├── .gitattributes ├── README.md ├── ReadMe.pdf ├── YOLO Tensorflow.ipynb ├── _config.yml ├── files ├── coco.names ├── futur.ttf └── yolo_v3_weights.txt ├── images ├── 000000018150.jpg ├── 000000021903.jpg ├── batch norm.png └── prelu.jpg ├── output_54_0.png ├── output_54_1.png ├── output_54_10.png ├── output_54_11.png ├── output_54_2.png ├── output_54_3.png ├── output_54_4.png ├── output_54_5.png ├── output_54_6.png ├── output_54_7.png ├── output_54_8.png ├── output_54_9.png └── val2017 ├── 000000001000.jpg ├── 000000001268.jpg ├── 000000001296.jpg ├── 000000002006.jpg ├── 000000002153.jpg ├── 000000002261.jpg ├── 000000002473.jpg ├── 000000016451.jpg ├── 000000017207.jpg ├── 000000017905.jpg ├── 000000022371.jpg └── 000000024144.jpg /.gitattributes: -------------------------------------------------------------------------------- 1 | # Auto detect text files and perform LF normalization 2 | * text=auto 3 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # yolo_v3-tensorflow-ipynb 2 | This repository explains you how to convert the yolo v3 darknet model into tensorflow model and run it in jupyter notebook with python3. 3 | 4 | 5 | ## 1 What is YOLO? 6 | 7 | ‘You Only Look Once’ is an Object Detection Algorithm. So what’s great about object detection? In 8 | comparison to recognition algorithms, a detection algorithm does not only predict class labels but 9 | detects locations of objects as well. So, It not only classifies the image into a category, but it can also 10 | detect multiple Objects within an Image. And this Algorithm doesn’t depend on multiple Neural 11 | networks. It applies a single Neural network to the Full Image. This network divides the image 12 | into regions and predicts bounding boxes and probabilities for each region. These bounding boxes 13 | are weighted by the predicted probabilities. 14 | 15 | ![alt text](https://github.com/shahkaran76/yolo_v3-tensorflow-ipynb/blob/master/output_54_10.png) 16 | 17 | ![alt text](https://github.com/shahkaran76/yolo_v3-tensorflow-ipynb/blob/master/output_54_4.png) 18 | 19 | ## 2 Why this Notebook? 20 | 21 | The original YOLO algorithm is deployed in Darknet. Darknet is an open source neural network 22 | framework written in C and CUDA. We will deploy this Algorithm in Tensorflow with Python. 23 | 24 | ## 3 Dependencies 25 | 26 | To build the YOLO we will require : 27 | 1. Tensorflow (GPU version preferred for Deep Learning) 28 | 2. NumPy (for Numeric Computation) 29 | 3. Pillow/PIL (for Image Processing) 30 | 4. IPython (for displaying images in Jupyter Notebook) 31 | 5. Glob (for finding pathname of all the files in a folder) 32 | 33 | ## 4 Acknowledgements 34 | 35 | YOLO - https://pjreddie.com/darknet/yolo/ 36 | -------------------------------------------------------------------------------- /ReadMe.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/shahkaran76/yolo_v3-tensorflow-ipynb/7472f58be09ba36094eb0b94a5406e83bb2e5386/ReadMe.pdf -------------------------------------------------------------------------------- /_config.yml: -------------------------------------------------------------------------------- 1 | theme: jekyll-theme-midnight -------------------------------------------------------------------------------- /files/coco.names: -------------------------------------------------------------------------------- 1 | person 2 | bicycle 3 | car 4 | motorbike 5 | aeroplane 6 | bus 7 | train 8 | truck 9 | boat 10 | traffic light 11 | fire hydrant 12 | stop sign 13 | parking meter 14 | bench 15 | bird 16 | cat 17 | dog 18 | horse 19 | sheep 20 | cow 21 | elephant 22 | bear 23 | zebra 24 | giraffe 25 | backpack 26 | umbrella 27 | handbag 28 | tie 29 | suitcase 30 | frisbee 31 | skis 32 | snowboard 33 | sports ball 34 | kite 35 | baseball bat 36 | baseball glove 37 | skateboard 38 | surfboard 39 | tennis racket 40 | bottle 41 | wine glass 42 | cup 43 | fork 44 | knife 45 | spoon 46 | bowl 47 | banana 48 | apple 49 | sandwich 50 | orange 51 | broccoli 52 | carrot 53 | hot dog 54 | pizza 55 | donut 56 | cake 57 | chair 58 | sofa 59 | pottedplant 60 | bed 61 | diningtable 62 | toilet 63 | tvmonitor 64 | laptop 65 | mouse 66 | remote 67 | keyboard 68 | cell phone 69 | microwave 70 | oven 71 | toaster 72 | sink 73 | refrigerator 74 | book 75 | clock 76 | vase 77 | scissors 78 | teddy bear 79 | hair drier 80 | toothbrush 81 | -------------------------------------------------------------------------------- /files/futur.ttf: 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