Comments (2)
π Hello @FazanAfzal, thank you for your interest in Ultralytics YOLOv8 π! We recommend a visit to the Docs for new users where you can find many Python and CLI usage examples and where many of the most common questions may already be answered.
If this is a π Bug Report, please provide a minimum reproducible example to help us debug it.
If this is a custom training β 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.
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Install
Pip install the ultralytics
package including all requirements in a Python>=3.8 environment with PyTorch>=1.8.
pip install ultralytics
Environments
YOLOv8 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 Ultralytics CI tests are currently passing. CI tests verify correct operation of all YOLOv8 Modes and Tasks on macOS, Windows, and Ubuntu every 24 hours and on every commit.
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@FazanAfzal hello! Combining a YOLOv7 segmentation model with a YOLOv8 detection model directly within the same framework is not supported out-of-the-box. Each version of YOLO (v7 and v8) operates independently with its own architectural design and functionalities.
However, you can integrate the outputs of both models in your application logic. Simply run each model separately on your input data, then merge or analyze their outputs according to your requirements. Here is a very basic Python example to illustrate how you might approach this:
from ultralytics import YOLO
# Load your trained models
model_v7 = YOLO('path/to/yolov7_segmentation_model.pt')
model_v8 = YOLO('path/to/yolov8_detection_model.pt')
# Process the same image with both models
img = 'path/to/image.jpg'
segmentation_results = model_v7(img)
detection_results = model_v8(img)
# Now, you can combine the results from segmentation_results and detection_results as needed
Remember, the actual integration and usage might depend on what exactly you need from these combined results. This approach allows using the specific strengths of both models while maintaining flexibility in processing and outcome.
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Related Issues (20)
- How to run YOLOv8 .engine but only need to obtain the output of the model in Jetson Xavier NX? HOT 2
- Why is yolov5n better than yolov8n in my own dataset (e.g., P, R, MAP50, etc.)οΌeven in coco128 HOT 2
- About loading model weights HOT 4
- I'm also having the same problem, I use botsort to track the target to detect the target but can't, the confidence level is also very high, the threshold setting should be fine. How to solve it, I thought it was a confidence threshold, but I can't debug it HOT 2
- yolov8 keypoint detection results HOT 2
- can anyone guide to do quantization for custom trained yolov8 HOT 4
- Implement Resnet Backbone into YOLOv3 and YOLOv5 HOT 3
- Is there any implementation of yolov8 in cpp using tensorflow lite? HOT 3
- Angle representation method of YOLOV8-obb HOT 1
- Ultralytics not working on Jetpack 6.0? HOT 3
- The YOLOv8 segmentation model with batching option doesn't run on the GPU ? HOT 1
- yolov8n's coco pre-training? HOT 2
- Can not extract predicted classes from the results as a variable. HOT 2
- Changing the Yolov8-OBB head to output Polygonal Bounding Box with Four Corners instead of Oriented Bounding Box HOT 15
- annotation type HOT 5
- How to improve the accuracy of yolov8-obb in detecting large targets HOT 2
- The parameters printed through model.info are different from those printed through the val command HOT 1
- YOLOv8 Training Your Own Dataset HOT 2
- Image prediction HOT 5
- Train the selected model structure for your dataset HOT 3
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