--- title: YOLOV3 GradCAM emoji: ๐Ÿข 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 ``` โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“ โ”ƒ Validate metric โ”ƒ DataLoader 0 โ”ƒ โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ โ”‚ val_class_accuracy_epoch โ”‚ 81.89761352539062 โ”‚ โ”‚ val_loss โ”‚ 6.100630283355713 โ”‚ โ”‚ val_no_obj_accuracy_epoch โ”‚ 97.92534637451172 โ”‚ โ”‚ val_obj_accuracy_epoch โ”‚ 71.2684097290039 โ”‚ โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜ 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 619/619 [29:42<00:00, 2.88s/it] MAP: 0.10860311985015869 ``` ## Inference Samples ![pred1](https://github.com/Delve-ERAV1/S13/assets/11761529/df995d26-8d1b-44cd-8979-df4fd514ed44) ![pred2](https://github.com/Delve-ERAV1/S13/assets/11761529/c343787c-1d39-44f6-86f5-c8c228e193e8) ## 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 ![supported_classes](https://github.com/Delve-ERAV1/S13/assets/11761529/49ef1748-9eed-4cca-b8d6-24200400bdf0) ## 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 ![examples_yolo](https://github.com/Delve-ERAV1/S13/assets/11761529/ca81abde-8193-4d3b-b7d3-989b47d2cc5f) ## 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