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Yolo v4
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README.md
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README.md
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@ -49,25 +49,31 @@ More details: http://pjreddie.com/darknet/yolo/
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There are weights-file for different cfg-files (trained for MS COCO dataset):
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FPS on GeForce 1080Ti:
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FPS on RTX 2070 (R) and Tesla V100 (V):
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* [csresnext50-panet-spp-original-optimal.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/csresnext50-panet-spp-original-optimal.cfg) - **65.4% mAP@0.5 (43.2% AP@0.5..0.95) - 35 FPS** - 100.5 BFlops - 217 MB: [csresnext50-panet-spp-original-optimal_final.weights](https://drive.google.com/open?id=1_NnfVgj0EDtb_WLNoXV8Mo7WKgwdYZCc)
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* [yolov4.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4.cfg) - 245 MB: [yolov4.weights](https://drive.google.com/open?id=1cewMfusmPjYWbrnuJRuKhPMwRe_b9PaT)
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* `width=608 height=608` in cfg: **65.7% mAP@0.5 (43.5% AP@0.5:0.95) - 34(R) FPS / 62(V) FPS** - 128.5 BFlops
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* `width=512 height=512` in cfg: **64.9% mAP@0.5 (43.0% AP@0.5:0.95) - 45(R) FPS / 83(V) FPS** - 91.1 BFlops
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* `width=416 height=416` in cfg: **62.8% mAP@0.5 (41.2% AP@0.5:0.95) - 55(R) FPS / 96(V) FPS** - 60.1 BFlops
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* `width=320 height=320` in cfg: ** 60% mAP@0.5 ( 38% AP@0.5:0.95) - 63(R) FPS / 123(V) FPS** - 35.5 BFlops
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* [yolov3-spp.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3-spp.cfg) - **60.6% mAP@0.5 - 30 FPS** - 141.5 BFlops - 240 MB: [yolov3-spp.weights](https://pjreddie.com/media/files/yolov3-spp.weights)
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* [yolov3-tiny-prn.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3-tiny-prn.cfg) - **33.1% mAP@0.5 - 370(R) FPS** - 3.5 BFlops - 18.8 MB: [yolov3-tiny-prn.weights](https://drive.google.com/file/d/18yYZWyKbo4XSDVyztmsEcF9B_6bxrhUY/view?usp=sharing)
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* [yolov3-tiny-prn.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3-tiny-prn.cfg) - **33.1% mAP@0.5 - 400 FPS** - 3.5 BFlops - 18.8 MB: [yolov3-tiny-prn.weights](https://drive.google.com/file/d/18yYZWyKbo4XSDVyztmsEcF9B_6bxrhUY/view?usp=sharing)
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* [enet-coco.cfg (EfficientNetB0-Yolov3)](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/enet-coco.cfg) - **45.5% mAP@0.5 - 55(R) FPS** - 3.7 BFlops - 18.3 MB: [enetb0-coco_final.weights](https://drive.google.com/file/d/1FlHeQjWEQVJt0ay1PVsiuuMzmtNyv36m/view)
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* [enet-coco.cfg (EfficientNetB0-Yolov3)](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/enet-coco.cfg) - **45.5% mAP@0.5 - 60 FPS** - 3.7 BFlops - 18.3 MB: [enetb0-coco_final.weights](https://drive.google.com/file/d/1FlHeQjWEQVJt0ay1PVsiuuMzmtNyv36m/view)
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* [yolov3-openimages.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3-openimages.cfg) - 247 MB - OpenImages dataset: [yolov3-openimages.weights](https://pjreddie.com/media/files/yolov3-openimages.weights)
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* [yolov3-openimages.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3-openimages.cfg) - 247 MB - 18(R) FPS - OpenImages dataset: [yolov3-openimages.weights](https://pjreddie.com/media/files/yolov3-openimages.weights)
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<details><summary><b>CLICK ME</b> - Yolo v3 models</summary>
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* [csresnext50-panet-spp-original-optimal.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/csresnext50-panet-spp-original-optimal.cfg) - **65.4% mAP@0.5 (43.2% AP@0.5:0.95) - 32(R) FPS** - 100.5 BFlops - 217 MB: [csresnext50-panet-spp-original-optimal_final.weights](https://drive.google.com/open?id=1_NnfVgj0EDtb_WLNoXV8Mo7WKgwdYZCc)
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* [yolov3-spp.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3-spp.cfg) - **60.6% mAP@0.5 - 38(R) FPS** - 141.5 BFlops - 240 MB: [yolov3-spp.weights](https://pjreddie.com/media/files/yolov3-spp.weights)
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* [csresnext50-panet-spp.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/csresnext50-panet-spp.cfg) - **60.0% mAP@0.5 - 44 FPS** - 71.3 BFlops - 217 MB: [csresnext50-panet-spp_final.weights](https://drive.google.com/file/d/1aNXdM8qVy11nqTcd2oaVB3mf7ckr258-/view?usp=sharing)
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* [yolov3.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3.cfg) - **55.3% mAP@0.5 - 46 FPS** - 65.9 BFlops - 236 MB: [yolov3.weights](https://pjreddie.com/media/files/yolov3.weights)
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* [yolov3.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3.cfg) - **55.3% mAP@0.5 - 66(R) FPS** - 65.9 BFlops - 236 MB: [yolov3.weights](https://pjreddie.com/media/files/yolov3.weights)
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* [yolov3-tiny.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3-tiny.cfg) - **33.1% mAP@0.5 - 330 FPS** - 5.6 BFlops - 33.7 MB: [yolov3-tiny.weights](https://pjreddie.com/media/files/yolov3-tiny.weights)
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* [yolov3-tiny.cfg](https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3-tiny.cfg) - **33.1% mAP@0.5 - 345(R) FPS** - 5.6 BFlops - 33.7 MB: [yolov3-tiny.weights](https://pjreddie.com/media/files/yolov3-tiny.weights)
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</details>
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@ -102,7 +108,7 @@ You can get cfg-files by path: `darknet/cfg/`
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* **Intel OpenVINO 2019 R1:** (Myriad X / USB Neural Compute Stick / Arria FPGA): read this [manual](https://software.intel.com/en-us/articles/OpenVINO-Using-TensorFlow#converting-a-darknet-yolo-model)
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* **OpenCV-dnn** the fastest implementation for CPU (x86/ARM-Android), OpenCV can be compiled with [OpenVINO-backend](https://github.com/opencv/opencv/wiki/Intel's-Deep-Learning-Inference-Engine-backend) for running on (Myriad X / USB Neural Compute Stick / Arria FPGA), use `yolov3.weights`/`cfg` with: [C++ example](https://github.com/opencv/opencv/blob/8c25a8eb7b10fb50cda323ee6bec68aa1a9ce43c/samples/dnn/object_detection.cpp#L192-L221) or [Python example](https://github.com/opencv/opencv/blob/8c25a8eb7b10fb50cda323ee6bec68aa1a9ce43c/samples/dnn/object_detection.py#L129-L150)
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* **PyTorch > ONNX > CoreML > iOS** how to convert cfg/weights-files to pt-file: [ultralytics/yolov3](https://github.com/ultralytics/yolov3#darknet-conversion) and [iOS App](https://itunes.apple.com/app/id1452689527)
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* **TensorRT** for YOLOv3 (-70% faster inference): [Yolo is natively supported in DeepStream 4.0](https://news.developer.nvidia.com/deepstream-sdk-4-now-available/)
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* **TensorRT** for YOLOv3 (-70% faster inference): [Yolo is natively supported in DeepStream 4.0](https://news.developer.nvidia.com/deepstream-sdk-4-now-available/) read [PDF](https://docs.nvidia.com/metropolis/deepstream/Custom_YOLO_Model_in_the_DeepStream_YOLO_App.pdf)
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* **TVM** - compilation of deep learning models (Keras, MXNet, PyTorch, Tensorflow, CoreML, DarkNet) into minimum deployable modules on diverse hardware backends (CPUs, GPUs, FPGA, and specialized accelerators): https://tvm.ai/about
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* **OpenDataCam** - It detects, tracks and counts moving objects by using Yolo: https://github.com/opendatacam/opendatacam#-hardware-pre-requisite
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* **Netron** - Visualizer for neural networks: https://github.com/lutzroeder/netron
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@ -153,30 +159,30 @@ Also, you might be interested in using a simplified repository where is implemen
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#### How to use on the command line
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On Linux use `./darknet` instead of `darknet.exe`, like this:`./darknet detector test ./cfg/coco.data ./cfg/yolov3.cfg ./yolov3.weights`
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On Linux use `./darknet` instead of `darknet.exe`, like this:`./darknet detector test ./cfg/coco.data ./cfg/yolov4.cfg ./yolov4.weights`
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On Linux find executable file `./darknet` in the root directory, while on Windows find it in the directory `\build\darknet\x64`
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* Yolo v3 COCO - **image**: `darknet.exe detector test cfg/coco.data cfg/yolov3.cfg yolov3.weights -thresh 0.25`
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* **Output coordinates** of objects: `darknet.exe detector test cfg/coco.data yolov3.cfg yolov3.weights -ext_output dog.jpg`
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* Yolo v3 COCO - **video**: `darknet.exe detector demo cfg/coco.data cfg/yolov3.cfg yolov3.weights -ext_output test.mp4`
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* Yolo v3 COCO - **WebCam 0**: `darknet.exe detector demo cfg/coco.data cfg/yolov3.cfg yolov3.weights -c 0`
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* Yolo v3 COCO for **net-videocam** - Smart WebCam: `darknet.exe detector demo cfg/coco.data cfg/yolov3.cfg yolov3.weights http://192.168.0.80:8080/video?dummy=param.mjpg`
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* Yolo v3 - **save result videofile res.avi**: `darknet.exe detector demo cfg/coco.data cfg/yolov3.cfg yolov3.weights test.mp4 -out_filename res.avi`
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* Yolo v3 COCO - **image**: `darknet.exe detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -thresh 0.25`
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* **Output coordinates** of objects: `darknet.exe detector test cfg/coco.data yolov4.cfg yolov4.weights -ext_output dog.jpg`
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* Yolo v3 COCO - **video**: `darknet.exe detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights -ext_output test.mp4`
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* Yolo v3 COCO - **WebCam 0**: `darknet.exe detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights -c 0`
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* Yolo v3 COCO for **net-videocam** - Smart WebCam: `darknet.exe detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights http://192.168.0.80:8080/video?dummy=param.mjpg`
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* Yolo v3 - **save result videofile res.avi**: `darknet.exe detector demo cfg/coco.data cfg/yolov4.cfg yolov4.weights test.mp4 -out_filename res.avi`
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* Yolo v3 **Tiny** COCO - video: `darknet.exe detector demo cfg/coco.data cfg/yolov3-tiny.cfg yolov3-tiny.weights test.mp4`
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* **JSON and MJPEG server** that allows multiple connections from your soft or Web-browser `ip-address:8070` and 8090: `./darknet detector demo ./cfg/coco.data ./cfg/yolov3.cfg ./yolov3.weights test50.mp4 -json_port 8070 -mjpeg_port 8090 -ext_output`
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* Yolo v3 Tiny **on GPU #1**: `darknet.exe detector demo cfg/coco.data cfg/yolov3-tiny.cfg yolov3-tiny.weights -i 1 test.mp4`
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* Alternative method Yolo v3 COCO - image: `darknet.exe detect cfg/yolov3.cfg yolov3.weights -i 0 -thresh 0.25`
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* Alternative method Yolo v3 COCO - image: `darknet.exe detect cfg/yolov4.cfg yolov4.weights -i 0 -thresh 0.25`
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* Train on **Amazon EC2**, to see mAP & Loss-chart using URL like: `http://ec2-35-160-228-91.us-west-2.compute.amazonaws.com:8090` in the Chrome/Firefox (**Darknet should be compiled with OpenCV**):
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`./darknet detector train cfg/coco.data yolov3.cfg darknet53.conv.74 -dont_show -mjpeg_port 8090 -map`
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`./darknet detector train cfg/coco.data yolov4.cfg yolov4.conv.137 -dont_show -mjpeg_port 8090 -map`
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* 186 MB Yolo9000 - image: `darknet.exe detector test cfg/combine9k.data cfg/yolo9000.cfg yolo9000.weights`
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* Remeber to put data/9k.tree and data/coco9k.map under the same folder of your app if you use the cpp api to build an app
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* To process a list of images `data/train.txt` and save results of detection to `result.json` file use:
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`darknet.exe detector test cfg/coco.data cfg/yolov3.cfg yolov3.weights -ext_output -dont_show -out result.json < data/train.txt`
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`darknet.exe detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -ext_output -dont_show -out result.json < data/train.txt`
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* To process a list of images `data/train.txt` and save results of detection to `result.txt` use:
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`darknet.exe detector test cfg/coco.data cfg/yolov3.cfg yolov3.weights -dont_show -ext_output < data/train.txt > result.txt`
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`darknet.exe detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -dont_show -ext_output < data/train.txt > result.txt`
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* Pseudo-lableing - to process a list of images `data/new_train.txt` and save results of detection in Yolo training format for each image as label `<image_name>.txt` (in this way you can increase the amount of training data) use:
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`darknet.exe detector test cfg/coco.data cfg/yolov3.cfg yolov3.weights -thresh 0.25 -dont_show -save_labels < data/new_train.txt`
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`darknet.exe detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -thresh 0.25 -dont_show -save_labels < data/new_train.txt`
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* To calculate anchors: `darknet.exe detector calc_anchors data/obj.data -num_of_clusters 9 -width 416 -height 416`
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* To check accuracy mAP@IoU=50: `darknet.exe detector map data/obj.data yolo-obj.cfg backup\yolo-obj_7000.weights`
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* To check accuracy mAP@IoU=75: `darknet.exe detector map data/obj.data yolo-obj.cfg backup\yolo-obj_7000.weights -iou_thresh 0.75`
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@ -192,7 +198,7 @@ On Linux find executable file `./darknet` in the root directory, while on Window
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3. Start Smart WebCam on your phone
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4. Replace the address below, on shown in the phone application (Smart WebCam) and launch:
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* Yolo v3 COCO-model: `darknet.exe detector demo data/coco.data yolov3.cfg yolov3.weights http://192.168.0.80:8080/video?dummy=param.mjpg -i 0`
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* Yolo v3 COCO-model: `darknet.exe detector demo data/coco.data yolov4.cfg yolov4.weights http://192.168.0.80:8080/video?dummy=param.mjpg -i 0`
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### How to compile on Linux (using `cmake`)
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@ -222,9 +228,9 @@ Before make, you can set such options in the `Makefile`: [link](https://github.c
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* `DEBUG=1` to bould debug version of Yolo
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* `OPENMP=1` to build with OpenMP support to accelerate Yolo by using multi-core CPU
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* `LIBSO=1` to build a library `darknet.so` and binary runable file `uselib` that uses this library. Or you can try to run so `LD_LIBRARY_PATH=./:$LD_LIBRARY_PATH ./uselib test.mp4` How to use this SO-library from your own code - you can look at C++ example: https://github.com/AlexeyAB/darknet/blob/master/src/yolo_console_dll.cpp
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or use in such a way: `LD_LIBRARY_PATH=./:$LD_LIBRARY_PATH ./uselib data/coco.names cfg/yolov3.cfg yolov3.weights test.mp4`
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or use in such a way: `LD_LIBRARY_PATH=./:$LD_LIBRARY_PATH ./uselib data/coco.names cfg/yolov4.cfg yolov4.weights test.mp4`
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* `ZED_CAMERA=1` to build a library with ZED-3D-camera support (should be ZED SDK installed), then run
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`LD_LIBRARY_PATH=./:$LD_LIBRARY_PATH ./uselib data/coco.names cfg/yolov3.cfg yolov3.weights zed_camera`
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`LD_LIBRARY_PATH=./:$LD_LIBRARY_PATH ./uselib data/coco.names cfg/yolov4.cfg yolov4.weights zed_camera`
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To run Darknet on Linux use examples from this article, just use `./darknet` instead of `darknet.exe`, i.e. use this command: `./darknet detector test ./cfg/coco.data ./cfg/yolov3.cfg ./yolov3.weights`
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@ -343,6 +349,8 @@ Then add to your created project:
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## How to train (Pascal VOC Data):
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<details><summary><b>CLICK ME</b> - How to train (Pascal VOC Data)</summary>
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1. Download pre-trained weights for the convolutional layers (154 MB): http://pjreddie.com/media/files/darknet53.conv.74 and put to the directory `build\darknet\x64`
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2. Download The Pascal VOC Data and unpack it to directory `build\darknet\x64\data\voc` will be created dir `build\darknet\x64\data\voc\VOCdevkit\`:
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**Note:** If during training you see `nan` values for `avg` (loss) field - then training goes wrong, but if `nan` is in some other lines - then training goes well.
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</details>
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## How to train with multi-GPU:
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1. Train it first on 1 GPU for like 1000 iterations: `darknet.exe detector train cfg/voc.data cfg/yolov3-voc.cfg darknet53.conv.74`
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@ -387,7 +397,7 @@ https://groups.google.com/d/msg/darknet/NbJqonJBTSY/Te5PfIpuCAAJ
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Training Yolo v3:
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1. Create file `yolo-obj.cfg` with the same content as in `yolov3.cfg` (or copy `yolov3.cfg` to `yolo-obj.cfg)` and:
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1. Create file `yolo-obj.cfg` with the same content as in `yolov4-custom.cfg` (or copy `yolov4-custom.cfg` to `yolo-obj.cfg)` and:
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* change line batch to [`batch=64`](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L3)
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* change line subdivisions to [`subdivisions=16`](https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L4)
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(Generally `filters` depends on the `classes`, `coords` and number of `mask`s, i.e. filters=`(classes + coords + 1)*<number of mask>`, where `mask` is indices of anchors. If `mask` is absence, then filters=`(classes + coords + 1)*num`)
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So for example, for 2 objects, your file `yolo-obj.cfg` should differ from `yolov3.cfg` in such lines in each of **3** [yolo]-layers:
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So for example, for 2 objects, your file `yolo-obj.cfg` should differ from `yolov4-custom.cfg` in such lines in each of **3** [yolo]-layers:
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```
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[convolutional]
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```
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7. Download pre-trained weights for the convolutional layers and put to the directory `build\darknet\x64`
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* for `yolov4.cfg`, `yolov4-custom.cfg` (162 MB): [yolov4.conv.137](https://drive.google.com/open?id=1JKF-bdIklxOOVy-2Cr5qdvjgGpmGfcbp)
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* for `csresnext50-panet-spp.cfg` (133 MB): [csresnext50-panet-spp.conv.112](https://drive.google.com/file/d/16yMYCLQTY_oDlCIZPfn_sab6KD3zgzGq/view?usp=sharing)
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* for `yolov3.cfg, yolov3-spp.cfg` (154 MB): [darknet53.conv.74](https://pjreddie.com/media/files/darknet53.conv.74)
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* for `yolov3-tiny-prn.cfg , yolov3-tiny.cfg` (6 MB): [yolov3-tiny.conv.11](https://drive.google.com/file/d/18v36esoXCh-PsOKwyP2GWrpYDptDY8Zf/view?usp=sharing)
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* for `enet-coco.cfg (EfficientNetB0-Yolov3)` (14 MB): [enetb0-coco.conv.132](https://drive.google.com/file/d/1uhh3D6RSn0ekgmsaTcl-ZW53WBaUDo6j/view?usp=sharing)
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8. Start training by using the command line: `darknet.exe detector train data/obj.data yolo-obj.cfg darknet53.conv.74`
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8. Start training by using the command line: `darknet.exe detector train data/obj.data yolo-obj.cfg yolov4.conv.137`
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To train on Linux use command: `./darknet detector train data/obj.data yolo-obj.cfg darknet53.conv.74` (just use `./darknet` instead of `darknet.exe`)
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To train on Linux use command: `./darknet detector train data/obj.data yolo-obj.cfg yolov4.conv.137` (just use `./darknet` instead of `darknet.exe`)
|
||||
|
||||
* (file `yolo-obj_last.weights` will be saved to the `build\darknet\x64\backup\` for each 100 iterations)
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||||
* (file `yolo-obj_xxxx.weights` will be saved to the `build\darknet\x64\backup\` for each 1000 iterations)
|
||||
* (to disable Loss-Window use `darknet.exe detector train data/obj.data yolo-obj.cfg darknet53.conv.74 -dont_show`, if you train on computer without monitor like a cloud Amazon EC2)
|
||||
* (to see the mAP & Loss-chart during training on remote server without GUI, use command `darknet.exe detector train data/obj.data yolo-obj.cfg darknet53.conv.74 -dont_show -mjpeg_port 8090 -map` then open URL `http://ip-address:8090` in Chrome/Firefox browser)
|
||||
* (to disable Loss-Window use `darknet.exe detector train data/obj.data yolo-obj.cfg yolov4.conv.137 -dont_show`, if you train on computer without monitor like a cloud Amazon EC2)
|
||||
* (to see the mAP & Loss-chart during training on remote server without GUI, use command `darknet.exe detector train data/obj.data yolo-obj.cfg yolov4.conv.137 -dont_show -mjpeg_port 8090 -map` then open URL `http://ip-address:8090` in Chrome/Firefox browser)
|
||||
|
||||
8.1. For training with mAP (mean average precisions) calculation for each 4 Epochs (set `valid=valid.txt` or `train.txt` in `obj.data` file) and run: `darknet.exe detector train data/obj.data yolo-obj.cfg darknet53.conv.74 -map`
|
||||
8.1. For training with mAP (mean average precisions) calculation for each 4 Epochs (set `valid=valid.txt` or `train.txt` in `obj.data` file) and run: `darknet.exe detector train data/obj.data yolo-obj.cfg yolov4.conv.137 -map`
|
||||
|
||||
9. After training is complete - get result `yolo-obj_final.weights` from path `build\darknet\x64\backup\`
|
||||
|
||||
|
|
@ -553,7 +564,7 @@ For example, **bigger mAP** gives weights `yolo-obj_8000.weights` - then **use t
|
|||
|
||||
Or just train with `-map` flag:
|
||||
|
||||
`darknet.exe detector train data/obj.data yolo-obj.cfg darknet53.conv.74 -map`
|
||||
`darknet.exe detector train data/obj.data yolo-obj.cfg yolov4.conv.137 -map`
|
||||
|
||||
So you will see mAP-chart (red-line) in the Loss-chart Window. mAP will be calculated for each 4 Epochs using `valid=valid.txt` file that is specified in `obj.data` file (`1 Epoch = images_in_train_txt / batch` iterations)
|
||||
|
||||
|
|
@ -635,9 +646,7 @@ Example of custom object detection: `darknet.exe detector test data/obj.data yol
|
|||
|
||||
That is, if only objects that occupied 80-90% of the image were present in the training set, then the trained network will not be able to detect objects that occupy 1-10% of the image.
|
||||
|
||||
* to speedup training (with decreasing detection accuracy) do Fine-Tuning instead of Transfer-Learning, set param `stopbackward=1` here: https://github.com/AlexeyAB/darknet/blob/6d44529cf93211c319813c90e0c1adb34426abe5/cfg/yolov3.cfg#L548
|
||||
then do this command: `./darknet partial cfg/yolov3.cfg yolov3.weights yolov3.conv.81 81` will be created file `yolov3.conv.81`,
|
||||
then train by using weights file `yolov3.conv.81` instead of `darknet53.conv.74`
|
||||
* to speedup training (with decreasing detection accuracy) set param `stopbackward=1` for layer-136 in cfg-file
|
||||
|
||||
* each: `model of object, side, illimination, scale, each 30 grad` of the turn and inclination angles - these are *different objects* from an internal perspective of the neural network. So the more *different objects* you want to detect, the more complex network model should be used.
|
||||
|
||||
|
|
@ -720,7 +729,7 @@ There are 2 APIs:
|
|||
2. To use Yolo as DLL-file in your C++ console application - open the solution `build\darknet\yolo_console_dll.sln`, set **x64** and **Release**, and do the: Build -> Build yolo_console_dll
|
||||
|
||||
* you can run your console application from Windows Explorer `build\darknet\x64\yolo_console_dll.exe`
|
||||
**use this command**: `yolo_console_dll.exe data/coco.names yolov3.cfg yolov3.weights test.mp4`
|
||||
**use this command**: `yolo_console_dll.exe data/coco.names yolov4.cfg yolov4.weights test.mp4`
|
||||
|
||||
* after launching your console application and entering the image file name - you will see info for each object:
|
||||
`<obj_id> <left_x> <top_y> <width> <height> <probability>`
|
||||
|
|
|
|||
File diff suppressed because it is too large
Load Diff
|
|
@ -5,8 +5,8 @@
|
|||
# Training
|
||||
batch=64
|
||||
subdivisions=8
|
||||
width=512
|
||||
height=512
|
||||
width=608
|
||||
height=608
|
||||
channels=3
|
||||
momentum=0.949
|
||||
decay=0.0005
|
||||
File diff suppressed because it is too large
Load Diff
|
|
@ -5,8 +5,8 @@
|
|||
# Training
|
||||
batch=64
|
||||
subdivisions=8
|
||||
width=512
|
||||
height=512
|
||||
width=608
|
||||
height=608
|
||||
channels=3
|
||||
momentum=0.949
|
||||
decay=0.0005
|
||||
Loading…
Reference in New Issue