YOLO（You Only Look Once）物体检测机制在 Tensorflow 中的实现.zip

### You Only Look Once ( YOLO v1 ) Implementation of YOLO object detection pipeline using tensorflow. YOLO is a real time object detection method. It treats both object detection and localisation as regression problems. This is in contrast to previous object detection pipelines such as R-CNN, which had seperate entities for detection and localisation and were far more complicated to fine tune/train. More on YOLO [here](https://arxiv.org/pdf/1506.02640.pdf). As of now, YOLO v2 is out. Check it out [here](https://arxiv.org/pdf/1612.08242.pdf). ### How to use ? Make sure that weight file is present in weights directory. Currently there are three modes, all pertaining to test the pre-trained model. - 'testDB' - Tests the code on a database. ( PASCAL VOC 2007, 2012, MS-COCO ). Keep in mind the model has been trained on PASCAL VOC 2007+2012. So any other dataset would require training. As this has not been implemented yet, [darkflow](https://github.com/thtrieu/darkflow) may help. - 'testLive' - Tests from a live Webcam feed. - 'testFile' - Tests on a single image. By default, it runs on 'testLive' mode. ``` python yolo.py ``` ### Results (PACAL VOC 2007) Class Name | Ground Truth | Predicted | True Positive | False Positive | Avg. Precision ---------- | ------------ | --------- | ------------- | -------------- | -------------- aeroplane| 311| 213| 141| 72| 0.55494075262829001 bicycle| 389| 237| 157| 80| 0.56105639251746608 bird| 576| 359| 184| 175| 0.42433899865958929 boat| 393| 213| 77| 136| 0.24679748475368918 bottle| 657| 128| 33| 95| 0.17272727272727273 bus| 254| 168| 117| 51| 0.54621080695222668 car| 1541| 925| 436| 489| 0.34186953795331443 cat| 370| 322| 250| 72| 0.67658801636799115 chair| 1374| 420| 102| 318| 0.12245608573113981 cow| 329| 204| 66| 138| 0.17318304265255621 diningtable| 299| 160| 114| 46| 0.6494860956834515 dog| 530| 422| 299| 123| 0.63538308205967287 horse| 395| 279| 209| 70| 0.63316214093397671 motorbike| 369| 228| 140| 88| 0.48458388143892261 person| 5227| 3319| 1166| 2153| 0.23070649513423477 pottedplant| 592| 200| 53| 147| 0.17236723672367235 sheep| 311| 172| 44| 128| 0.16292819499341238 sofa| 396| 141| 105| 36| 0.6494177280693908 train| 302| 255| 191| 64| 0.66130285346624584 tvmonitor| 361| 209| 133| 76| 0.54199124564843304 ### Requirements - Tenseflow 1.0 - OpenCV 2 - Python 2 - Pre-trained [weights](https://drive.google.com/file/d/0B2JbaJSrWLpza08yS2FSUnV2dlE/view) #### TODO List - [x] Complete the mean-Average Precision - [ ] Document the code - [x] Add PASCAL VOC 2007 results to the readme - [ ] Complete network training function ### References - Author's [Website](https://pjreddie.com/darknet/yolo/) - [This](https://github.com/hizhangp/yolo_tensorflow) implementation