├── .gitignore ├── LICENSE ├── README.md └── object_detection.gif /.gitignore: -------------------------------------------------------------------------------- 1 | # Prerequisites 2 | *.d 3 | 4 | # Compiled Object files 5 | *.slo 6 | *.lo 7 | *.o 8 | *.obj 9 | 10 | # Precompiled Headers 11 | *.gch 12 | *.pch 13 | 14 | # Compiled Dynamic libraries 15 | *.so 16 | *.dylib 17 | *.dll 18 | 19 | # Fortran module files 20 | *.mod 21 | *.smod 22 | 23 | # Compiled Static libraries 24 | *.lai 25 | *.la 26 | *.a 27 | *.lib 28 | 29 | # Executables 30 | *.exe 31 | *.out 32 | *.app 33 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2021 Shubham Shah 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Open Source Models 2 | Open Source Models is a archive for all the open source computer vision models. Training Computer Vision models is an arduous task which involves a series of strenuous tasks such as collecting the images, annotating them, uploading them on cloud(In case you don't have a rig with a beffy GPU) and training them for hours and hours (which also requires you to find a workaround so that the colab doesn't timeout). All the steps mentioned above are to be executed without making any error as a small oversight can lead to a model trained with faulty config file, incorrect annotations etc. Thanks to all the generous people in the field of computer vision which are doing all the above tasks and providing thier work to others as an open source project, so that not everyone has to reinvent neural networks and can focus on the actual task that has to be carried out with the model. 3 | 4 | This archive consists of models with different architecture, accuracy, and framework in the same category as different use cases demand different types of model to achieve similar goals. 5 | 6 | ![](https://github.com/shubham-shahh/Open-Source-Models/blob/master/object_detection.gif) 7 | 8 | ## Contribution 9 | This project cannot work without YOUR help. Everyone is encouraged to contribute to this project by listing the models they ha 10 | ve trained after spending endless time and efforts to train them, so that everyone in the community is aware about its existance and can use them for their purpose. 11 | 12 | ### Format for contribution 13 | 14 | * Add the source links(blogs, github repo) to the model and all the supporting files in the respective category along with information such as, number of classe(s), name of classe(s), number of images used for training, type of network(detection, segmentation, classification) and if possible also include a performance metric. 15 | 16 | * You can also contribute to this project even if you haven't trained a model yet by testing the models already listed here and test it for performance, accuracy and report if the link is broken or the the file does not exist on the mentioned link. 17 | 18 | * If you are adding links from someone else's page you came across, please do not add direct links to the files(eg. google drive or dropbox) as they could be changed by the author. 19 | 20 | 21 | ## Can't find the model here? 22 | Incase, you aren't able to find a model in here that fits your requirement and planning to train your own model, You can checkout the Google [Open Images Dataset](https://storage.googleapis.com/openimages/web/index.html). Here you can find annotated images that can be downloaded as per your convinience with [OIDv4_ToolKit](https://github.com/EscVM/OIDv4_ToolKit) and use this [fork](https://github.com/theAIGuysCode/OIDv4_ToolKit) in case you want the annotations that can be used to train a [YOLO](https://github.com/AlexeyAB/darknet) model. 23 | 24 | If you cannot train a model for some reason, you can put up a request in the [issues](https://github.com/shubham-shahh/Open-Source-Models/issues) and see if someone can help you with that. 25 | 26 | 27 | ## Models Archive 28 | 29 | ### Table of contents 30 | * [License Plate Detector](#License-plate-detector) 31 | * [Fire Detector](#Fire-Detector) 32 | * [Face Detector](#Face-Detector) 33 | * [Face Mask Detector](#Face-mask-detector) 34 | * [Drone Detection](#Drone-Detection) 35 | 36 | ### License plate detector 37 | This section consists of link to models that has **License plate** or **number plate** as one of their classes. 38 | 39 | 1. License plate detector 40 | * **Model Architecture -** YOLOv4 41 | * **Dataset-** [Open Images Dataset](https://storage.googleapis.com/openimages/web/visualizer/index.html?set=train&type=segmentation&r=false&c=%2Fm%2F01jfm_) 42 | * **Number of training examples -** 1500 43 | * **Accuracy Metric -** (mAP@0.50) = 88.57% 44 | * **Number of classe(s) -** 1 45 | * **Link to the model and supporting files -** [Model](https://github.com/theAIGuysCode/yolov4-custom-functions) 46 | * **Author Remarks -** NA 47 | 48 | 2. License plate detector + Character detection 49 | * **Model Architecture -** YOLO 50 | * **Dataset-** [Dataset](https://github.com/TheophileBuy/LicensePlateRecognition) 51 | * **Number of training examples -** 1900 52 | * **Accuracy Metric -** NA 53 | * **Number of classe(s) -** 1 54 | * **Link to the model and supporting files -** [Model](https://github.com/TheophileBuy/LicensePlateRecognition) 55 | * **Author Remarks -** NA 56 | 57 | 3. License plate detector along with type of the vehicle 58 | * **Model Architecture -** YOLOv3-tiny 59 | * **Dataset-** NA 60 | * **Number of training examples -** 1700+ 61 | * **Accuracy Metric -** NA 62 | * **Number of classe(s) -** 10 63 | * **Link to the model and supporting files -** [Model](https://github.com/SumanSudhir/Vehicle-and-Its-License-Plate-detection) 64 | * **Author Remarks -** NA 65 | 66 | 4. License plate detector 67 | * **Model Architecture -** YOLOv3 68 | * **Dataset-** NA 69 | * **Number of training examples -** NA 70 | * **Accuracy Metric -** NA 71 | * **Number of classe(s) -** 1 72 | * **Link to the model and supporting files -** [Model](https://www.kaggle.com/achrafkhazri/yolo-weights-for-licence-plate-detector) 73 | * **Author Remarks -** NA 74 | 75 | 5. License plate detector 76 | * **Model Architecture -** YOLOv3 77 | * **Dataset-** [Dataset avaliable for academic use only](https://github.com/alitourani/yolo-license-plate-detection) 78 | * **Number of training examples -** 3000+ 79 | * **Accuracy Metric -** NA 80 | * **Number of classe(s) -** 1 81 | * **Link to the model and supporting files -** [Model](https://github.com/alitourani/yolo-license-plate-detection) 82 | * **Author Remarks -** NA 83 | 84 | 85 | ### Fire detector 86 | This section consists of link to models that has **fire** as one of their classes. 87 | 88 | 1. Fire detector 89 | * **Model Architecture -** YOLOv3 90 | * **Dataset-** Open Images Dataset 91 | * **Number of training examples -** NA 92 | * **Accuracy Metric -** NA 93 | * **Number of classe(s) -** 1 94 | * **Link to the model and supporting files -** [Model](https://github.com/snehitvaddi/YOLOv3-Cloud-Based-Fire-Detection) 95 | * **Author Remarks -** NA 96 | 97 | 2. Fire and fire arms detector 98 | * **Model Architecture -** YOLOv3 99 | * **Dataset-** NA 100 | * **Number of training examples -** NA 101 | * **Accuracy Metric -** [details in paper](https://github.com/atulyakumar97/fire-and-gun-detection/blob/master/ICESC2020_Published%20Paper.pdf) 102 | * **Number of classe(s) -** 2 103 | * **Link to the model and supporting files -** [Model](https://github.com/atulyakumar97/fire-and-gun-detection) 104 | * **Author Remarks -** NA 105 | 106 | 3. Fire detector 107 | * **Model Architecture -** YOLOv3 108 | * **Dataset-** [FireNet](https://github.com/OlafenwaMoses/FireNET) 109 | * **Number of training examples -** 502 spilt into 2 parts, 412 for training 90 for validation 110 | * **Accuracy Metric -** NA 111 | * **Number of classe(s) -** NA 112 | * **Link to the model and supporting files -** [Model](https://github.com/OlafenwaMoses/FireNET) 113 | * **Author Remarks -** NA 114 | 115 | 4. Fire and smoke detector 116 | * **Model Architecture -** YOLOv4 117 | * **Dataset-** NA 118 | * **Number of training examples -** NA 119 | * **Accuracy Metric -** NA 120 | * **Number of classe(s) -** NA 121 | * **Link to the model and supporting files -** [Model](https://github.com/gengyanlei/fire-detect-yolov4/blob/master/README_EN.md) 122 | * **Author Remarks -** NA 123 | 124 | 5. Fire detector 125 | * **Model Architecture -** InceptionV4-OnFire 126 | * **Dataset-** [Durham Collections - Dunnings/Breckon, 2018](https://github.com/tobybreckon/fire-detection-cnn) 127 | * **Number of training examples -** NA 128 | * **Accuracy Metric -** NA 129 | * **Number of classe(s) -** NA 130 | * **Link to the model and supporting files -** [Model](https://github.com/tobybreckon/fire-detection-cnn) 131 | * **Author Remarks -** NA 132 | 133 | 134 | ### Face detector 135 | This section consists of link to models that has **face** as one of their classes. 136 | 137 | 1. YOLO Face 138 | * **Model Architecture -** YOLOv3 139 | * **Dataset-** [WIDER FACE: A Face Detection Benchmark](https://github.com/sthanhng/yoloface) 140 | * **Number of training examples -** NA 141 | * **Accuracy Metric -** NA 142 | * **Number of classe(s) -** NA 143 | * **Link to the model and supporting files -** [Model](https://github.com/sthanhng/yoloface) 144 | * **Author Remarks -** NA 145 | 146 | 2. YOLO Face Keras 147 | * **Model Architecture -** YOLOv3 148 | * **Dataset-** [WIDER FACE: A Face Detection Benchmark](https://github.com/swdev1202/keras-yolo3-facedetection) 149 | * **Number of training examples -** NA 150 | * **Accuracy Metric -** NA 151 | * **Number of classe(s) -** NA 152 | * **Link to the model and supporting files -** [Model](https://github.com/swdev1202/keras-yolo3-facedetection) 153 | * **Author Remarks -** NA 154 | 155 | 3. Face detector 156 | * **Model Architecture -** YOLOv2-tiny 157 | * **Dataset-** [WIDER FACE: A Face Detection Benchmark](https://github.com/zlmo/Face-Detection) 158 | * **Number of training examples -** NA 159 | * **Accuracy Metric -** NA 160 | * **Number of classe(s) -** NA 161 | * **Link to the model and supporting files -** [Model](https://github.com/zlmo/Face-Detection) 162 | * **Author Remarks -** NA 163 | 164 | 4. Face detector 165 | * **Model Architecture -** YOLOv2 166 | * **Dataset-** FDDB+Dlib 167 | * **Number of training examples -** NA 168 | * **Accuracy Metric -** NA 169 | * **Number of classe(s) -** NA 170 | * **Link to the model and supporting files -** [Model](https://github.com/azmathmoosa/azFace) 171 | * **Author Remarks -** NA 172 | 173 | 5. Ultra light face detector 174 | * **Model Architecture -** Multiple models 175 | * **Dataset-** WIDER FACE: A Face Detection Benchmark 176 | * **Number of training examples -** NA 177 | * **Accuracy Metric -** NA 178 | * **Number of classe(s) -** NA 179 | * **Link to the model and supporting files -** [Model](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB) 180 | * **Author Remarks -** NA 181 | 182 | 183 | ### Face Mask detector 184 | This section consists of link to models that has **face mask** as one of their classes. 185 | 186 | 1. Mask detector 187 | * **Model Architecture -** YOLOv3-tiny-prn 188 | * **Dataset-** [Dataset](https://github.com/cansik/yolo-mask-detection) 189 | * **Number of training examples -** 678 190 | * **Accuracy Metric -** NA 191 | * **Number of classe(s) -** NA 192 | * **Link to the model and supporting files -** [Model](https://github.com/cansik/yolo-mask-detection) 193 | * **Author Remarks -** NA 194 | 195 | 2. Mask detector 196 | * **Model Architecture -** YOLOv2, YOLOv3, YOLOv4 197 | * **Dataset-** [Dataset](https://github.com/adityap27/face-mask-detector) 198 | * **Number of training examples -** 920 199 | * **Accuracy Metric -** [Performance and accuracy](https://github.com/adityap27/face-mask-detector) 200 | * **Number of classe(s) -** NA 201 | * **Link to the model and supporting files -** [Model](https://github.com/adityap27/face-mask-detector) 202 | * **Author Remarks -** NA 203 | 204 | 3. Mask detector 205 | * **Model Architecture -** YOLOv3 206 | * **Dataset-** [Dataset](https://github.com/VictorLin000/YOLOv3_mask_detect) 207 | * **Number of training examples -** 678 208 | * **Accuracy Metric -** NA 209 | * **Number of classe(s) -** NA 210 | * **Link to the model and supporting files -** [Model](https://github.com/VictorLin000/YOLOv3_mask_detect) 211 | * **Author Remarks -** NA 212 | 213 | 3. Mask detector 214 | * **Model Architecture -** YOLOv3 215 | * **Dataset-** NA 216 | * **Number of training examples -** 678 217 | * **Accuracy Metric -** NA 218 | * **Number of classe(s) -** NA 219 | * **Link to the model and supporting files -** [Model](https://github.com/rushad7/mask-detection) 220 | * **Author Remarks -** NA 221 | 222 | 223 | ### Drone Detection 224 | This section consists of link to models that has **Drone** as one of their classes. 225 | 1. D-Drone v2 226 | * **Model Architectures -** YOLOv4, YOLOv5 and DETR 227 | * **Dataset-** [Custom Dataset](https://github.com/5a7man/D-Drone_v2) 228 | * **Number of training examples -** 2000 229 | * **Accuracy Metric -** mAP, avg IOU 230 | * **Number of classe(s) -** 1 231 | * **Link to the model and supporting files -** [Model](https://github.com/5a7man/D-Drone_v2) 232 | * **Author Remarks -** Results can be used for benchmarking purposes. 233 | 234 | 235 | 236 | 237 | 238 | 239 | 240 | 241 | 242 | 243 | -------------------------------------------------------------------------------- /object_detection.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/shubham-shahh/Open-Source-Models/f916fb36788d35aad0d195f781b45e482c8a48ee/object_detection.gif --------------------------------------------------------------------------------