From 0f3b8bb1fae5885474ba861bbbd1924fb622ee93 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Wed, 15 Jul 2020 00:28:53 -0700 Subject: [PATCH] Delete README.md --- README.md | 122 ------------------------------------------------------ 1 file changed, 122 deletions(-) delete mode 100755 README.md diff --git a/README.md b/README.md deleted file mode 100755 index fc09cfc94ff5..000000000000 --- a/README.md +++ /dev/null @@ -1,122 +0,0 @@ - - -  - -This repository represents Ultralytics open-source research into future object detection methods, and incorporates our lessons learned and best practices evolved over training thousands of models on custom client datasets with our previous YOLO repository https://github.com/ultralytics/yolov3. **All code and models are under active development, and are subject to modification or deletion without notice.** Use at your own risk. - -** GPU Speed measures end-to-end time per image averaged over 5000 COCO val2017 images using a V100 GPU with batch size 8, and includes image preprocessing, PyTorch FP16 inference, postprocessing and NMS. - -- **June 22, 2020**: [PANet](https://arxiv.org/abs/1803.01534) updates: new heads, reduced parameters, faster inference and improved mAP [364fcfd](https://github.com/ultralytics/yolov5/commit/364fcfd7dba53f46edd4f04c037a039c0a287972). -- **June 19, 2020**: [FP16](https://pytorch.org/docs/stable/nn.html#torch.nn.Module.half) as new default for smaller checkpoints and faster inference [d4c6674](https://github.com/ultralytics/yolov5/commit/d4c6674c98e19df4c40e33a777610a18d1961145). -- **June 9, 2020**: [CSP](https://github.com/WongKinYiu/CrossStagePartialNetworks) updates: improved speed, size, and accuracy (credit to @WongKinYiu for CSP). -- **May 27, 2020**: Public release of repo. YOLOv5 models are SOTA among all known YOLO implementations. -- **April 1, 2020**: Start development of future [YOLOv3](https://github.com/ultralytics/yolov3)/[YOLOv4](https://github.com/AlexeyAB/darknet)-based PyTorch models in a range of compound-scaled sizes. - - -## Pretrained Checkpoints - -| Model | APval | APtest | AP50 | SpeedGPU | FPSGPU || params | FLOPS | -|---------- |------ |------ |------ | -------- | ------| ------ |------ | :------: | -| [YOLOv5s](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J) | 36.6 | 36.6 | 55.8 | **2.1ms** | **476** || 7.5M | 13.2B -| [YOLOv5m](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J) | 43.4 | 43.4 | 62.4 | 3.0ms | 333 || 21.8M | 39.4B -| [YOLOv5l](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J) | 46.6 | 46.7 | 65.4 | 3.9ms | 256 || 47.8M | 88.1B -| [YOLOv5x](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J) | **48.4** | **48.4** | **66.9** | 6.1ms | 164 || 89.0M | 166.4B -| [YOLOv3-SPP](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J) | 45.6 | 45.5 | 65.2 | 4.5ms | 222 || 63.0M | 118.0B - - -** APtest denotes COCO [test-dev2017](http://cocodataset.org/#upload) server results, all other AP results in the table denote val2017 accuracy. -** All AP numbers are for single-model single-scale without ensemble or test-time augmentation. Reproduce by `python test.py --data coco.yaml --img 736 --conf 0.001` -** SpeedGPU measures end-to-end time per image averaged over 5000 COCO val2017 images using a GCP [n1-standard-16](https://cloud.google.com/compute/docs/machine-types#n1_standard_machine_types) instance with one V100 GPU, and includes image preprocessing, PyTorch FP16 image inference at --batch-size 32 --img-size 640, postprocessing and NMS. Average NMS time included in this chart is 1-2ms/img. Reproduce by `python test.py --data coco.yaml --img 640 --conf 0.1` -** All checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation). - - -## Requirements - -Python 3.7 or later with all `requirements.txt` dependencies installed, including `torch >= 1.5`. To install run: -```bash -$ pip install -U -r requirements.txt -``` - - -## Tutorials - -* [Notebook](https://github.com/ultralytics/yolov5/blob/master/tutorial.ipynb) Open In Colab -* [Kaggle](https://www.kaggle.com/ultralytics/yolov5) -* [Train Custom Data](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data) -* [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36) -* [ONNX and TorchScript Export](https://github.com/ultralytics/yolov5/issues/251) -* [Test-Time Augmentation (TTA)](https://github.com/ultralytics/yolov5/issues/303) -* [Google Cloud Quickstart](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart) -* [Docker Quickstart](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) ![Docker Pulls](https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker) - - -## Inference - -Inference can be run on most common media formats. Model [checkpoints](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J) are downloaded automatically if available. Results are saved to `./inference/output`. -```bash -$ python detect.py --source 0 # webcam - file.jpg # image - file.mp4 # video - path/ # directory - path/*.jpg # glob - rtsp://170.93.143.139/rtplive/470011e600ef003a004ee33696235daa # rtsp stream - http://112.50.243.8/PLTV/88888888/224/3221225900/1.m3u8 # http stream -``` - -To run inference on examples in the `./inference/images` folder: - -```bash -$ python detect.py --source ./inference/images/ --weights yolov5s.pt --conf 0.4 - -Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.4, device='', fourcc='mp4v', half=False, img_size=640, iou_thres=0.5, output='inference/output', save_txt=False, source='./inference/images/', view_img=False, weights='yolov5s.pt') -Using CUDA device0 _CudaDeviceProperties(name='Tesla P100-PCIE-16GB', total_memory=16280MB) - -Downloading https://drive.google.com/uc?export=download&id=1R5T6rIyy3lLwgFXNms8whc-387H0tMQO as yolov5s.pt... Done (2.6s) - -image 1/2 inference/images/bus.jpg: 640x512 3 persons, 1 buss, Done. (0.009s) -image 2/2 inference/images/zidane.jpg: 384x640 2 persons, 2 ties, Done. (0.009s) -Results saved to /content/yolov5/inference/output -``` - - - -## Reproduce Our Training - -Download [COCO](https://github.com/ultralytics/yolov5/blob/master/data/get_coco2017.sh), install [Apex](https://github.com/NVIDIA/apex) and run command below. Training times for YOLOv5s/m/l/x are 2/4/6/8 days on a single V100 (multi-GPU times faster). Use the largest `--batch-size` your GPU allows (batch sizes shown for 16 GB devices). -```bash -$ python train.py --data coco.yaml --cfg yolov5s.yaml --weights '' --batch-size 64 - yolov5m 48 - yolov5l 32 - yolov5x 16 -``` - - - -## Reproduce Our Environment - -YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled): - -- **Google Colab Notebook** with free GPU: Open In Colab -- **Kaggle Notebook** with free GPU: [https://www.kaggle.com/ultralytics/yolov5](https://www.kaggle.com/ultralytics/yolov5) -- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart) -- **Docker Image** https://hub.docker.com/r/ultralytics/yolov5. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) ![Docker Pulls](https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker) - - -## Citation - -[![DOI](https://zenodo.org/badge/264818686.svg)](https://zenodo.org/badge/latestdoi/264818686) - - -## About Us - -Ultralytics is a U.S.-based particle physics and AI startup with over 6 years of expertise supporting government, academic and business clients. We offer a wide range of vision AI services, spanning from simple expert advice up to delivery of fully customized, end-to-end production solutions, including: -- **Cloud-based AI** systems operating on **hundreds of HD video streams in realtime.** -- **Edge AI** integrated into custom iOS and Android apps for realtime **30 FPS video inference.** -- **Custom data training**, hyperparameter evolution, and model exportation to any destination. - -For business inquiries and professional support requests please visit us at https://www.ultralytics.com. - - -## Contact - -**Issues should be raised directly in the repository.** For business inquiries or professional support requests please visit https://www.ultralytics.com or email Glenn Jocher at glenn.jocher@ultralytics.com.