第一课 你的宠物
本Nb的目的
the purpose of this Nb
In this lesson we will build our first image classifier from scratch, and see if we can achieve world-class results. Let’s dive in!
三行Jupyter notebook魔法代码
three lines of magics
Every notebook starts with the following three lines; they ensure that any edits to libraries you make are reloaded here automatically, and also that any charts or images displayed are shown in this notebook.
%reload_ext autoreload
%autoreload 2
%matplotlib inline
fastai如何使用import
how fastai designs import
We import all the necessary packages. We are going to work with the fastai V1 library which sits on top of Pytorch 1.0. The fastai library provides many useful functions that enable us to quickly and easily build neural networks and train our models.
如何快速调用我们所需的一切
import everything we need
from fastai.vision import *
from fastai.metrics import error_rate
如何解决内存不够问题?
how to handle out of memory problem?
If you’re using a computer with an unusually small GPU, you may get an out of memory error when running this notebook. If this happens, click Kernel->Restart, uncomment the 2nd line below to use a smaller batch size (you’ll learn all about what this means during the course), and try again.
设置小批量大小
set batch_size
bs = 64
# bs = 16 # uncomment this line if you run out of memory even after clicking Kernel->Restart
Looking at the data
关于Pets数据集
What Pets dataset is about?
We are going to use the Oxford-IIIT Pet Dataset by O. M. Parkhi et al., 2012 which features 12 cat breeds and 25 dogs breeds. Our model will need to learn to differentiate between these 37 distinct categories. According to their paper, the best accuracy they could get in 2012 was 59.21%, using a complex model that was specific to pet detection, with separate “Image”, “Head”, and “Body” models for the pet photos. Let’s see how accurate we can be using deep learning!
We are going to use the untar_data function to which we must pass a URL as an argument and which will download and extract the data.
如何查看函数文档帮助
How to get docs
help(untar_data)
Help on function untar_data in module fastai.datasets:
untar_data(url: str, fname: Union[pathlib.Path, str] = None, dest: Union[pathlib.Path, str] = None, data=True, force_download=False) -> pathlib.Path
Download `url` to `fname` if it doesn't exist, and un-tgz to folder `dest`.
fastai如何下载数据集
how fastai get dataset
path = untar_data(URLs.PETS); path
PosixPath('/home/ubuntu/.fastai/data/oxford-iiit-pet')
如何快捷查看文件夹内容
how to see inside a folder
path.ls()
[PosixPath('/home/ubuntu/.fastai/data/oxford-iiit-pet/images'),
PosixPath('/home/ubuntu/.fastai/data/oxford-iiit-pet/annotations')]
如何快速查看子文件夹
how to build path to sub-folders
path_anno = path/'annotations'
path_img = path/'images'
怎样才算查看数据
what does it mean to look at the data
The first thing we do when we approach a problem is to take a look at the data. We always need to understand very well what the problem is and what the data looks like before we can figure out how to solve it. Taking a look at the data means understanding how the data directories are structured, what the labels are and what some sample images look like.
处理数据集的难点是获取标注
getting labels is the key of handling dataset
The main difference between the handling of image classification datasets is the way labels are stored. In this particular dataset, labels are stored in the filenames themselves. We will need to extract them to be able to classify the images into the correct categories. Fortunately, the fastai library has a handy function made exactly for this, ImageDataBunch.from_name_re gets the labels from the filenames using a regular expression.
如何将文件夹中文件转化为文件地址列表
turn files inside a folder into a list of path objects
fnames = get_image_files(path_img)
fnames[:5]
[PosixPath('/home/ubuntu/.fastai/data/oxford-iiit-pet/images/saint_bernard_188.jpg'),
PosixPath('/home/ubuntu/.fastai/data/oxford-iiit-pet/images/staffordshire_bull_terrier_114.jpg'),
PosixPath('/home/ubuntu/.fastai/data/oxford-iiit-pet/images/Persian_144.jpg'),
PosixPath('/home/ubuntu/.fastai/data/oxford-iiit-pet/images/Maine_Coon_268.jpg'),
PosixPath('/home/ubuntu/.fastai/data/oxford-iiit-pet/images/newfoundland_95.jpg')]
如何确保验证集一致性?
how to make sure the same validation set?
np.random.seed(2)
pat = r'/([^/]+)_\d+.jpg$'
如何从regular expression 创建ImageDataBunch
how to create an ImageDataBunch from re
data = ImageDataBunch.from_name_re(path_img,
fnames,
pat,
ds_tfms=get_transforms(),
size=224,
bs=bs
).normalize(imagenet_stats)
打印图片和标注
print out images with labels
data.show_batch(rows=3, figsize=(7,6))
打印类别和c
print out all classes and c
print(data.classes)
len(data.classes),data.c
['Abyssinian', 'Bengal', 'Birman', 'Bombay', 'British_Shorthair', 'Egyptian_Mau', 'Maine_Coon', 'Persian', 'Ragdoll', 'Russian_Blue', 'Siamese', 'Sphynx', 'american_bulldog', 'american_pit_bull_terrier', 'basset_hound', 'beagle', 'boxer', 'chihuahua', 'english_cocker_spaniel', 'english_setter', 'german_shorthaired', 'great_pyrenees', 'havanese', 'japanese_chin', 'keeshond', 'leonberger', 'miniature_pinscher', 'newfoundland', 'pomeranian', 'pug', 'saint_bernard', 'samoyed', 'scottish_terrier', 'shiba_inu', 'staffordshire_bull_terrier', 'wheaten_terrier', 'yorkshire_terrier']
(37, 37)
Training: resnet34
迁移学习大概模样?
what is transfer learning like?
Now we will start training our model. We will use a convolutional neural network backbone and a fully connected head with a single hidden layer as a classifier. Don’t know what these things mean? Not to worry, we will dive deeper in the coming lessons. For the moment you need to know that we are building a model which will take images as input and will output the predicted probability for each of the categories (in this case, it will have 37 outputs).
We will train for 4 epochs (4 cycles through all our data).
如何做CNN迁移学习
how to create a CNN model as transfer learning
learn = create_cnn(data, models.resnet34, metrics=error_rate)
如何查看模型结构
how to see the structure of model
learn.model
Sequential(
(0): Sequential(
(0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace)
(3): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
(4): Sequential(
(0): BasicBlock(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(1): BasicBlock(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(2): BasicBlock(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(5): Sequential(
(0): BasicBlock(
(conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(downsample): Sequential(
(0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(2): BasicBlock(
(conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(3): BasicBlock(
(conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(6): Sequential(
(0): BasicBlock(
(conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(downsample): Sequential(
(0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(2): BasicBlock(
(conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(3): BasicBlock(
(conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(4): BasicBlock(
(conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(5): BasicBlock(
(conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(7): Sequential(
(0): BasicBlock(
(conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(downsample): Sequential(
(0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(2): BasicBlock(
(conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
)
(1): Sequential(
(0): AdaptiveConcatPool2d(
(ap): AdaptiveAvgPool2d(output_size=1)
(mp): AdaptiveMaxPool2d(output_size=1)
)
(1): Flatten()
(2): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): Dropout(p=0.25)
(4): Linear(in_features=1024, out_features=512, bias=True)
(5): ReLU(inplace)
(6): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(7): Dropout(p=0.5)
(8): Linear(in_features=512, out_features=37, bias=True)
)
)
如何用最优默认值训练模型
how to fit the model with the best default setting
learn.fit_one_cycle(4)
Total time: 01:46
| epoch | train_loss | valid_loss | error_rate |
|---|---|---|---|
| 1 | 1.409939 | 0.357608 | 0.102165 |
| 2 | 0.539408 | 0.242496 | 0.073072 |
| 3 | 0.340212 | 0.221338 | 0.066306 |
| 4 | 0.261859 | 0.216619 | 0.071042 |
如何保存模型
how to save a model
learn.save('stage-1')
Results
如何判断模型是否工作正常?
how do we know our model is working correctly or reasonably or not?
Let’s see what results we have got.
We will first see which were the categories that the model most confused with one another. We will try to see if what the model predicted was reasonable or not. In this case the mistakes look reasonable (none of the mistakes seems obviously naive). This is an indicator that our classifier is working correctly.
confusion matrix能告诉我们什么?
what can confusion matrix tell us?
Furthermore, when we plot the confusion matrix, we can see that the distribution is heavily skewed: the model makes the same mistakes over and over again but it rarely confuses other categories. This suggests that it just finds it difficult to distinguish some specific categories between each other; this is normal behaviour.
如何获取最大损失值对应照片的序号和损失值
how to access the idx and losses of the images with the top losses
interp = ClassificationInterpretation.from_learner(learn)
losses,idxs = interp.top_losses()
len(data.valid_ds)==len(losses)==len(idxs)
True
如何查看漂亮的docs
how to print out docs nicely
doc(interp.plot_top_losses)
如何画confusion matrix
how to plot confusion matrix
interp.plot_confusion_matrix(figsize=(12,12), dpi=60)
如何打印出最易出错的类别和它们的计数
how to print out the most confused categories and count errors
interp.most_confused(min_val=2)
[('British_Shorthair', 'Russian_Blue', 5),
('Ragdoll', 'Birman', 5),
('staffordshire_bull_terrier', 'american_pit_bull_terrier', 5),
('Birman', 'Ragdoll', 3),
('Birman', 'Siamese', 3),
('Persian', 'Maine_Coon', 3),
('Persian', 'Ragdoll', 3),
('Russian_Blue', 'British_Shorthair', 3),
('american_bulldog', 'american_pit_bull_terrier', 3),
('american_pit_bull_terrier', 'staffordshire_bull_terrier', 3),
('chihuahua', 'miniature_pinscher', 3)]
Unfreezing, fine-tuning, and learning rates
什么时候解冻模型?
when to unfreeze the model?
Since our model is working as we expect it to, we will unfreeze our model and train some more.
如何解冻模型?
how to unfreeze the model?
learn.unfreeze()
如何训练一次
how to fit for one epoch
learn.fit_one_cycle(1)
Total time: 00:26
| epoch | train_loss | valid_loss | error_rate |
|---|---|---|---|
| 1 | 0.558166 | 0.314579 | 0.101489 |
如何保存模型
how to save the model
learn.load('stage-1');
如何在一个范围里探索学习率对损失值影响?
how to explore lr within a range for lower loss?
learn.lr_find()
LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.
如何对学习率损失值作图以及解读最佳区间?
how to plot the loss-lr graph and read the best range?
learn.recorder.plot()
如何解冻模型并用学习率区间训练数次
how to unfreeze model and fit with a specific range of lr with epochs
learn.unfreeze()
learn.fit_one_cycle(2, max_lr=slice(1e-6,1e-4))
Total time: 00:53
| epoch | train_loss | valid_loss | error_rate |
|---|---|---|---|
| 1 | 0.242544 | 0.208489 | 0.067659 |
| 2 | 0.206940 | 0.204482 | 0.062246 |
That’s a pretty accurate model!
Training: resnet50
what is the difference between resnet34 and resnet50
what is the difference between resnet34 and resnet50
Now we will train in the same way as before but with one caveat: instead of using resnet34 as our backbone we will use resnet50 (resnet34 is a 34 layer residual network while resnet50 has 50 layers. It will be explained later in the course and you can learn the details in the resnet paper).
why use larger model and image to train with smaller batch size?
why use larger model and image to train with smaller batch size?
Basically, resnet50 usually performs better because it is a deeper network with more parameters. Let’s see if we can achieve a higher performance here. To help it along, let’s us use larger images too, since that way the network can see more detail. We reduce the batch size a bit since otherwise this larger network will require more GPU memory.
how to create an ImageDatabunch with re and setting image size and batch size?
how to create an ImageDatabunch with re and setting image size and batch size?
data = ImageDataBunch.from_name_re(path_img,
fnames,
pat,
ds_tfms=get_transforms(),
size=299,
bs=bs//2).normalize(imagenet_stats)
how to create an CNN model with this data?
how to create an CNN model with this data?
learn = create_cnn(data, models.resnet50, metrics=error_rate)
find and plot the loss-lr relation
find and plot the loss-lr relation
learn.lr_find()
learn.recorder.plot()
LR Finder complete, type {learner_name}.recorder.plot() to see the graph.
how to fit the model 8 epochs
how to fit the model 8 epochs
learn.fit_one_cycle(8)
Total time: 06:59
epoch train_loss valid_loss error_rate
1 0.548006 0.268912 0.076455 (00:57)
2 0.365533 0.193667 0.064953 (00:51)
3 0.336032 0.211020 0.073072 (00:51)
4 0.263173 0.212025 0.060893 (00:51)
5 0.217016 0.183195 0.063599 (00:51)
6 0.161002 0.167274 0.048038 (00:51)
7 0.086668 0.143490 0.044655 (00:51)
8 0.082288 0.154927 0.046008 (00:51)
how to save the model with a different name
how to save the model with a different name
learn.save('stage-1-50')
It’s astonishing that it’s possible to recognize pet breeds so accurately! Let’s see if full fine-tuning helps:
how to unfreeze and fit with a specific range for 3 epochs
how to unfreeze and fit with a specific range for 3 epochs
learn.unfreeze()
learn.fit_one_cycle(3, max_lr=slice(1e-6,1e-4))
Total time: 03:27
epoch train_loss valid_loss error_rate
1 0.097319 0.155017 0.048038 (01:10)
2 0.074885 0.144853 0.044655 (01:08)
3 0.063509 0.144917 0.043978 (01:08)
how to go back to previous model
how to go back to previous model
learn.load('stage-1-50');
how to get classification model interpretor
how to get classification model interpretor
interp = ClassificationInterpretation.from_learner(learn)
how to print out the most confused categories with minimum count
how to print out the most confused categories with minimum count
interp.most_confused(min_val=2)
[('american_pit_bull_terrier', 'staffordshire_bull_terrier', 6),
('Bengal', 'Egyptian_Mau', 5),
('Bengal', 'Abyssinian', 4),
('boxer', 'american_bulldog', 4),
('Ragdoll', 'Birman', 4),
('Egyptian_Mau', 'Bengal', 3)]
Other data formats
how to get MNIST_SAMPLE dataset
how to get MNIST_SAMPLE dataset
path = untar_data(URLs.MNIST_SAMPLE); path
PosixPath('/home/ubuntu/course-v3/nbs/dl1/data/mnist_sample')
how to set flip false for transformation
how to set flip false for transformation
tfms = get_transforms(do_flip=False)
how to create ImageDataBunch from folders
how to create ImageDataBunch from folders
data = ImageDataBunch.from_folder(path, ds_tfms=tfms, size=26)
how to print out image examples from a batch
how to print out image examples from a batch
data.show_batch(rows=3, figsize=(5,5))
how to create a cnn with resnet18 and accuracy as metrics
how to create a cnn with resnet18 and accuracy as metrics
learn = create_cnn(data, models.resnet18, metrics=accuracy)
how to fit 2 epocs
how to fit 2 epocs
learn.fit(2)
Total time: 00:23
epoch train_loss valid_loss accuracy
1 0.116117 0.029745 0.991168 (00:12)
2 0.056860 0.015974 0.994603 (00:10)
how to read a csv with pd
how to read a csv with pd
df = pd.read_csv(path/'labels.csv')
how to read first 5 lines
how to read first 5 lines
df.head()
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
| name | label | |
|---|---|---|
| 0 | train/3/7463.png | 0 |
| 1 | train/3/21102.png | 0 |
| 2 | train/3/31559.png | 0 |
| 3 | train/3/46882.png | 0 |
| 4 | train/3/26209.png | 0 |
how to create ImageDataBunch with csv
how to create ImageDataBunch with csv
data = ImageDataBunch.from_csv(path, ds_tfms=tfms, size=28)
how to print out images from a batch and classes
how to print out images from a batch and classes
data.show_batch(rows=3, figsize=(5,5))
data.classes
[0, 1]
how to create ImageDataBunch from df
how to create ImageDataBunch from df
data = ImageDataBunch.from_df(path, df, ds_tfms=tfms, size=24)
data.classes
[0, 1]
how to create file path object into a list from df
how to create file path object into a list from df
fn_paths = [path/name for name in df['name']]; fn_paths[:2]
[PosixPath('/home/ubuntu/course-v3/nbs/dl1/data/mnist_sample/train/3/7463.png'),
PosixPath('/home/ubuntu/course-v3/nbs/dl1/data/mnist_sample/train/3/21102.png')]
how to create ImageDataBunch from re
how to create ImageDataBunch from re
pat = r"/(\d)/\d+\.png$"
data = ImageDataBunch.from_name_re(path, fn_paths, pat=pat, ds_tfms=tfms, size=24)
data.classes
['3', '7']
how to create ImageDataBunch from name function with file path list
how to create ImageDataBunch from name function with file path list
data = ImageDataBunch.from_name_func(path, fn_paths, ds_tfms=tfms, size=24,
label_func = lambda x: '3' if '/3/' in str(x) else '7')
data.classes
['3', '7']
how to create a list of labels from list of file path
how to create a list of labels from list of file path
labels = [('3' if '/3/' in str(x) else '7') for x in fn_paths]
labels[:5]
['3', '3', '3', '3', '3']
how to create an ImageDataBunch from list
how to create an ImageDataBunch from list
data = ImageDataBunch.from_lists(path, fn_paths, labels=labels, ds_tfms=tfms, size=24)
data.classes
['3', '7']