Lesson 1. How to use saved model?

alabamalama1 · May 23, 2020, 4:06pm

Hello everyone.
I trained model and saved it.
Now i want my model to classify my new dowloaded cat image.
for example img path is = path/‘newcat’

code
%reload_ext autoreload
%autoreload 2
%matplotlib inline
from fastai.vision import *
from fastai.metrics import error_rate
path = untar_data(URLs.PETS)
path_img = path/‘images’
fnames = get_image_files(path_img)
np.random.seed(2)
pat = r’/([^/]+)_\d+.jpg$’
data = ImageDataBunch.from_name_re(path_img, fnames, pat, ds_tfms=get_transforms(),
size=299, bs=32).normalize(imagenet_stats)
learn = cnn_learner(data, models.resnet50, metrics=error_rate)
learn.load(‘model-1’)

what should i change to see the breed of new cat?

solved

we need to add

img = open_image(path/‘randomcat’/‘cat_3.jpg’)
pred_class,pred_idx,outputs = learn.predict(img)
num = int(str(pred_idx)[7:-1])
proc = str(outputs[pred_idx])[9:11]+’%’
print(data.classes[num], proc)

#output: Birman 93%

Alek · May 23, 2020, 8:06pm

I am not sure but maybe You should add this?:

img = open_image(path/'cat.png')
breed   = learn.predict(img)
breed #to show result

Or maybe You want to see some results?
learn.show_results

alabamalama1 · May 24, 2020, 6:31am

Thanks for reply.
My full code:

%reload_ext autoreload
%autoreload 2
%matplotlib inline
from fastai.vision import *
from fastai.metrics import error_rate
path = untar_data(URLs.PETS)
path_img = path/‘images’
fnames = get_image_files(path_img)
np.random.seed(2)
pat = r’/([^/]+)_\d+.jpg$’
data = ImageDataBunch.from_name_re(path_img, fnames, pat, ds_tfms=get_transforms(),
size=299, bs=32).normalize(imagenet_stats)
learn = cnn_learner(data, models.resnet50, metrics=error_rate)

learn.load(‘model-1-50’)

img = open_image(path/‘randomcat’/‘cat_1.jpg’)
breed = learn.predict(img)
breed #to show result

output

(Category tensor(8),
tensor(8),
tensor([1.7711e-04, 1.2962e-05, 2.7366e-01, 6.4201e-05, 2.6118e-03, 1.8057e-04,
3.9145e-04, 1.7402e-01, 5.4468e-01, 3.4600e-05, 9.6248e-04, 5.0224e-05,
1.3048e-05, 1.5770e-04, 8.9922e-05, 5.5734e-05, 1.3812e-05, 1.2908e-04,
1.5094e-04, 2.7817e-05, 1.0924e-04, 4.1123e-05, 7.0042e-05, 9.5551e-04,
1.8155e-04, 6.3321e-05, 1.6144e-05, 1.5543e-05, 7.6111e-04, 4.3801e-05,
1.8073e-05, 5.2987e-05, 7.0687e-05, 7.6506e-05, 7.9788e-06, 1.3422e-05,
3.1444e-05]))

learn.show_results

output

Output

<bound method Learner.show_results of Learner(data=ImageDataBunch;

Train: LabelList (5913 items)
x: ImageList
Image (3, 299, 299),Image (3, 299, 299),Image (3, 299, 299),Image (3, 299, 299),Image (3, 299, 299)
y: CategoryList
Abyssinian,Abyssinian,Abyssinian,Abyssinian,Abyssinian
Path: C:\Users\User.fastai\data\oxford-iiit-pet\images;

Valid: LabelList (1478 items)
x: ImageList
Image (3, 299, 299),Image (3, 299, 299),Image (3, 299, 299),Image (3, 299, 299),Image (3, 299, 299)
y: CategoryList
american_pit_bull_terrier,staffordshire_bull_terrier,Abyssinian,Egyptian_Mau,Maine_Coon
Path: C:\Users\User.fastai\data\oxford-iiit-pet\images;

Test: None, 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=True)
(3): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
(4): Sequential(
(0): Bottleneck(
(conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(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)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): Bottleneck(
(conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(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)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(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)
(conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
)
(5): Sequential(
(0): Bottleneck(
(conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=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): Bottleneck(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(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)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(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)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
(3): Bottleneck(
(conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(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)
(conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
)
(6): Sequential(
(0): Bottleneck(
(conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(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)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(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)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
(3): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(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)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
(4): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(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)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
(5): Bottleneck(
(conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(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)
(conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
)
(7): Sequential(
(0): Bottleneck(
(conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(downsample): Sequential(
(0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): Bottleneck(
(conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(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)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
(2): Bottleneck(
(conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(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)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
)
)
)
(1): Sequential(
(0): AdaptiveConcatPool2d(
(ap): AdaptiveAvgPool2d(output_size=1)
(mp): AdaptiveMaxPool2d(output_size=1)
)
(1): Flatten()
(2): BatchNorm1d(4096, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): Dropout(p=0.25, inplace=False)
(4): Linear(in_features=4096, out_features=512, bias=True)
(5): ReLU(inplace=True)
(6): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(7): Dropout(p=0.5, inplace=False)
(8): Linear(in_features=512, out_features=37, bias=True)
)
), opt_func=functools.partial(<class ‘torch.optim.adam.Adam’>, betas=(0.9, 0.99)), loss_func=FlattenedLoss of CrossEntropyLoss(), metrics=[<function error_rate at 0x00000244350F95E8>], true_wd=True, bn_wd=True, wd=0.01, train_bn=True, path=WindowsPath(‘C:/Users/User/.fastai/data/oxford-iiit-pet/images’), model_dir=‘models’, callback_fns=[functools.partial(<class ‘fastai.basic_train.Recorder’>, add_time=True, silent=False)], callbacks=[], layer_groups=[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=True)
(3): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
(4): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(5): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(6): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(7): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(8): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(9): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(10): ReLU(inplace=True)
(11): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(12): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(13): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(14): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(15): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(16): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(17): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(18): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(19): ReLU(inplace=True)
(20): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(21): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(22): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(23): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(24): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(25): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(26): ReLU(inplace=True)
(27): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(28): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(29): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(30): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(31): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(32): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(33): ReLU(inplace=True)
(34): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
(35): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(36): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(37): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(38): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(39): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(40): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(41): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(42): ReLU(inplace=True)
(43): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(44): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(45): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(46): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(47): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(48): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(49): ReLU(inplace=True)
(50): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(51): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(52): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(53): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(54): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(55): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(56): ReLU(inplace=True)
), Sequential(
(0): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
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(9): Linear(in_features=512, out_features=37, bias=True)
)], add_time=True, silent=False)>
[/details]

I still haven’t received an answer what breed my cat.
What should i change to see a prediction like “maine-coon” or “pitbull” ?

JonathanSum · May 24, 2020, 7:15am

Step 1, open the image.
img = open_image(path/‘shouko/vlcsnap-2020-02-05-17h08m13s825.png’)
Step 2, load it into the model(if you have dataloader issue, it means you have not load the dataloader before).
pred_class,pred_idx,outputs = learn.predict(img)
Step 3
receives an answer on what the object is.

Step 0:
it is for exporting the model, including the classes of the model. The classes in your case are the breed of the cat.

github.com

JonathanSum/Pytorch_Projects/blob/master/character identifier_old.ipynb

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      "name": "Copy of character identifier.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
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    "accelerator": "GPU",
    "widgets": {
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          "model_name": "HBoxModel",

This file has been truncated. show original

My Code(Github)

Recolored powered by powerful Fastai
02f0bbfd00b0bca4153f5d24308e3b24

I hope I can help you.

alabamalama1 · May 24, 2020, 8:45am

Thanks guys for answers.
I need to add
img = open_image(path/‘randomcat’/‘cat_3.jpg’)
pred_class,pred_idx,outputs = learn.predict(img)
num = int(str(pred_idx)[7:-1])
proc = str(outputs[pred_idx])[9:11]+’%’
print(data.classes[num], proc)

to my code to get what i needed. Problem solved.

And although I still do not understand why I need to enter all the initial ‘untar’ and other things, if I already have a saved model.
But I have to deal with this later.