![Darknet Logo](http://pjreddie.com/media/files/darknet-black-small.png)
# Darknet #
Darknet is an open source neural network framework written in C and CUDA. It is fast, easy to install, and supports CPU and GPU computation.
**Discord** invite link for for communication and questions: https://discord.gg/zSq8rtW
## Scaled-YOLOv4:
* **paper (CVPR 2021)**: https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Scaled-YOLOv4_Scaling_Cross_Stage_Partial_Network_CVPR_2021_paper.html
* **source code - Pytorch (use to reproduce results):** https://github.com/WongKinYiu/ScaledYOLOv4
* **source code - Darknet:** https://github.com/AlexeyAB/darknet
* **Medium:** https://alexeyab84.medium.com/scaled-yolo-v4-is-the-best-neural-network-for-object-detection-on-ms-coco-dataset-39dfa22fa982?source=friends_link&sk=c8553bfed861b1a7932f739d26f487c8
## YOLOv4:
* **paper:** https://arxiv.org/abs/2004.10934
* **source code:** https://github.com/AlexeyAB/darknet
* **Wiki:** https://github.com/AlexeyAB/darknet/wiki
* **useful links:** https://medium.com/@alexeyab84/yolov4-the-most-accurate-real-time-neural-network-on-ms-coco-dataset-73adfd3602fe?source=friends_link&sk=6039748846bbcf1d960c3061542591d7
For more information see the [Darknet project website](http://pjreddie.com/darknet).
![yolo_progress](https://user-images.githubusercontent.com/4096485/146988929-1ed0cbec-1e01-4ad0-b42c-808dcef32994.png) https://paperswithcode.com/sota/object-detection-on-coco
----
![scaled_yolov4](https://user-images.githubusercontent.com/4096485/112776361-281d8380-9048-11eb-8083-8728b12dcd55.png) AP50:95 - FPS (Tesla V100) Paper: https://arxiv.org/abs/2011.08036
----
![YOLOv4Tiny](https://user-images.githubusercontent.com/4096485/101363015-e5c21200-38b1-11eb-986f-b3e516e05977.png)
----
![YOLOv4](https://user-images.githubusercontent.com/4096485/90338826-06114c80-dff5-11ea-9ba2-8eb63a7409b3.png)
----
![OpenCV_TRT](https://user-images.githubusercontent.com/4096485/90338805-e5e18d80-dff4-11ea-8a68-5710956256ff.png)
## Citation
```
@misc{bochkovskiy2020yolov4,
title={YOLOv4: Optimal Speed and Accuracy of Object Detection},
author={Alexey Bochkovskiy and Chien-Yao Wang and Hong-Yuan Mark Liao},
year={2020},
eprint={2004.10934},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
```
@InProceedings{Wang_2021_CVPR,
author = {Wang, Chien-Yao and Bochkovskiy, Alexey and Liao, Hong-Yuan Mark},
title = {{Scaled-YOLOv4}: Scaling Cross Stage Partial Network},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
pages = {13029-13038}
}
```
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编译好的darknet win10
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编译好的darknet win10 (1045个子文件)
libdarknet.a 697KB
data.c 47KB
parser.c 44KB
lsd.c 44KB
go.c 43KB
image.c 38KB
classifier.c 35KB
network.c 30KB
detector.c 27KB
lstm_layer.c 24KB
region_layer.c 19KB
convolutional_layer.c 19KB
darknet.c 18KB
attention.c 15KB
rnn.c 15KB
utils.c 14KB
gru_layer.c 13KB
nightmare.c 13KB
coco.c 13KB
yolo_layer.c 12KB
connected_layer.c 11KB
yolo.c 11KB
captcha.c 11KB
compare.c 11KB
batchnorm_layer.c 10KB
detection_layer.c 10KB
rnn_layer.c 10KB
demo.c 10KB
deconvolutional_layer.c 10KB
blas.c 9KB
crnn_layer.c 9KB
local_layer.c 9KB
box.c 8KB
gemm.c 8KB
cifar.c 8KB
instance-segmenter.c 8KB
segmenter.c 8KB
regressor.c 7KB
iseg_layer.c 7KB
rnn_vid.c 7KB
normalization_layer.c 5KB
cost_layer.c 5KB
reorg_layer.c 5KB
voxel.c 5KB
layer.c 4KB
writing.c 4KB
tag.c 4KB
matrix.c 4KB
cuda.c 4KB
maxpool_layer.c 4KB
route_layer.c 4KB
activations.c 4KB
tree.c 4KB
super.c 4KB
dice.c 4KB
softmax_layer.c 3KB
upsample_layer.c 3KB
option_list.c 3KB
shortcut_layer.c 3KB
crop_layer.c 3KB
swag.c 2KB
logistic_layer.c 2KB
avgpool_layer.c 2KB
l2norm_layer.c 2KB
activation_layer.c 2KB
dropout_layer.c 2KB
art.c 1KB
list.c 1KB
col2im.c 1KB
im2col.c 1KB
densenet201.cfg 19KB
resnext152-32x4d.cfg 16KB
resnet152.cfg 15KB
resnext101-32x4d.cfg 11KB
resnet101.cfg 10KB
yolov3-spp.cfg 8KB
yolov3-openimages.cfg 8KB
yolov3.cfg 8KB
yolov3-voc.cfg 8KB
darknet53.cfg 6KB
darknet53_448.cfg 6KB
resnext50.cfg 5KB
resnet50.cfg 5KB
resnet34.cfg 4KB
yolov1.cfg 3KB
yolov2.cfg 3KB
yolov2-voc.cfg 3KB
resnet18.cfg 2KB
yolo9000.cfg 2KB
extraction22k.cfg 2KB
extraction.cfg 2KB
darknet9000.cfg 2KB
darknet19.cfg 2KB
darknet19_448.cfg 2KB
yolov3-tiny.cfg 2KB
strided.cfg 2KB
tiny.cfg 2KB
extraction.conv.cfg 2KB
yolov2-tiny.cfg 1KB
yolov2-tiny-voc.cfg 1KB
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