sleekmike/yolov5

YOLOv5 in PyTorch > ONNX > CoreML > iOS

 

** GPU Latency measures end-to-end latency per image averaged over 5000 COCO val2017 images using a V100 GPU with batch size 32, and includes image preprocessing, PyTorch FP32 inference, postprocessing and NMS.

Pretrained Checkpoints

Model	APval	APtest	AP50	LatencyGPU	FPSGPU		params	FLOPs
YOLOv5-s (ckpt)	35.5	35.5	55.0	2.5ms	400		7.1M	12.6B
YOLOv5-m (ckpt)	42.7	42.7	62.4	4.4ms	227		22.0M	39.0B
YOLOv5-l (ckpt)	45.7	45.9	65.1	6.8ms	147		50.3M	89.0B
YOLOv5-x (ckpt)	47.2	47.3	66.6	11.7ms	85		95.9M	170.3B
YOLOv3-SPP (ckpt)	45.6	45.5	65.2	7.9ms	127		63.0M	118.0B

** AP^test denotes COCO test-dev2017 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 --img 736 --conf 0.001
** Latency_GPU measures end-to-end latency per image averaged over 5000 COCO val2017 images using a GCP n1-standard-16 instance with one V100 GPU, and includes image preprocessing, PyTorch FP32 inference at batch size 32, postprocessing and NMS. Average NMS time included in this chart is 1-2ms/img. Reproduce by python test.py --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:

$ pip install -U -r requirements.txt

This repository contains code for this tutorial.

Tutorials

• Notebook
• Train Custom Data
• Google Cloud Quickstart Guide
• Docker Quickstart Guide

Inference

Inference can be run on most common media formats. Model checkpoints are downloaded automatically if available. Results are saved to ./inference/output. To run inference on examples in the ./inference/images folder:

$ python3 detect.py --source ./inference/images/test1.jpg  --weights weights/last.pt --conf 0.5

Output

Reproduce my Environment

To access an up-to-date working environment (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled), consider a:

• GCP Deep Learning VM with $300 free credit offer: See our GCP Quickstart Guide
• Google Colab Notebook with 12 hours of free GPU time.
• Docker Image https://hub.docker.com/r/ultralytics/yolov5. See Docker Quickstart Guide

Citation