** 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.
| 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 |
** APtest 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
** LatencyGPU 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).
Python 3.7 or later with all requirements.txt dependencies installed, including torch >= 1.5. To install run:
$ pip install -U -r requirements.txtThis repository contains code for this tutorial.
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
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
