 'west_highland_white_terrier',
 'whippet',
 'wire-haired_fox_terrier',
 'yorkshire_terrier']
In [45]:
# We can also see all of the file names
data.test_ds.fnames
Out[45]:
['test/de084b830010b6107215fef5d4a75b94.jpg',
 'test/6b423ca7020e70eb05732843c5d2bad1.jpg',
 'test/74aa7e201e0e93e13e87b986a7d31839.jpg',
 'test/a079f72193264bc5685e5d28d7372680.jpg',
 'test/583f7580fa5fec1266331fcf83b76fd6.jpg',
 'test/72cd7496a319c832f670fbb8d349217e.jpg',
 'test/ed9e75a553dbb4712addc1638693dc03.jpg',
 'test/6322fa7edb40af844609b4843a89b5fd.jpg',
 'test/d18de4f66f7d2ae8dffb08ec13b9bbc5.jpg',
 'test/8c567325012bf8d7daeff453ec959312.jpg',
 'test/bbecb2e61f07719abe32be3f36b3fac0.jpg',
 'test/7d3a1066521da895f915af31e8e739df.jpg',
 'test/a0256a9577f2e7c6631dd78556e4bab8.jpg',
 'test/87dafc44ed8ae792ea5c341d7cbb6df4.jpg',
 'test/1f2e9080823a6b093e7a4baa668393d2.jpg',
 'test/c65172e587bf02794e5b40f830bcb2af.jpg',
 'test/4f0b2dbc23fdef5f939144f456a62140.jpg',
 'test/a5049952a1a2ea0473e6520db5e4a4fb.jpg',
 'test/685ac2356ad42163b2141a41972e5c54.jpg',
 'test/bd405393e16688e5282b57d92bdbb97b.jpg',
 'test/cbb4039e5a16f80c02f3d188f98beaf7.jpg',
 'test/18cf06b0c6a59c6891bf6f5a42bcc73d.jpg',
 'test/9ec769e34bc902bd9de458103e9347c8.jpg',
 'test/14dac63a8e92005b685c7d39ddd8ed40.jpg',
 'test/c5ebdbaf77a7d833edfde7061bbb21d8.jpg',
 'test/161b2a67618d2217940282d01d27c32a.jpg',
 'test/74b133bbc2d4ece626394bdec13d702a.jpg',
 'test/bfbadb3ef3dc0635a5a0fd800fe456b1.jpg',
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 'test/2e57d925d44dc59983f4fe979f78d505.jpg',
 'test/4ded44ea906ddaeaff15eb82965a14d8.jpg',
 'test/fb2cb63322ef0e9a7ade9ecd93921eb2.jpg',
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 'test/e702ddff98bd0b05ab200ffb43fb080a.jpg',
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 'test/ee0bd39d8d6a0f57d626dbe1e4c52ebb.jpg',
 'test/e241e149039740594a6af60dc18b269b.jpg',
 ...]
In [47]:
# Get predictions on the test set
# (We can't get accuracy since by definition we don't know)
log_preds, y = learn.TTA(is_test=True)
probs = np.mean(np.exp(log_preds), 0)
                                              
In [48]:
# 10357 images, 120 possible breeds
# Think of it like a matri -- a list of images, then a probability for the breeds
probs.shape 
Out[48]:
(10357, 120)
In [49]:
df = pd.DataFrame(probs)
df.columns = data.classes
In [50]:
# Insert the IDs, but remove the test/ before the file name ([5:-4])
df.insert(0, 'id', [o[5:-4] for o in data.test_ds.fnames])
In [51]:
# Now we have the data in our submission format
df.head()
Out[51]:
id affenpinscher afghan_hound african_hunting_dog airedale american_staffordshire_terrier appenzeller australian_terrier basenji basset ... toy_poodle toy_terrier vizsla walker_hound weimaraner welsh_springer_spaniel west_highland_white_terrier whippet wire-haired_fox_terrier yorkshire_terrier
0 de084b830010b6107215fef5d4a75b94 0.000018 0.000110 0.000039 0.000007 0.000008 0.000004 0.000001 7.672430e-07 3.367392e-06 ... 1.198677e-06 0.000002 6.501280e-06 1.887059e-06 0.000010 4.076534e-06 0.000017 0.000051 0.000002 1.166302e-06
1 6b423ca7020e70eb05732843c5d2bad1 0.000029 0.000325 0.000021 0.000039 0.000005 0.000002 0.000039 4.944084e-06 1.226995e-05 ... 1.103837e-05 0.000002 1.619151e-06 5.791285e-07 0.000008 8.745586e-06 0.000345 0.000012 0.000052 2.870508e-05
2 74aa7e201e0e93e13e87b986a7d31839 0.000078 0.000045 0.000026 0.003559 0.000409 0.000041 0.000045 1.390945e-04 1.496679e-05 ... 5.939487e-05 0.000068 1.552802e-04 7.329275e-05 0.000020 1.114965e-04 0.000083 0.000251 0.243859 3.139315e-05
3 a079f72193264bc5685e5d28d7372680 0.000064 0.000024 0.000049 0.000095 0.000048 0.000002 0.000177 2.290131e-06 1.020107e-05 ... 1.873471e-05 0.000027 1.161859e-05 3.685182e-06 0.000012 2.911502e-05 0.000159 0.000013 0.000835 2.092581e-04
4 583f7580fa5fec1266331fcf83b76fd6 0.000005 0.000011 0.000016 0.000122 0.000003 0.000008 0.000003 1.663623e-06 5.128858e-07 ... 9.443419e-07 0.000006 2.278522e-07 1.967593e-07 0.000003 8.487252e-07 0.000001 0.000015 0.000034 8.924907e-07

5 rows × 121 columns

In [52]:
# Then we just save it to a file
submission = f'{PATH}subm/'
os.makedirs(submission, exist_ok=True)
df.to_csv(f'{submission}submission.gz', compression='gzip', index=False)
In [53]:
# Get a link to the file to download
FileLink(f'{submission}submission.gz')

Predicting individual images

Sometimes you might want to predict just one image.

In [54]:
file_name = data.val_ds.fnames[0]
In [55]:
file_name
Out[55]:
'train/000bec180eb18c7604dcecc8fe0dba07.jpg'
In [56]:
Image.open(PATH+file_name).resize((150, 150))
Out[56]:
In [57]:
trn_tfms,val_tfms = tfms_from_model(architecture, size)
In [61]:
# Prediction expects a mini-batch -- a collection, we by indexing with
# im[None] we essentially create a mini-batch just containing that one image
im = val_tfms(Image.open(PATH+file_name))
preds = learn.predict_array(im[None])
np.argmax(preds)