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| title: YOLOV3 GradCAM | |
| emoji: 🐢 | |
| colorFrom: pink | |
| colorTo: green | |
| sdk: gradio | |
| sdk_version: 3.40.1 | |
| app_file: app.py | |
| pinned: false | |
| license: mit | |
| # Gradio Object Detection App with GradCAM for YOLOv3 - ERAv1 Session 13 | |
| ## Table of Contents | |
| - [Introduction](#introduction) | |
| - [Features](#features) | |
| - [Model Performance](#model-performance) | |
| - [Inference Samples](#inference-samples) | |
| - [How to Use](#how-to-use) | |
| - [Supported Classes](#supported-classes) | |
| - [Link to the Model](#link-to-the-model) | |
| - [Acknowledgements](#acknowledgements) | |
| ## Introduction | |
| This Gradio app showcases an object detection model using YOLOv3 architecture. The model is trained with enhanced features like multi-resolution training and Mosaic Augmentation. Additionally, the app provides GradCAM outputs for better visualization of the model's predictions. | |
| ## Features | |
| - **PytorchLightning Implementation**: The codebase has been refactored to use PytorchLightning for a more modular and scalable approach. | |
| - **Multi-resolution Training**: Unlike traditional models that train on a fixed resolution, this model has been trained on multiple resolutions (416, 608, 896, 1280) for better generalization. | |
| - **Mosaic Augmentation**: Implemented Mosaic Augmentation to enhance the training dataset, but only applied 75% of the time to maintain variety. | |
| - **Precision Training**: The model is trained using float16 precision for faster convergence and reduced memory usage. | |
| - **GradCAM Visualization**: Integrated GradCAM to provide a heatmap visualization of the regions in the image that the model focuses on during prediction. | |
| ## Model Performance | |
| ``` | |
| ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ | |
| ┃ Validate metric ┃ DataLoader 0 ┃ | |
| ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩ | |
| │ val_class_accuracy_epoch │ 81.89761352539062 │ | |
| │ val_loss │ 6.100630283355713 │ | |
| │ val_no_obj_accuracy_epoch │ 97.92534637451172 │ | |
| │ val_obj_accuracy_epoch │ 71.2684097290039 │ | |
| └───────────────────────────┴───────────────────────────┘ | |
| 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 619/619 [29:42<00:00, 2.88s/it] | |
| MAP: 0.10860311985015869 | |
| ``` | |
| ## Inference Samples | |
|  | |
|  | |
| ## How to Use | |
| 1. Navigate to the Gradio app interface. | |
| 2. Upload a custom image or select from the provided samples. | |
| 3. Click on the "Predict" button. | |
| 4. View the object detection predictions along with the GradCAM heatmap. | |
| ## Supported Classes | |
|  | |
| ## Model Architecture | |
| ``` | |
| ---------------------------------------------------------------- | |
| Layer (type) Output Shape Param # | |
| ================================================================ | |
| Conv2d-1 [-1, 32, 416, 416] 864 | |
| BatchNorm2d-2 [-1, 32, 416, 416] 64 | |
| LeakyReLU-3 [-1, 32, 416, 416] 0 | |
| CNNBlock-4 [-1, 32, 416, 416] 0 | |
| Conv2d-5 [-1, 64, 208, 208] 18,432 | |
| BatchNorm2d-6 [-1, 64, 208, 208] 128 | |
| LeakyReLU-7 [-1, 64, 208, 208] 0 | |
| CNNBlock-8 [-1, 64, 208, 208] 0 | |
| Conv2d-9 [-1, 32, 208, 208] 2,048 | |
| BatchNorm2d-10 [-1, 32, 208, 208] 64 | |
| LeakyReLU-11 [-1, 32, 208, 208] 0 | |
| CNNBlock-12 [-1, 32, 208, 208] 0 | |
| Conv2d-13 [-1, 64, 208, 208] 18,432 | |
| BatchNorm2d-14 [-1, 64, 208, 208] 128 | |
| LeakyReLU-15 [-1, 64, 208, 208] 0 | |
| CNNBlock-16 [-1, 64, 208, 208] 0 | |
| ResidualBlock-17 [-1, 64, 208, 208] 0 | |
| Conv2d-18 [-1, 128, 104, 104] 73,728 | |
| BatchNorm2d-19 [-1, 128, 104, 104] 256 | |
| LeakyReLU-20 [-1, 128, 104, 104] 0 | |
| CNNBlock-21 [-1, 128, 104, 104] 0 | |
| Conv2d-22 [-1, 64, 104, 104] 8,192 | |
| BatchNorm2d-23 [-1, 64, 104, 104] 128 | |
| LeakyReLU-24 [-1, 64, 104, 104] 0 | |
| CNNBlock-25 [-1, 64, 104, 104] 0 | |
| Conv2d-26 [-1, 128, 104, 104] 73,728 | |
| BatchNorm2d-27 [-1, 128, 104, 104] 256 | |
| LeakyReLU-28 [-1, 128, 104, 104] 0 | |
| CNNBlock-29 [-1, 128, 104, 104] 0 | |
| Conv2d-30 [-1, 64, 104, 104] 8,192 | |
| BatchNorm2d-31 [-1, 64, 104, 104] 128 | |
| LeakyReLU-32 [-1, 64, 104, 104] 0 | |
| CNNBlock-33 [-1, 64, 104, 104] 0 | |
| Conv2d-34 [-1, 128, 104, 104] 73,728 | |
| BatchNorm2d-35 [-1, 128, 104, 104] 256 | |
| LeakyReLU-36 [-1, 128, 104, 104] 0 | |
| CNNBlock-37 [-1, 128, 104, 104] 0 | |
| ResidualBlock-38 [-1, 128, 104, 104] 0 | |
| Conv2d-39 [-1, 256, 52, 52] 294,912 | |
| BatchNorm2d-40 [-1, 256, 52, 52] 512 | |
| LeakyReLU-41 [-1, 256, 52, 52] 0 | |
| CNNBlock-42 [-1, 256, 52, 52] 0 | |
| Conv2d-43 [-1, 128, 52, 52] 32,768 | |
| BatchNorm2d-44 [-1, 128, 52, 52] 256 | |
| LeakyReLU-45 [-1, 128, 52, 52] 0 | |
| CNNBlock-46 [-1, 128, 52, 52] 0 | |
| Conv2d-47 [-1, 256, 52, 52] 294,912 | |
| BatchNorm2d-48 [-1, 256, 52, 52] 512 | |
| LeakyReLU-49 [-1, 256, 52, 52] 0 | |
| CNNBlock-50 [-1, 256, 52, 52] 0 | |
| Conv2d-51 [-1, 128, 52, 52] 32,768 | |
| BatchNorm2d-52 [-1, 128, 52, 52] 256 | |
| LeakyReLU-53 [-1, 128, 52, 52] 0 | |
| CNNBlock-54 [-1, 128, 52, 52] 0 | |
| Conv2d-55 [-1, 256, 52, 52] 294,912 | |
| BatchNorm2d-56 [-1, 256, 52, 52] 512 | |
| LeakyReLU-57 [-1, 256, 52, 52] 0 | |
| CNNBlock-58 [-1, 256, 52, 52] 0 | |
| Conv2d-59 [-1, 128, 52, 52] 32,768 | |
| BatchNorm2d-60 [-1, 128, 52, 52] 256 | |
| LeakyReLU-61 [-1, 128, 52, 52] 0 | |
| CNNBlock-62 [-1, 128, 52, 52] 0 | |
| Conv2d-63 [-1, 256, 52, 52] 294,912 | |
| BatchNorm2d-64 [-1, 256, 52, 52] 512 | |
| LeakyReLU-65 [-1, 256, 52, 52] 0 | |
| CNNBlock-66 [-1, 256, 52, 52] 0 | |
| Conv2d-67 [-1, 128, 52, 52] 32,768 | |
| BatchNorm2d-68 [-1, 128, 52, 52] 256 | |
| LeakyReLU-69 [-1, 128, 52, 52] 0 | |
| CNNBlock-70 [-1, 128, 52, 52] 0 | |
| Conv2d-71 [-1, 256, 52, 52] 294,912 | |
| BatchNorm2d-72 [-1, 256, 52, 52] 512 | |
| LeakyReLU-73 [-1, 256, 52, 52] 0 | |
| CNNBlock-74 [-1, 256, 52, 52] 0 | |
| Conv2d-75 [-1, 128, 52, 52] 32,768 | |
| BatchNorm2d-76 [-1, 128, 52, 52] 256 | |
| LeakyReLU-77 [-1, 128, 52, 52] 0 | |
| CNNBlock-78 [-1, 128, 52, 52] 0 | |
| Conv2d-79 [-1, 256, 52, 52] 294,912 | |
| BatchNorm2d-80 [-1, 256, 52, 52] 512 | |
| LeakyReLU-81 [-1, 256, 52, 52] 0 | |
| CNNBlock-82 [-1, 256, 52, 52] 0 | |
| Conv2d-83 [-1, 128, 52, 52] 32,768 | |
| BatchNorm2d-84 [-1, 128, 52, 52] 256 | |
| LeakyReLU-85 [-1, 128, 52, 52] 0 | |
| CNNBlock-86 [-1, 128, 52, 52] 0 | |
| Conv2d-87 [-1, 256, 52, 52] 294,912 | |
| BatchNorm2d-88 [-1, 256, 52, 52] 512 | |
| LeakyReLU-89 [-1, 256, 52, 52] 0 | |
| CNNBlock-90 [-1, 256, 52, 52] 0 | |
| Conv2d-91 [-1, 128, 52, 52] 32,768 | |
| BatchNorm2d-92 [-1, 128, 52, 52] 256 | |
| LeakyReLU-93 [-1, 128, 52, 52] 0 | |
| CNNBlock-94 [-1, 128, 52, 52] 0 | |
| Conv2d-95 [-1, 256, 52, 52] 294,912 | |
| BatchNorm2d-96 [-1, 256, 52, 52] 512 | |
| LeakyReLU-97 [-1, 256, 52, 52] 0 | |
| CNNBlock-98 [-1, 256, 52, 52] 0 | |
| Conv2d-99 [-1, 128, 52, 52] 32,768 | |
| BatchNorm2d-100 [-1, 128, 52, 52] 256 | |
| LeakyReLU-101 [-1, 128, 52, 52] 0 | |
| CNNBlock-102 [-1, 128, 52, 52] 0 | |
| Conv2d-103 [-1, 256, 52, 52] 294,912 | |
| BatchNorm2d-104 [-1, 256, 52, 52] 512 | |
| LeakyReLU-105 [-1, 256, 52, 52] 0 | |
| CNNBlock-106 [-1, 256, 52, 52] 0 | |
| ResidualBlock-107 [-1, 256, 52, 52] 0 | |
| Conv2d-108 [-1, 512, 26, 26] 1,179,648 | |
| BatchNorm2d-109 [-1, 512, 26, 26] 1,024 | |
| LeakyReLU-110 [-1, 512, 26, 26] 0 | |
| CNNBlock-111 [-1, 512, 26, 26] 0 | |
| Conv2d-112 [-1, 256, 26, 26] 131,072 | |
| BatchNorm2d-113 [-1, 256, 26, 26] 512 | |
| LeakyReLU-114 [-1, 256, 26, 26] 0 | |
| CNNBlock-115 [-1, 256, 26, 26] 0 | |
| Conv2d-116 [-1, 512, 26, 26] 1,179,648 | |
| BatchNorm2d-117 [-1, 512, 26, 26] 1,024 | |
| LeakyReLU-118 [-1, 512, 26, 26] 0 | |
| CNNBlock-119 [-1, 512, 26, 26] 0 | |
| Conv2d-120 [-1, 256, 26, 26] 131,072 | |
| BatchNorm2d-121 [-1, 256, 26, 26] 512 | |
| LeakyReLU-122 [-1, 256, 26, 26] 0 | |
| CNNBlock-123 [-1, 256, 26, 26] 0 | |
| Conv2d-124 [-1, 512, 26, 26] 1,179,648 | |
| BatchNorm2d-125 [-1, 512, 26, 26] 1,024 | |
| LeakyReLU-126 [-1, 512, 26, 26] 0 | |
| CNNBlock-127 [-1, 512, 26, 26] 0 | |
| Conv2d-128 [-1, 256, 26, 26] 131,072 | |
| BatchNorm2d-129 [-1, 256, 26, 26] 512 | |
| LeakyReLU-130 [-1, 256, 26, 26] 0 | |
| CNNBlock-131 [-1, 256, 26, 26] 0 | |
| Conv2d-132 [-1, 512, 26, 26] 1,179,648 | |
| BatchNorm2d-133 [-1, 512, 26, 26] 1,024 | |
| LeakyReLU-134 [-1, 512, 26, 26] 0 | |
| CNNBlock-135 [-1, 512, 26, 26] 0 | |
| Conv2d-136 [-1, 256, 26, 26] 131,072 | |
| BatchNorm2d-137 [-1, 256, 26, 26] 512 | |
| LeakyReLU-138 [-1, 256, 26, 26] 0 | |
| CNNBlock-139 [-1, 256, 26, 26] 0 | |
| Conv2d-140 [-1, 512, 26, 26] 1,179,648 | |
| BatchNorm2d-141 [-1, 512, 26, 26] 1,024 | |
| LeakyReLU-142 [-1, 512, 26, 26] 0 | |
| CNNBlock-143 [-1, 512, 26, 26] 0 | |
| Conv2d-144 [-1, 256, 26, 26] 131,072 | |
| BatchNorm2d-145 [-1, 256, 26, 26] 512 | |
| LeakyReLU-146 [-1, 256, 26, 26] 0 | |
| CNNBlock-147 [-1, 256, 26, 26] 0 | |
| Conv2d-148 [-1, 512, 26, 26] 1,179,648 | |
| BatchNorm2d-149 [-1, 512, 26, 26] 1,024 | |
| LeakyReLU-150 [-1, 512, 26, 26] 0 | |
| CNNBlock-151 [-1, 512, 26, 26] 0 | |
| Conv2d-152 [-1, 256, 26, 26] 131,072 | |
| BatchNorm2d-153 [-1, 256, 26, 26] 512 | |
| LeakyReLU-154 [-1, 256, 26, 26] 0 | |
| CNNBlock-155 [-1, 256, 26, 26] 0 | |
| Conv2d-156 [-1, 512, 26, 26] 1,179,648 | |
| BatchNorm2d-157 [-1, 512, 26, 26] 1,024 | |
| LeakyReLU-158 [-1, 512, 26, 26] 0 | |
| CNNBlock-159 [-1, 512, 26, 26] 0 | |
| Conv2d-160 [-1, 256, 26, 26] 131,072 | |
| BatchNorm2d-161 [-1, 256, 26, 26] 512 | |
| LeakyReLU-162 [-1, 256, 26, 26] 0 | |
| CNNBlock-163 [-1, 256, 26, 26] 0 | |
| Conv2d-164 [-1, 512, 26, 26] 1,179,648 | |
| BatchNorm2d-165 [-1, 512, 26, 26] 1,024 | |
| LeakyReLU-166 [-1, 512, 26, 26] 0 | |
| CNNBlock-167 [-1, 512, 26, 26] 0 | |
| Conv2d-168 [-1, 256, 26, 26] 131,072 | |
| BatchNorm2d-169 [-1, 256, 26, 26] 512 | |
| LeakyReLU-170 [-1, 256, 26, 26] 0 | |
| CNNBlock-171 [-1, 256, 26, 26] 0 | |
| Conv2d-172 [-1, 512, 26, 26] 1,179,648 | |
| BatchNorm2d-173 [-1, 512, 26, 26] 1,024 | |
| LeakyReLU-174 [-1, 512, 26, 26] 0 | |
| CNNBlock-175 [-1, 512, 26, 26] 0 | |
| ResidualBlock-176 [-1, 512, 26, 26] 0 | |
| Conv2d-177 [-1, 1024, 13, 13] 4,718,592 | |
| BatchNorm2d-178 [-1, 1024, 13, 13] 2,048 | |
| LeakyReLU-179 [-1, 1024, 13, 13] 0 | |
| CNNBlock-180 [-1, 1024, 13, 13] 0 | |
| Conv2d-181 [-1, 512, 13, 13] 524,288 | |
| BatchNorm2d-182 [-1, 512, 13, 13] 1,024 | |
| LeakyReLU-183 [-1, 512, 13, 13] 0 | |
| CNNBlock-184 [-1, 512, 13, 13] 0 | |
| Conv2d-185 [-1, 1024, 13, 13] 4,718,592 | |
| BatchNorm2d-186 [-1, 1024, 13, 13] 2,048 | |
| LeakyReLU-187 [-1, 1024, 13, 13] 0 | |
| CNNBlock-188 [-1, 1024, 13, 13] 0 | |
| Conv2d-189 [-1, 512, 13, 13] 524,288 | |
| BatchNorm2d-190 [-1, 512, 13, 13] 1,024 | |
| LeakyReLU-191 [-1, 512, 13, 13] 0 | |
| CNNBlock-192 [-1, 512, 13, 13] 0 | |
| Conv2d-193 [-1, 1024, 13, 13] 4,718,592 | |
| BatchNorm2d-194 [-1, 1024, 13, 13] 2,048 | |
| LeakyReLU-195 [-1, 1024, 13, 13] 0 | |
| CNNBlock-196 [-1, 1024, 13, 13] 0 | |
| Conv2d-197 [-1, 512, 13, 13] 524,288 | |
| BatchNorm2d-198 [-1, 512, 13, 13] 1,024 | |
| LeakyReLU-199 [-1, 512, 13, 13] 0 | |
| CNNBlock-200 [-1, 512, 13, 13] 0 | |
| Conv2d-201 [-1, 1024, 13, 13] 4,718,592 | |
| BatchNorm2d-202 [-1, 1024, 13, 13] 2,048 | |
| LeakyReLU-203 [-1, 1024, 13, 13] 0 | |
| CNNBlock-204 [-1, 1024, 13, 13] 0 | |
| Conv2d-205 [-1, 512, 13, 13] 524,288 | |
| BatchNorm2d-206 [-1, 512, 13, 13] 1,024 | |
| LeakyReLU-207 [-1, 512, 13, 13] 0 | |
| CNNBlock-208 [-1, 512, 13, 13] 0 | |
| Conv2d-209 [-1, 1024, 13, 13] 4,718,592 | |
| BatchNorm2d-210 [-1, 1024, 13, 13] 2,048 | |
| LeakyReLU-211 [-1, 1024, 13, 13] 0 | |
| CNNBlock-212 [-1, 1024, 13, 13] 0 | |
| ResidualBlock-213 [-1, 1024, 13, 13] 0 | |
| Conv2d-214 [-1, 1024, 13, 13] 1,048,576 | |
| BatchNorm2d-215 [-1, 1024, 13, 13] 2,048 | |
| LeakyReLU-216 [-1, 1024, 13, 13] 0 | |
| CNNBlock-217 [-1, 1024, 13, 13] 0 | |
| Conv2d-218 [-1, 2048, 13, 13] 18,874,368 | |
| BatchNorm2d-219 [-1, 2048, 13, 13] 4,096 | |
| LeakyReLU-220 [-1, 2048, 13, 13] 0 | |
| CNNBlock-221 [-1, 2048, 13, 13] 0 | |
| Conv2d-222 [-1, 1024, 13, 13] 2,097,152 | |
| BatchNorm2d-223 [-1, 1024, 13, 13] 2,048 | |
| LeakyReLU-224 [-1, 1024, 13, 13] 0 | |
| CNNBlock-225 [-1, 1024, 13, 13] 0 | |
| Conv2d-226 [-1, 2048, 13, 13] 18,874,368 | |
| BatchNorm2d-227 [-1, 2048, 13, 13] 4,096 | |
| LeakyReLU-228 [-1, 2048, 13, 13] 0 | |
| CNNBlock-229 [-1, 2048, 13, 13] 0 | |
| ResidualBlock-230 [-1, 2048, 13, 13] 0 | |
| Conv2d-231 [-1, 1024, 13, 13] 2,097,152 | |
| BatchNorm2d-232 [-1, 1024, 13, 13] 2,048 | |
| LeakyReLU-233 [-1, 1024, 13, 13] 0 | |
| CNNBlock-234 [-1, 1024, 13, 13] 0 | |
| Conv2d-235 [-1, 2048, 13, 13] 18,874,368 | |
| BatchNorm2d-236 [-1, 2048, 13, 13] 4,096 | |
| LeakyReLU-237 [-1, 2048, 13, 13] 0 | |
| CNNBlock-238 [-1, 2048, 13, 13] 0 | |
| Conv2d-239 [-1, 75, 13, 13] 153,675 | |
| CNNBlock-240 [-1, 75, 13, 13] 0 | |
| ScalePrediction-241 [-1, 3, 13, 13, 25] 0 | |
| Conv2d-242 [-1, 256, 13, 13] 262,144 | |
| BatchNorm2d-243 [-1, 256, 13, 13] 512 | |
| LeakyReLU-244 [-1, 256, 13, 13] 0 | |
| CNNBlock-245 [-1, 256, 13, 13] 0 | |
| Upsample-246 [-1, 256, 26, 26] 0 | |
| Conv2d-247 [-1, 256, 26, 26] 196,608 | |
| BatchNorm2d-248 [-1, 256, 26, 26] 512 | |
| LeakyReLU-249 [-1, 256, 26, 26] 0 | |
| CNNBlock-250 [-1, 256, 26, 26] 0 | |
| Conv2d-251 [-1, 512, 26, 26] 1,179,648 | |
| BatchNorm2d-252 [-1, 512, 26, 26] 1,024 | |
| LeakyReLU-253 [-1, 512, 26, 26] 0 | |
| CNNBlock-254 [-1, 512, 26, 26] 0 | |
| Conv2d-255 [-1, 256, 26, 26] 131,072 | |
| BatchNorm2d-256 [-1, 256, 26, 26] 512 | |
| LeakyReLU-257 [-1, 256, 26, 26] 0 | |
| CNNBlock-258 [-1, 256, 26, 26] 0 | |
| Conv2d-259 [-1, 512, 26, 26] 1,179,648 | |
| BatchNorm2d-260 [-1, 512, 26, 26] 1,024 | |
| LeakyReLU-261 [-1, 512, 26, 26] 0 | |
| CNNBlock-262 [-1, 512, 26, 26] 0 | |
| ResidualBlock-263 [-1, 512, 26, 26] 0 | |
| Conv2d-264 [-1, 256, 26, 26] 131,072 | |
| BatchNorm2d-265 [-1, 256, 26, 26] 512 | |
| LeakyReLU-266 [-1, 256, 26, 26] 0 | |
| CNNBlock-267 [-1, 256, 26, 26] 0 | |
| Conv2d-268 [-1, 512, 26, 26] 1,179,648 | |
| BatchNorm2d-269 [-1, 512, 26, 26] 1,024 | |
| LeakyReLU-270 [-1, 512, 26, 26] 0 | |
| CNNBlock-271 [-1, 512, 26, 26] 0 | |
| Conv2d-272 [-1, 75, 26, 26] 38,475 | |
| CNNBlock-273 [-1, 75, 26, 26] 0 | |
| ScalePrediction-274 [-1, 3, 26, 26, 25] 0 | |
| Conv2d-275 [-1, 128, 26, 26] 32,768 | |
| BatchNorm2d-276 [-1, 128, 26, 26] 256 | |
| LeakyReLU-277 [-1, 128, 26, 26] 0 | |
| CNNBlock-278 [-1, 128, 26, 26] 0 | |
| Upsample-279 [-1, 128, 52, 52] 0 | |
| Conv2d-280 [-1, 128, 52, 52] 49,152 | |
| BatchNorm2d-281 [-1, 128, 52, 52] 256 | |
| LeakyReLU-282 [-1, 128, 52, 52] 0 | |
| CNNBlock-283 [-1, 128, 52, 52] 0 | |
| Conv2d-284 [-1, 256, 52, 52] 294,912 | |
| BatchNorm2d-285 [-1, 256, 52, 52] 512 | |
| LeakyReLU-286 [-1, 256, 52, 52] 0 | |
| CNNBlock-287 [-1, 256, 52, 52] 0 | |
| Conv2d-288 [-1, 128, 52, 52] 32,768 | |
| BatchNorm2d-289 [-1, 128, 52, 52] 256 | |
| LeakyReLU-290 [-1, 128, 52, 52] 0 | |
| CNNBlock-291 [-1, 128, 52, 52] 0 | |
| Conv2d-292 [-1, 256, 52, 52] 294,912 | |
| BatchNorm2d-293 [-1, 256, 52, 52] 512 | |
| LeakyReLU-294 [-1, 256, 52, 52] 0 | |
| CNNBlock-295 [-1, 256, 52, 52] 0 | |
| ResidualBlock-296 [-1, 256, 52, 52] 0 | |
| Conv2d-297 [-1, 128, 52, 52] 32,768 | |
| BatchNorm2d-298 [-1, 128, 52, 52] 256 | |
| LeakyReLU-299 [-1, 128, 52, 52] 0 | |
| CNNBlock-300 [-1, 128, 52, 52] 0 | |
| Conv2d-301 [-1, 256, 52, 52] 294,912 | |
| BatchNorm2d-302 [-1, 256, 52, 52] 512 | |
| LeakyReLU-303 [-1, 256, 52, 52] 0 | |
| CNNBlock-304 [-1, 256, 52, 52] 0 | |
| Conv2d-305 [-1, 75, 52, 52] 19,275 | |
| CNNBlock-306 [-1, 75, 52, 52] 0 | |
| ScalePrediction-307 [-1, 3, 52, 52, 25] 0 | |
| ================================================================ | |
| Total params: 107,980,481 | |
| Trainable params: 107,980,481 | |
| Non-trainable params: 0 | |
| ---------------------------------------------------------------- | |
| Input size (MB): 1.98 | |
| Forward/backward pass size (MB): 1253.79 | |
| Params size (MB): 411.91 | |
| Estimated Total Size (MB): 1667.68 | |
| ---------------------------------------------------------------- | |
| ``` | |
| ## Examples | |
| App includes some examples images for testing | |
|  | |
| ## Github | |
| Training code may be found [here](https://github.com/Delve-ERAV1/S13) | |
| ## References | |
| https://arxiv.org/abs/1804.02767 \ | |
| https://www.youtube.com/watch?v=Grir6TZbc1M \ | |
| https://github.com/jacobgil/pytorch-grad-cam | |