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| | | * **GPU with CC >= 3.0**: https://en.wikipedia.org/wiki/CUDA#GPUs_supported |
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| | | |
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| | | ##### Pre-trained models for different cfg-files can be downloaded from (smaller -> faster & lower quality): |
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| | | * `yolov3.cfg` (236 MB COCO **Yolo v3**) - require 4 GB GPU-RAM: https://pjreddie.com/media/files/yolov3.weights |
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| | | * `yolov2.cfg` (194 MB COCO Yolo v2) - require 4 GB GPU-RAM: https://pjreddie.com/media/files/yolov2.weights |
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| | | * `yolo-voc.cfg` (194 MB VOC Yolo v2) - require 4 GB GPU-RAM: http://pjreddie.com/media/files/yolo-voc.weights |
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| | | * `yolov2-tiny.cfg` (43 MB COCO Yolo v2) - require 1 GB GPU-RAM: https://pjreddie.com/media/files/yolov2-tiny.weights |
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| | | * `yolov2-tiny-voc.cfg` (60 MB VOC Yolo v2) - require 1 GB GPU-RAM: http://pjreddie.com/media/files/yolov2-tiny-voc.weights |
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| | | * `yolo9000.cfg` (186 MB Yolo9000-model) - require 4 GB GPU-RAM: http://pjreddie.com/media/files/yolo9000.weights |
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| | | * `yolov3.cfg` (236 MB COCO **Yolo v3**) - requires 4 GB GPU-RAM: https://pjreddie.com/media/files/yolov3.weights |
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| | | * `yolov3-tiny.cfg` (34 MB COCO **Yolo v3 tiny**) - requires 1 GB GPU-RAM: https://pjreddie.com/media/files/yolov3-tiny.weights |
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| | | * `yolov2.cfg` (194 MB COCO Yolo v2) - requires 4 GB GPU-RAM: https://pjreddie.com/media/files/yolov2.weights |
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| | | * `yolo-voc.cfg` (194 MB VOC Yolo v2) - requires 4 GB GPU-RAM: http://pjreddie.com/media/files/yolo-voc.weights |
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| | | * `yolov2-tiny.cfg` (43 MB COCO Yolo v2) - requires 1 GB GPU-RAM: https://pjreddie.com/media/files/yolov2-tiny.weights |
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| | | * `yolov2-tiny-voc.cfg` (60 MB VOC Yolo v2) - requires 1 GB GPU-RAM: http://pjreddie.com/media/files/yolov2-tiny-voc.weights |
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| | | * `yolo9000.cfg` (186 MB Yolo9000-model) - requires 4 GB GPU-RAM: http://pjreddie.com/media/files/yolo9000.weights |
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| | | |
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| | | Put it near compiled: darknet.exe |
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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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| | | |
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| | | * 194 MB COCO-model - image: `darknet.exe detector test data/coco.data yolo.cfg yolo.weights -i 0 -thresh 0.2` |
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| | | * Alternative method 194 MB COCO-model - image: `darknet.exe detect yolo.cfg yolo.weights -i 0 -thresh 0.2` |
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| | | * **Yolo v3** COCO - image: `darknet.exe detector test data/coco.data cfg/yolov3.cfg yolov3.weights -i 0 -thresh 0.25` |
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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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| | | * Output coordinates of objects: `darknet.exe detector test data/coco.data yolov3.cfg yolov3.weights -thresh 0.25 dog.jpg -ext_output` |
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| | | * 194 MB VOC-model - image: `darknet.exe detector test data/voc.data yolo-voc.cfg yolo-voc.weights -i 0` |
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| | | * 194 MB COCO-model - video: `darknet.exe detector demo data/coco.data yolo.cfg yolo.weights test.mp4 -i 0` |
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| | | * 194 MB VOC-model - video: `darknet.exe detector demo data/voc.data yolo-voc.cfg yolo-voc.weights test.mp4 -i 0` |
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| | | * 194 MB COCO-model - **save result to the file res.avi**: `darknet.exe detector demo data/coco.data yolo.cfg yolo.weights test.mp4 -i 0 -out_filename res.avi` |
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| | | * 194 MB VOC-model - **save result to the file res.avi**: `darknet.exe detector demo data/voc.data yolo-voc.cfg yolo-voc.weights test.mp4 -i 0 -out_filename res.avi` |
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| | | * Alternative method 194 MB VOC-model - video: `darknet.exe yolo demo yolo-voc.cfg yolo-voc.weights test.mp4 -i 0` |
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| | | * 60 MB VOC-model for video: `darknet.exe detector demo data/voc.data tiny-yolo-voc.cfg tiny-yolo-voc.weights test.mp4 -i 0` |
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| | | * 194 MB COCO-model for net-videocam - Smart WebCam: `darknet.exe detector demo data/coco.data yolo.cfg yolo.weights http://192.168.0.80:8080/video?dummy=param.mjpg -i 0` |
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| | | * 43 MB VOC-model for video: `darknet.exe detector demo data/coco.data cfg/yolov2-tiny.cfg yolov2-tiny.weights test.mp4 -i 0` |
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| | | * **Yolo v3** 236 MB COCO for net-videocam - Smart WebCam: `darknet.exe detector demo data/coco.data cfg/yolov3.cfg yolov3.weights http://192.168.0.80:8080/video?dummy=param.mjpg -i 0` |
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| | | * 194 MB VOC-model for net-videocam - Smart WebCam: `darknet.exe detector demo data/voc.data yolo-voc.cfg yolo-voc.weights http://192.168.0.80:8080/video?dummy=param.mjpg -i 0` |
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| | | * 194 MB VOC-model - WebCamera #0: `darknet.exe detector demo data/voc.data yolo-voc.cfg yolo-voc.weights -c 0` |
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| | | * 186 MB Yolo9000 - image: `darknet.exe detector test cfg/combine9k.data yolo9000.cfg yolo9000.weights` |
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| | | * 186 MB Yolo9000 - video: `darknet.exe detector demo cfg/combine9k.data yolo9000.cfg yolo9000.weights test.mp4` |
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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.txt` use: |
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| | | `darknet.exe detector test data/voc.data yolo-voc.cfg yolo-voc.weights -dont_show < data/train.txt > result.txt` |
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| | | `darknet.exe detector test data/voc.data yolo-voc.cfg yolo-voc.weights -dont_show -ext_output < data/train.txt > result.txt` |
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| | | You can comment this line so that each image does not require pressing the button ESC: https://github.com/AlexeyAB/darknet/blob/6ccb41808caf753feea58ca9df79d6367dedc434/src/detector.c#L509 |
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| | | |
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| | | ##### For using network video-camera mjpeg-stream with any Android smartphone: |
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| | | More information about training by the link: http://pjreddie.com/darknet/yolo/#train-voc |
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| | | **Note:** If during training you see `nan` values in some lines then training goes well, but if `nan` are in all lines then training goes wrong. |
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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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| | | |
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| | | ## How to train with multi-GPU: |
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| | | * Also you can get result earlier than all 45000 iterations. |
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| | | |
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| | | **Note:** If during training you see `nan` values in some lines then training goes well, but if `nan` are in all lines then training goes wrong. |
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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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| | | |
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| | | ### How to train tiny-yolo (to detect your custom objects): |
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| | | Do all the same steps as for the full yolo model as described above. With the exception of: |
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| | | * Download default weights file for yolov2-tiny-voc: http://pjreddie.com/media/files/yolov2-tiny-voc.weights |
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| | | * Get pre-trained weights yolov2-tiny-voc.conv.13 using command: `darknet.exe partial cfg/yolov2-tiny-voc.cfg yolov2-tiny-voc.weights yolov2-tiny-voc.conv.13 13` |
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| | | * Make your custom model `yolov2-tiny-obj.cfg` based on `cfg/yolov2-tiny-voc.cfg` instead of `yolov3.cfg` |
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| | | * Start training: `darknet.exe detector train data/obj.data yolov2-tiny-obj.cfg yolov2-tiny-voc.conv.13` |
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| | | * Download default weights file for yolov3-tiny: https://pjreddie.com/media/files/yolov3-tiny.weights |
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| | | * Get pre-trained weights `yolov3-tiny.conv.15` using command: `darknet.exe partial cfg/yolov3-tiny.cfg yolov3-tiny.weights yolov3-tiny.conv.15 15` |
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| | | * Make your custom model `yolov3-tiny-obj.cfg` based on `cfg/yolov3-tiny_obj.cfg` instead of `yolov3.cfg` |
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| | | * Start training: `darknet.exe detector train data/obj.data yolov3-tiny-obj.cfg yolov3-tiny.conv.15` |
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| | | For training Yolo based on other models ([DenseNet201-Yolo](https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/densenet201_yolo.cfg) or [ResNet50-Yolo](https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/resnet50_yolo.cfg)), you can download and get pre-trained weights as showed in this file: https://github.com/AlexeyAB/darknet/blob/master/build/darknet/x64/partial.cmd |
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| | | If you made you custom model that isn't based on other models, then you can train it without pre-trained weights, then will be used random initial weights. |
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| | | |
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| | | 2. Once training is stopped, you should take some of last `.weights`-files from `darknet\build\darknet\x64\backup` and choose the best of them: |
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| | | For example, you stopped training after 9000 iterations, but the best result can give one of previous weights (7000, 8000, 9000). It can happen due to overfitting. **Overfitting** - is case when you can detect objects on images from training-dataset, but can't detect ojbects on any others images. You should get weights from **Early Stopping Point**: |
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| | | For example, you stopped training after 9000 iterations, but the best result can give one of previous weights (7000, 8000, 9000). It can happen due to overfitting. **Overfitting** - is case when you can detect objects on images from training-dataset, but can't detect objects on any others images. You should get weights from **Early Stopping Point**: |
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| | |  |
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| | | * increase network resolution in your `.cfg`-file (`height=608`, `width=608` or any value multiple of 32) - it will increase precision |
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| | | |
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| | | * recalculate anchors for your dataset for `width` and `height` from cfg-file: |
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| | | `darknet.exe detector calc_anchors data/obj.data -num_of_clusters 9 -width 416 -heigh 416` |
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| | | `darknet.exe detector calc_anchors data/obj.data -num_of_clusters 9 -width 416 -height 416` |
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| | | then set the same 9 `anchors` in each of 3 `[yolo]`-layers in your cfg-file |
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| | | |
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| | | * check that each object are mandatory labeled in your dataset - no one object in your data set should not be without label. In the most training issues - there are wrong labels in your dataset (got labels by using some conversion script, marked with a third-party tool, ...). Always check your dataset by using: https://github.com/AlexeyAB/Yolo_mark |
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| | | * desirable that your training dataset include images with objects at diffrent: scales, rotations, lightings, from different sides, on different backgrounds |
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| | | * desirable that your training dataset include images with non-labeled objects that you do not want to detect - negative samples without bounded box |
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| | | * desirable that your training dataset include images with non-labeled objects that you do not want to detect - negative samples without bounded box (empty `.txt` files) |
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| | | |
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| | | * for training with a large number of objects in each image, add the parameter `max=200` or higher value in the last layer [region] in your cfg-file |
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| | | |
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| | | * for training for small objects - set `layers = -1, 11` instead of https://github.com/AlexeyAB/darknet/blob/6390a5a2ab61a0bdf6f1a9a6b4a739c16b36e0d7/cfg/yolov3.cfg#L720 |
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| | | and set `stride=4` instead of https://github.com/AlexeyAB/darknet/blob/6390a5a2ab61a0bdf6f1a9a6b4a739c16b36e0d7/cfg/yolov3.cfg#L717 |
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| | | * General rule - you should keep relative size of objects in the Training and Testing datasets roughly the same: |
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| | | * `train_network_width * train_obj_width / train_image_width ~= detection_network_width * detection_obj_width / detection_image_width` |
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| | | * `train_network_height * train_obj_height / train_image_height ~= detection_network_height * detection_obj_height / detection_image_height` |
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| | | |
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| | | * to speedup training (with decreasing detection accuracy) do Fine-Tuning instead of Transfer-Learning, set param `stopbackward=1` in one of the penultimate convolutional layers before the 1-st `[yolo]`-layer, for example here: https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L598 |
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| | | 2. After training - for detection: |
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