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.
The dataset is available at Kaggle and has been provided officially by Microsoft Research.You can find it here.
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 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.
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'} 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 ---------------------------------------------------------------- - Machine Learning Attacks Against the Asirra CAPTCHA
- Densely Connected Convolutional Networks
- An Optical Frontend for a Convolutional Neural Network
Feel free to submit any issues.
Currently open to only issues and bug fix related PRs. Feel free to solve an issue and submit a PR.