cat-v-dog-classifier-pytorch
cat-v-dog-classifier-pytorch copied to clipboard
Published
20 hours ago •
amitrajitbose
amitrajitbose
End To End Deep Learning Project For Classifying Cat vs Dog Images, using PyTorch
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.pyto start the server, with default port as8123. - 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
----------------------------------------------------------------
Related Papers
- Machine Learning Attacks Against the Asirra CAPTCHA
- Densely Connected Convolutional Networks
- An Optical Frontend for a Convolutional Neural Network
Team
Issues
Feel free to submit any issues.
Contributions
Currently open to only issues and bug fix related PRs. Feel free to solve an issue and submit a PR.
Metadata
43
Stars
13
Forks
Watchers
Owner
amitrajitbose
Metadata
End To End Deep Learning Project For Classifying Cat vs Dog Images, using PyTorch