Comments (6)
Hello! 😊 It seems you're missing an import statement for the Counter
class. You can fix this by adding the following line to the top of your script:
from collections import Counter
This imports the Counter
class from the Python collections
module, allowing you to count the objects per class as shown in the snippet. Happy coding! 🚀
from yolov5.
👋 Hello @tasyoooo, thank you for your interest in YOLOv5 🚀! Please visit our ⭐️ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution.
If this is a 🐛 Bug Report, please provide a minimum reproducible example to help us debug it.
If this is a custom training ❓ Question, please provide as much information as possible, including dataset image examples and training logs, and verify you are following our Tips for Best Training Results.
Requirements
Python>=3.8.0 with all requirements.txt installed including PyTorch>=1.8. To get started:
git clone https://github.com/ultralytics/yolov5 # clone
cd yolov5
pip install -r requirements.txt # install
Environments
YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):
- Notebooks with free GPU:
- Google Cloud Deep Learning VM. See GCP Quickstart Guide
- Amazon Deep Learning AMI. See AWS Quickstart Guide
- Docker Image. See Docker Quickstart Guide
Status
If this badge is green, all YOLOv5 GitHub Actions Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training, validation, inference, export and benchmarks on macOS, Windows, and Ubuntu every 24 hours and on every commit.
Introducing YOLOv8 🚀
We're excited to announce the launch of our latest state-of-the-art (SOTA) object detection model for 2023 - YOLOv8 🚀!
Designed to be fast, accurate, and easy to use, YOLOv8 is an ideal choice for a wide range of object detection, image segmentation and image classification tasks. With YOLOv8, you'll be able to quickly and accurately detect objects in real-time, streamline your workflows, and achieve new levels of accuracy in your projects.
Check out our YOLOv8 Docs for details and get started with:
pip install ultralytics
from yolov5.
Hello! 😊 To display the count of detected objects on the screen with YOLOv5, you can modify the code where detection bounding boxes are drawn. Here's a simple way to do it:
- After objects are detected and before the loop that goes through detections and draws boxes, calculate the count of objects by class.
- Use OpenCV's
cv2.putText()
to display the count on the frame.
Here's a basic snippet that you can add/modify in your detect.py
file after detection results are processed:
# Assuming 'pred' is the prediction result and 'names' are class names
for i, det in enumerate(pred): # detections per image
if len(det):
# Count objects by class
count_per_class = Counter(det[:, -1].int().tolist())
for class_id, count in count_per_class.items():
label = f"{names[class_id]}: {count}"
# Display on the top-left corner, you can change the position as needed
cv2.putText(im0, label, (10, 45 + 30 * class_id), cv2.FONT_HERSHEY_SIMPLEX,
1.25, (255,255,255), 3)
Be sure to adjust im0
(the frame you want to draw on), the position (10, 45 + 30 * class_id)
, the font size, and the color (255,255,255)
to fit your needs.
This will display a count of each detected class on the video frames. Happy coding! 🚀
from yolov5.
Hello! 😊 To display the count of detected objects on the screen with YOLOv5, you can modify the code where detection bounding boxes are drawn. Here's a simple way to do it:
- After objects are detected and before the loop that goes through detections and draws boxes, calculate the count of objects by class.
- Use OpenCV's
cv2.putText()
to display the count on the frame.Here's a basic snippet that you can add/modify in your
detect.py
file after detection results are processed:# Assuming 'pred' is the prediction result and 'names' are class names for i, det in enumerate(pred): # detections per image if len(det): # Count objects by class count_per_class = Counter(det[:, -1].int().tolist()) for class_id, count in count_per_class.items(): label = f"{names[class_id]}: {count}" # Display on the top-left corner, you can change the position as needed cv2.putText(im0, label, (10, 45 + 30 * class_id), cv2.FONT_HERSHEY_SIMPLEX, 1.25, (255,255,255), 3)Be sure to adjust
im0
(the frame you want to draw on), the position(10, 45 + 30 * class_id)
, the font size, and the color(255,255,255)
to fit your needs.This will display a count of each detected class on the video frames. Happy coding! 🚀
There is an error occurred:
count_per_class = Counter(det[:, -1].int().tolist())
NameError: name 'Counter' is not defined
from yolov5.
Hello! 😊 It seems you're missing an import statement for the
Counter
class. You can fix this by adding the following line to the top of your script:from collections import CounterThis imports the
Counter
class from the Pythoncollections
module, allowing you to count the objects per class as shown in the snippet. Happy coding! 🚀
it works now, thank you very much!
from yolov5.
@tasyoooo you're welcome! I'm glad it worked out for you. If you have any more questions or need further assistance, feel free to ask. Happy coding! 🚀
from yolov5.
Related Issues (20)
- Extract information of Coordinates HOT 2
- ModuleNotFoundError: No module named 'models.yolo' on custom trained weight. Is it a bug? HOT 6
- Why 'targets' values from train.py are different from the ground truth annotation in txt files? HOT 3
- A question between yolov5s-p5 and the parameters imgsz HOT 2
- YOLOv5 Output Size Issue HOT 2
- What Should Be the Output Size of Predictions for Object Detection? HOT 3
- stuck training on NVIDIA H100 HOT 2
- Problem running inference on YOLOv5s for custom trained model using Python HOT 2
- The confidence loss (obj_loss) scales of the training and validation sets are inconsistent. HOT 1
- Can I load YOLOv5 model without Ultralytics HOT 3
- The memory usage of the 0 card will increase until it is out of memory HOT 2
- scale_masks fucntion HOT 1
- cls loss HOT 1
- Problem with training for a single class HOT 4
- Issue when try to validate openvino format model HOT 3
- Is there a problem with the way I fine-tuned the YOLOv5? HOT 3
- No module named 'models' HOT 2
- Roc curve /part 2 HOT 1
- REQUIREMENTS.TXT FILE ERROR WITHIN YOLOV5 HOT 2
- Custom object detection by retaining the original classes of yolo HOT 5
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