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https://github.com/amitrajitbose/cat-v-dog-classifier-pytorch
End To End Deep Learning Project For Classifying Cat vs Dog Images, using PyTorch
https://github.com/amitrajitbose/cat-v-dog-classifier-pytorch
api catvsdog-classifier deep-learning densenet-pytorch flask-application pytorch
Last synced: about 8 hours ago
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End To End Deep Learning Project For Classifying Cat vs Dog Images, using PyTorch
- Host: GitHub
- URL: https://github.com/amitrajitbose/cat-v-dog-classifier-pytorch
- Owner: amitrajitbose
- License: mit
- Created: 2019-06-08T16:31:26.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2022-11-22T03:52:07.000Z (almost 2 years ago)
- Last Synced: 2023-03-04T03:34:25.680Z (over 1 year ago)
- Topics: api, catvsdog-classifier, deep-learning, densenet-pytorch, flask-application, pytorch
- Language: Jupyter Notebook
- Homepage:
- Size: 28 MB
- Stars: 36
- Watchers: 2
- Forks: 10
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# 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](https://www.kaggle.com/c/dogs-vs-cats/data).
### Requirements
We recommend to create a virtual environment using [conda](https://anaconda.org/anaconda/conda) or [virtualenv](https://pypi.org/project/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](https://www.kaggle.com/c/dogs-vs-cats/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](https://pypi.org/project/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](http://xenon.stanford.edu/~pgolle/papers/dogcat.pdf)
- [Densely Connected Convolutional Networks](https://arxiv.org/abs/1608.06993)
- [An Optical Frontend for a Convolutional Neural Network](https://arxiv.org/pdf/1901.03661.pdf)
### Team
- [Amitrajit Bose](https://www.linkedin.com/in/amitrajitbose/)
### 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.