This is a fork of ultralytics' YOLOv3 repo, used for a project on pedestrian detection.

Pretrained Checkpoints

Model	size (pixels)	mAPval 0.5:0.95	mAPtest 0.5:0.95	mAPval 0.5	Speed V100 (ms)		params (M)	FLOPS 640 (B)
YOLOv3-tiny	640	17.6	17.6	34.8	1.2		8.8	13.2
YOLOv3	640	43.3	43.3	63.0	4.1		61.9	156.3
YOLOv3-SPP	640	44.3	44.3	64.6	4.1		63.0	157.1
YOLOv5l	640	48.2	48.2	66.9	3.7		47.0	115.4

Table Notes (click to expand)

• AP^test denotes COCO test-dev2017 server results, all other AP results denote val2017 accuracy.
• AP values are for single-model single-scale unless otherwise noted. Reproduce mAP by python test.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65
• Speed_GPU averaged over 5000 COCO val2017 images using a GCP n1-standard-16 V100 instance, and includes FP16 inference, postprocessing and NMS. Reproduce speed by python test.py --data coco.yaml --img 640 --conf 0.25 --iou 0.45
• All checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation).

Requirements

pip install PyYAML==5.4.1 wandb

Inference

detect.py runs inference on a variety of sources, downloading models automatically from the latest YOLOv3 release and saving results to runs/detect.

$ python detect.py --source 0  # webcam
                            file.jpg  # image 
                            file.mp4  # video
                            path/  # directory
                            path/*.jpg  # glob
                            'https://youtu.be/NUsoVlDFqZg'  # YouTube video
                            'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream

To run inference on example images in data/images:

$ python detect.py --source data/images --weights yolov3.pt --conf 0.25

Model

model = torch.hub.load('ultralytics/yolov3', 'yolov3') # or 'yolov3_spp', 'yolov3_tiny'

Dataset

Run get_citypersons.sh Then, run make_citypersons_labels.py to generate label .txt files

Image

img = 'https://ultralytics.com/images/zidane.jpg'

Inference

results = model(img) results.print() # or .show(), .save()

## Training

Run commands below to reproduce results on [COCO](https://github.com/ultralytics/yolov3/blob/master/data/scripts/get_coco.sh) dataset (dataset auto-downloads on first use). Training times for YOLOv3/YOLOv3-SPP/YOLOv3-tiny are 6/6/2 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 yolov3.yaml      --weights '' --batch-size 24
                                         yolov3-spp.yaml                            24
                                         yolov3-tiny.yaml                           64

Citation

Todo

• Create test set

• Run baseline model

• Run experiments