How to know what is the detection square values?

[AvishayDev]

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Question

Hello,
Thanks G-d i success to build a working object detector as i need.
however i need more information about the detection that the model found..

i run this command to detect-
!python detect.py --weights runs/train/exp/weights/best.pt --img 640 --conf 0.1 --source {dataset.location}/test/images

and i try this script too-
model = torch.hub.load('/content/yolov5', 'custom', path='/content/yolov5/runs/train/exp/weights/best.pt', force_reload=True, source='local')
img = '/content/datasets/final-hebrew-1/test/images/test.jpeg'
results = model(img)
results.print()
=> image 1/1: 640x640 1 Aleph
Speed: 23.9ms pre-process, 87.7ms inference, 2.6ms NMS per image at shape (1, 3, 640, 640)

i cant understand from this infoemation were the detection was in my image..
please help!!
Thanks

Additional

No response

[glenn-jocher]

@AvishayDev

Simple Example

This example loads a pretrained YOLOv5s model from PyTorch Hub as model and passes an image for inference. 'yolov5s' is the lightest and fastest YOLOv5 model. For details on all available models please see the README.

import torch

# Model
model = torch.hub.load('ultralytics/yolov5', 'yolov5s')

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

# Inference
results = model(img)

results.pandas().xyxy[0]
#      xmin    ymin    xmax   ymax  confidence  class    name
# 0  749.50   43.50  1148.0  704.5    0.874023      0  person
# 1  433.50  433.50   517.5  714.5    0.687988     27     tie
# 2  114.75  195.75  1095.0  708.0    0.624512      0  person
# 3  986.00  304.00  1028.0  420.0    0.286865     27     tie

See YOLOv5 PyTorch Hub tutorial for details:

YOLOv5 Tutorials

• Train Custom Data  🚀 RECOMMENDED
• Tips for Best Training Results  ☘️ RECOMMENDED
• Weights & Biases Logging  🌟 NEW
• Supervisely Ecosystem  🌟 NEW
• Multi-GPU Training
• PyTorch Hub  ⭐ NEW
• TFLite, ONNX, CoreML, TensorRT Export 🚀
• Test-Time Augmentation (TTA)
• Model Ensembling
• Model Pruning/Sparsity
• Hyperparameter Evolution
• Transfer Learning with Frozen Layers  ⭐ NEW
• TensorRT Deployment

Good luck 🍀 and let us know if you have any other questions!

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