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End To End Deep Learning Project For Classifying Cat vs Dog Images, using PyTorch

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Cat Vs Dog Classifier

About

In this project, we build an algorithm, a deep learning model to classify whether images contain either a dog or a cat. This is easy for humans, dogs, and cats. Computers find it a bit more difficult.

Data

The dataset is available at Kaggle and has been provided officially by Microsoft Research.You can find it here.

Requirements

We recommend to create a virtual environment using conda or virtualenv, and then setup environment using pip install -r requirements.txt for setting up the environment. We have used Python 3.6.7 for development. Below is the detailed

torch==1.1.0
torchvision==0.3.0
Flask==1.0.3
Pillow==6.0.0
numpy==1.15.4
pandas==0.23.4
matplotlib==3.0.2
requests==2.22.0

Benchmarks

Our algorithm or model matched an average of 98% accuracy on test set. The best submission on Kaggle for the same is 98.9%. For more details you can check the leaderboard.

Below is the snapshot that was generated when we were training the model and validating its performance.

API (REST) Endpoint

Running The Server

  • Run python app.py to start the server, with default port as 8123.
  • To run on custom port, run python app.py [PORT].

Accessing The API

cURL
curl -X POST \
    http://127.0.0.1:8123/api \
    -H 'content-type: application/json' \
    -d '{"url":"https://images.unsplash.com/photo-1491604612772-6853927639ef?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=334&q=80"}'
Python
>>> import requests, os
>>> url = 'http://127.0.0.1:8123/api'
>>> data = {
    "url":"https://images.unsplash.com/photo-1491604612772-6853927639ef?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=334&q=80"
}
>>> req = requests.post(url, json=data)
>>> req.json()
{'class': 'dog', 'confidence': '0.8944258093833923'}

Architecture

We used a 121-layer DenseNet with a custom classifier for training the above network. It was trained on a GPU and it took approximately 30 minutes for a single epoch. Below is the Keras styled in-detail model summary, generated using torchsummary.

View Complete Architecture
----------------------------------------------------------------
        Layer (type)               Output Shape         Param #
================================================================
            Conv2d-1         [-1, 64, 122, 122]           9,408
       BatchNorm2d-2         [-1, 64, 122, 122]             128
              ReLU-3         [-1, 64, 122, 122]               0
         MaxPool2d-4           [-1, 64, 61, 61]               0
       BatchNorm2d-5           [-1, 64, 61, 61]             128
              ReLU-6           [-1, 64, 61, 61]               0
            Conv2d-7          [-1, 128, 61, 61]           8,192
       BatchNorm2d-8          [-1, 128, 61, 61]             256
              ReLU-9          [-1, 128, 61, 61]               0
           Conv2d-10           [-1, 32, 61, 61]          36,864
      BatchNorm2d-11           [-1, 96, 61, 61]             192
             ReLU-12           [-1, 96, 61, 61]               0
           Conv2d-13          [-1, 128, 61, 61]          12,288
      BatchNorm2d-14          [-1, 128, 61, 61]             256
             ReLU-15          [-1, 128, 61, 61]               0
           Conv2d-16           [-1, 32, 61, 61]          36,864
      BatchNorm2d-17          [-1, 128, 61, 61]             256
             ReLU-18          [-1, 128, 61, 61]               0
           Conv2d-19          [-1, 128, 61, 61]          16,384
      BatchNorm2d-20          [-1, 128, 61, 61]             256
             ReLU-21          [-1, 128, 61, 61]               0
           Conv2d-22           [-1, 32, 61, 61]          36,864
      BatchNorm2d-23          [-1, 160, 61, 61]             320
             ReLU-24          [-1, 160, 61, 61]               0
           Conv2d-25          [-1, 128, 61, 61]          20,480
      BatchNorm2d-26          [-1, 128, 61, 61]             256
             ReLU-27          [-1, 128, 61, 61]               0
           Conv2d-28           [-1, 32, 61, 61]          36,864
      BatchNorm2d-29          [-1, 192, 61, 61]             384
             ReLU-30          [-1, 192, 61, 61]               0
           Conv2d-31          [-1, 128, 61, 61]          24,576
      BatchNorm2d-32          [-1, 128, 61, 61]             256
             ReLU-33          [-1, 128, 61, 61]               0
           Conv2d-34           [-1, 32, 61, 61]          36,864
      BatchNorm2d-35          [-1, 224, 61, 61]             448
             ReLU-36          [-1, 224, 61, 61]               0
           Conv2d-37          [-1, 128, 61, 61]          28,672
      BatchNorm2d-38          [-1, 128, 61, 61]             256
             ReLU-39          [-1, 128, 61, 61]               0
           Conv2d-40           [-1, 32, 61, 61]          36,864
      BatchNorm2d-41          [-1, 256, 61, 61]             512
             ReLU-42          [-1, 256, 61, 61]               0
           Conv2d-43          [-1, 128, 61, 61]          32,768
        AvgPool2d-44          [-1, 128, 30, 30]               0
      BatchNorm2d-45          [-1, 128, 30, 30]             256
             ReLU-46          [-1, 128, 30, 30]               0
           Conv2d-47          [-1, 128, 30, 30]          16,384
      BatchNorm2d-48          [-1, 128, 30, 30]             256
             ReLU-49          [-1, 128, 30, 30]               0
           Conv2d-50           [-1, 32, 30, 30]          36,864
      BatchNorm2d-51          [-1, 160, 30, 30]             320
             ReLU-52          [-1, 160, 30, 30]               0
           Conv2d-53          [-1, 128, 30, 30]          20,480
      BatchNorm2d-54          [-1, 128, 30, 30]             256
             ReLU-55          [-1, 128, 30, 30]               0
           Conv2d-56           [-1, 32, 30, 30]          36,864
      BatchNorm2d-57          [-1, 192, 30, 30]             384
             ReLU-58          [-1, 192, 30, 30]               0
           Conv2d-59          [-1, 128, 30, 30]          24,576
      BatchNorm2d-60          [-1, 128, 30, 30]             256
             ReLU-61          [-1, 128, 30, 30]               0
           Conv2d-62           [-1, 32, 30, 30]          36,864
      BatchNorm2d-63          [-1, 224, 30, 30]             448
             ReLU-64          [-1, 224, 30, 30]               0
           Conv2d-65          [-1, 128, 30, 30]          28,672
      BatchNorm2d-66          [-1, 128, 30, 30]             256
             ReLU-67          [-1, 128, 30, 30]               0
           Conv2d-68           [-1, 32, 30, 30]          36,864
      BatchNorm2d-69          [-1, 256, 30, 30]             512
             ReLU-70          [-1, 256, 30, 30]               0
           Conv2d-71          [-1, 128, 30, 30]          32,768
      BatchNorm2d-72          [-1, 128, 30, 30]             256
             ReLU-73          [-1, 128, 30, 30]               0
           Conv2d-74           [-1, 32, 30, 30]          36,864
      BatchNorm2d-75          [-1, 288, 30, 30]             576
             ReLU-76          [-1, 288, 30, 30]               0
           Conv2d-77          [-1, 128, 30, 30]          36,864
      BatchNorm2d-78          [-1, 128, 30, 30]             256
             ReLU-79          [-1, 128, 30, 30]               0
           Conv2d-80           [-1, 32, 30, 30]          36,864
      BatchNorm2d-81          [-1, 320, 30, 30]             640
             ReLU-82          [-1, 320, 30, 30]               0
           Conv2d-83          [-1, 128, 30, 30]          40,960
      BatchNorm2d-84          [-1, 128, 30, 30]             256
             ReLU-85          [-1, 128, 30, 30]               0
           Conv2d-86           [-1, 32, 30, 30]          36,864
      BatchNorm2d-87          [-1, 352, 30, 30]             704
             ReLU-88          [-1, 352, 30, 30]               0
           Conv2d-89          [-1, 128, 30, 30]          45,056
      BatchNorm2d-90          [-1, 128, 30, 30]             256
             ReLU-91          [-1, 128, 30, 30]               0
           Conv2d-92           [-1, 32, 30, 30]          36,864
      BatchNorm2d-93          [-1, 384, 30, 30]             768
             ReLU-94          [-1, 384, 30, 30]               0
           Conv2d-95          [-1, 128, 30, 30]          49,152
      BatchNorm2d-96          [-1, 128, 30, 30]             256
             ReLU-97          [-1, 128, 30, 30]               0
           Conv2d-98           [-1, 32, 30, 30]          36,864
      BatchNorm2d-99          [-1, 416, 30, 30]             832
            ReLU-100          [-1, 416, 30, 30]               0
          Conv2d-101          [-1, 128, 30, 30]          53,248
     BatchNorm2d-102          [-1, 128, 30, 30]             256
            ReLU-103          [-1, 128, 30, 30]               0
          Conv2d-104           [-1, 32, 30, 30]          36,864
     BatchNorm2d-105          [-1, 448, 30, 30]             896
            ReLU-106          [-1, 448, 30, 30]               0
          Conv2d-107          [-1, 128, 30, 30]          57,344
     BatchNorm2d-108          [-1, 128, 30, 30]             256
            ReLU-109          [-1, 128, 30, 30]               0
          Conv2d-110           [-1, 32, 30, 30]          36,864
     BatchNorm2d-111          [-1, 480, 30, 30]             960
            ReLU-112          [-1, 480, 30, 30]               0
          Conv2d-113          [-1, 128, 30, 30]          61,440
     BatchNorm2d-114          [-1, 128, 30, 30]             256
            ReLU-115          [-1, 128, 30, 30]               0
          Conv2d-116           [-1, 32, 30, 30]          36,864
     BatchNorm2d-117          [-1, 512, 30, 30]           1,024
            ReLU-118          [-1, 512, 30, 30]               0
          Conv2d-119          [-1, 256, 30, 30]         131,072
       AvgPool2d-120          [-1, 256, 15, 15]               0
     BatchNorm2d-121          [-1, 256, 15, 15]             512
            ReLU-122          [-1, 256, 15, 15]               0
          Conv2d-123          [-1, 128, 15, 15]          32,768
     BatchNorm2d-124          [-1, 128, 15, 15]             256
            ReLU-125          [-1, 128, 15, 15]               0
          Conv2d-126           [-1, 32, 15, 15]          36,864
     BatchNorm2d-127          [-1, 288, 15, 15]             576
            ReLU-128          [-1, 288, 15, 15]               0
          Conv2d-129          [-1, 128, 15, 15]          36,864
     BatchNorm2d-130          [-1, 128, 15, 15]             256
            ReLU-131          [-1, 128, 15, 15]               0
          Conv2d-132           [-1, 32, 15, 15]          36,864
     BatchNorm2d-133          [-1, 320, 15, 15]             640
            ReLU-134          [-1, 320, 15, 15]               0
          Conv2d-135          [-1, 128, 15, 15]          40,960
     BatchNorm2d-136          [-1, 128, 15, 15]             256
            ReLU-137          [-1, 128, 15, 15]               0
          Conv2d-138           [-1, 32, 15, 15]          36,864
     BatchNorm2d-139          [-1, 352, 15, 15]             704
            ReLU-140          [-1, 352, 15, 15]               0
          Conv2d-141          [-1, 128, 15, 15]          45,056
     BatchNorm2d-142          [-1, 128, 15, 15]             256
            ReLU-143          [-1, 128, 15, 15]               0
          Conv2d-144           [-1, 32, 15, 15]          36,864
     BatchNorm2d-145          [-1, 384, 15, 15]             768
            ReLU-146          [-1, 384, 15, 15]               0
          Conv2d-147          [-1, 128, 15, 15]          49,152
     BatchNorm2d-148          [-1, 128, 15, 15]             256
            ReLU-149          [-1, 128, 15, 15]               0
          Conv2d-150           [-1, 32, 15, 15]          36,864
     BatchNorm2d-151          [-1, 416, 15, 15]             832
            ReLU-152          [-1, 416, 15, 15]               0
          Conv2d-153          [-1, 128, 15, 15]          53,248
     BatchNorm2d-154          [-1, 128, 15, 15]             256
            ReLU-155          [-1, 128, 15, 15]               0
          Conv2d-156           [-1, 32, 15, 15]          36,864
     BatchNorm2d-157          [-1, 448, 15, 15]             896
            ReLU-158          [-1, 448, 15, 15]               0
          Conv2d-159          [-1, 128, 15, 15]          57,344
     BatchNorm2d-160          [-1, 128, 15, 15]             256
            ReLU-161          [-1, 128, 15, 15]               0
          Conv2d-162           [-1, 32, 15, 15]          36,864
     BatchNorm2d-163          [-1, 480, 15, 15]             960
            ReLU-164          [-1, 480, 15, 15]               0
          Conv2d-165          [-1, 128, 15, 15]          61,440
     BatchNorm2d-166          [-1, 128, 15, 15]             256
            ReLU-167          [-1, 128, 15, 15]               0
          Conv2d-168           [-1, 32, 15, 15]          36,864
     BatchNorm2d-169          [-1, 512, 15, 15]           1,024
            ReLU-170          [-1, 512, 15, 15]               0
          Conv2d-171          [-1, 128, 15, 15]          65,536
     BatchNorm2d-172          [-1, 128, 15, 15]             256
            ReLU-173          [-1, 128, 15, 15]               0
          Conv2d-174           [-1, 32, 15, 15]          36,864
     BatchNorm2d-175          [-1, 544, 15, 15]           1,088
            ReLU-176          [-1, 544, 15, 15]               0
          Conv2d-177          [-1, 128, 15, 15]          69,632
     BatchNorm2d-178          [-1, 128, 15, 15]             256
            ReLU-179          [-1, 128, 15, 15]               0
          Conv2d-180           [-1, 32, 15, 15]          36,864
     BatchNorm2d-181          [-1, 576, 15, 15]           1,152
            ReLU-182          [-1, 576, 15, 15]               0
          Conv2d-183          [-1, 128, 15, 15]          73,728
     BatchNorm2d-184          [-1, 128, 15, 15]             256
            ReLU-185          [-1, 128, 15, 15]               0
          Conv2d-186           [-1, 32, 15, 15]          36,864
     BatchNorm2d-187          [-1, 608, 15, 15]           1,216
            ReLU-188          [-1, 608, 15, 15]               0
          Conv2d-189          [-1, 128, 15, 15]          77,824
     BatchNorm2d-190          [-1, 128, 15, 15]             256
            ReLU-191          [-1, 128, 15, 15]               0
          Conv2d-192           [-1, 32, 15, 15]          36,864
     BatchNorm2d-193          [-1, 640, 15, 15]           1,280
            ReLU-194          [-1, 640, 15, 15]               0
          Conv2d-195          [-1, 128, 15, 15]          81,920
     BatchNorm2d-196          [-1, 128, 15, 15]             256
            ReLU-197          [-1, 128, 15, 15]               0
          Conv2d-198           [-1, 32, 15, 15]          36,864
     BatchNorm2d-199          [-1, 672, 15, 15]           1,344
            ReLU-200          [-1, 672, 15, 15]               0
          Conv2d-201          [-1, 128, 15, 15]          86,016
     BatchNorm2d-202          [-1, 128, 15, 15]             256
            ReLU-203          [-1, 128, 15, 15]               0
          Conv2d-204           [-1, 32, 15, 15]          36,864
     BatchNorm2d-205          [-1, 704, 15, 15]           1,408
            ReLU-206          [-1, 704, 15, 15]               0
          Conv2d-207          [-1, 128, 15, 15]          90,112
     BatchNorm2d-208          [-1, 128, 15, 15]             256
            ReLU-209          [-1, 128, 15, 15]               0
          Conv2d-210           [-1, 32, 15, 15]          36,864
     BatchNorm2d-211          [-1, 736, 15, 15]           1,472
            ReLU-212          [-1, 736, 15, 15]               0
          Conv2d-213          [-1, 128, 15, 15]          94,208
     BatchNorm2d-214          [-1, 128, 15, 15]             256
            ReLU-215          [-1, 128, 15, 15]               0
          Conv2d-216           [-1, 32, 15, 15]          36,864
     BatchNorm2d-217          [-1, 768, 15, 15]           1,536
            ReLU-218          [-1, 768, 15, 15]               0
          Conv2d-219          [-1, 128, 15, 15]          98,304
     BatchNorm2d-220          [-1, 128, 15, 15]             256
            ReLU-221          [-1, 128, 15, 15]               0
          Conv2d-222           [-1, 32, 15, 15]          36,864
     BatchNorm2d-223          [-1, 800, 15, 15]           1,600
            ReLU-224          [-1, 800, 15, 15]               0
          Conv2d-225          [-1, 128, 15, 15]         102,400
     BatchNorm2d-226          [-1, 128, 15, 15]             256
            ReLU-227          [-1, 128, 15, 15]               0
          Conv2d-228           [-1, 32, 15, 15]          36,864
     BatchNorm2d-229          [-1, 832, 15, 15]           1,664
            ReLU-230          [-1, 832, 15, 15]               0
          Conv2d-231          [-1, 128, 15, 15]         106,496
     BatchNorm2d-232          [-1, 128, 15, 15]             256
            ReLU-233          [-1, 128, 15, 15]               0
          Conv2d-234           [-1, 32, 15, 15]          36,864
     BatchNorm2d-235          [-1, 864, 15, 15]           1,728
            ReLU-236          [-1, 864, 15, 15]               0
          Conv2d-237          [-1, 128, 15, 15]         110,592
     BatchNorm2d-238          [-1, 128, 15, 15]             256
            ReLU-239          [-1, 128, 15, 15]               0
          Conv2d-240           [-1, 32, 15, 15]          36,864
     BatchNorm2d-241          [-1, 896, 15, 15]           1,792
            ReLU-242          [-1, 896, 15, 15]               0
          Conv2d-243          [-1, 128, 15, 15]         114,688
     BatchNorm2d-244          [-1, 128, 15, 15]             256
            ReLU-245          [-1, 128, 15, 15]               0
          Conv2d-246           [-1, 32, 15, 15]          36,864
     BatchNorm2d-247          [-1, 928, 15, 15]           1,856
            ReLU-248          [-1, 928, 15, 15]               0
          Conv2d-249          [-1, 128, 15, 15]         118,784
     BatchNorm2d-250          [-1, 128, 15, 15]             256
            ReLU-251          [-1, 128, 15, 15]               0
          Conv2d-252           [-1, 32, 15, 15]          36,864
     BatchNorm2d-253          [-1, 960, 15, 15]           1,920
            ReLU-254          [-1, 960, 15, 15]               0
          Conv2d-255          [-1, 128, 15, 15]         122,880
     BatchNorm2d-256          [-1, 128, 15, 15]             256
            ReLU-257          [-1, 128, 15, 15]               0
          Conv2d-258           [-1, 32, 15, 15]          36,864
     BatchNorm2d-259          [-1, 992, 15, 15]           1,984
            ReLU-260          [-1, 992, 15, 15]               0
          Conv2d-261          [-1, 128, 15, 15]         126,976
     BatchNorm2d-262          [-1, 128, 15, 15]             256
            ReLU-263          [-1, 128, 15, 15]               0
          Conv2d-264           [-1, 32, 15, 15]          36,864
     BatchNorm2d-265         [-1, 1024, 15, 15]           2,048
            ReLU-266         [-1, 1024, 15, 15]               0
          Conv2d-267          [-1, 512, 15, 15]         524,288
       AvgPool2d-268            [-1, 512, 7, 7]               0
     BatchNorm2d-269            [-1, 512, 7, 7]           1,024
            ReLU-270            [-1, 512, 7, 7]               0
          Conv2d-271            [-1, 128, 7, 7]          65,536
     BatchNorm2d-272            [-1, 128, 7, 7]             256
            ReLU-273            [-1, 128, 7, 7]               0
          Conv2d-274             [-1, 32, 7, 7]          36,864
     BatchNorm2d-275            [-1, 544, 7, 7]           1,088
            ReLU-276            [-1, 544, 7, 7]               0
          Conv2d-277            [-1, 128, 7, 7]          69,632
     BatchNorm2d-278            [-1, 128, 7, 7]             256
            ReLU-279            [-1, 128, 7, 7]               0
          Conv2d-280             [-1, 32, 7, 7]          36,864
     BatchNorm2d-281            [-1, 576, 7, 7]           1,152
            ReLU-282            [-1, 576, 7, 7]               0
          Conv2d-283            [-1, 128, 7, 7]          73,728
     BatchNorm2d-284            [-1, 128, 7, 7]             256
            ReLU-285            [-1, 128, 7, 7]               0
          Conv2d-286             [-1, 32, 7, 7]          36,864
     BatchNorm2d-287            [-1, 608, 7, 7]           1,216
            ReLU-288            [-1, 608, 7, 7]               0
          Conv2d-289            [-1, 128, 7, 7]          77,824
     BatchNorm2d-290            [-1, 128, 7, 7]             256
            ReLU-291            [-1, 128, 7, 7]               0
          Conv2d-292             [-1, 32, 7, 7]          36,864
     BatchNorm2d-293            [-1, 640, 7, 7]           1,280
            ReLU-294            [-1, 640, 7, 7]               0
          Conv2d-295            [-1, 128, 7, 7]          81,920
     BatchNorm2d-296            [-1, 128, 7, 7]             256
            ReLU-297            [-1, 128, 7, 7]               0
          Conv2d-298             [-1, 32, 7, 7]          36,864
     BatchNorm2d-299            [-1, 672, 7, 7]           1,344
            ReLU-300            [-1, 672, 7, 7]               0
          Conv2d-301            [-1, 128, 7, 7]          86,016
     BatchNorm2d-302            [-1, 128, 7, 7]             256
            ReLU-303            [-1, 128, 7, 7]               0
          Conv2d-304             [-1, 32, 7, 7]          36,864
     BatchNorm2d-305            [-1, 704, 7, 7]           1,408
            ReLU-306            [-1, 704, 7, 7]               0
          Conv2d-307            [-1, 128, 7, 7]          90,112
     BatchNorm2d-308            [-1, 128, 7, 7]             256
            ReLU-309            [-1, 128, 7, 7]               0
          Conv2d-310             [-1, 32, 7, 7]          36,864
     BatchNorm2d-311            [-1, 736, 7, 7]           1,472
            ReLU-312            [-1, 736, 7, 7]               0
          Conv2d-313            [-1, 128, 7, 7]          94,208
     BatchNorm2d-314            [-1, 128, 7, 7]             256
            ReLU-315            [-1, 128, 7, 7]               0
          Conv2d-316             [-1, 32, 7, 7]          36,864
     BatchNorm2d-317            [-1, 768, 7, 7]           1,536
            ReLU-318            [-1, 768, 7, 7]               0
          Conv2d-319            [-1, 128, 7, 7]          98,304
     BatchNorm2d-320            [-1, 128, 7, 7]             256
            ReLU-321            [-1, 128, 7, 7]               0
          Conv2d-322             [-1, 32, 7, 7]          36,864
     BatchNorm2d-323            [-1, 800, 7, 7]           1,600
            ReLU-324            [-1, 800, 7, 7]               0
          Conv2d-325            [-1, 128, 7, 7]         102,400
     BatchNorm2d-326            [-1, 128, 7, 7]             256
            ReLU-327            [-1, 128, 7, 7]               0
          Conv2d-328             [-1, 32, 7, 7]          36,864
     BatchNorm2d-329            [-1, 832, 7, 7]           1,664
            ReLU-330            [-1, 832, 7, 7]               0
          Conv2d-331            [-1, 128, 7, 7]         106,496
     BatchNorm2d-332            [-1, 128, 7, 7]             256
            ReLU-333            [-1, 128, 7, 7]               0
          Conv2d-334             [-1, 32, 7, 7]          36,864
     BatchNorm2d-335            [-1, 864, 7, 7]           1,728
            ReLU-336            [-1, 864, 7, 7]               0
          Conv2d-337            [-1, 128, 7, 7]         110,592
     BatchNorm2d-338            [-1, 128, 7, 7]             256
            ReLU-339            [-1, 128, 7, 7]               0
          Conv2d-340             [-1, 32, 7, 7]          36,864
     BatchNorm2d-341            [-1, 896, 7, 7]           1,792
            ReLU-342            [-1, 896, 7, 7]               0
          Conv2d-343            [-1, 128, 7, 7]         114,688
     BatchNorm2d-344            [-1, 128, 7, 7]             256
            ReLU-345            [-1, 128, 7, 7]               0
          Conv2d-346             [-1, 32, 7, 7]          36,864
     BatchNorm2d-347            [-1, 928, 7, 7]           1,856
            ReLU-348            [-1, 928, 7, 7]               0
          Conv2d-349            [-1, 128, 7, 7]         118,784
     BatchNorm2d-350            [-1, 128, 7, 7]             256
            ReLU-351            [-1, 128, 7, 7]               0
          Conv2d-352             [-1, 32, 7, 7]          36,864
     BatchNorm2d-353            [-1, 960, 7, 7]           1,920
            ReLU-354            [-1, 960, 7, 7]               0
          Conv2d-355            [-1, 128, 7, 7]         122,880
     BatchNorm2d-356            [-1, 128, 7, 7]             256
            ReLU-357            [-1, 128, 7, 7]               0
          Conv2d-358             [-1, 32, 7, 7]          36,864
     BatchNorm2d-359            [-1, 992, 7, 7]           1,984
            ReLU-360            [-1, 992, 7, 7]               0
          Conv2d-361            [-1, 128, 7, 7]         126,976
     BatchNorm2d-362            [-1, 128, 7, 7]             256
            ReLU-363            [-1, 128, 7, 7]               0
          Conv2d-364             [-1, 32, 7, 7]          36,864
     BatchNorm2d-365           [-1, 1024, 7, 7]           2,048
          Linear-366                  [-1, 512]         524,800
            ReLU-367                  [-1, 512]               0
         Dropout-368                  [-1, 512]               0
          Linear-369                  [-1, 256]         131,328
            ReLU-370                  [-1, 256]               0
         Dropout-371                  [-1, 256]               0
          Linear-372                    [-1, 2]             514
      LogSoftmax-373                    [-1, 2]               0
================================================================
Total params: 7,610,498
Trainable params: 7,610,498
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.68
Forward/backward pass size (MB): 341.21
Params size (MB): 29.03
Estimated Total Size (MB): 370.92
----------------------------------------------------------------

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End To End Deep Learning Project For Classifying Cat vs Dog Images, using PyTorch

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