Issue with fps in yolov8m-pose

[Utsav-2301]

I've been using yolov8m-pose just by downloading the weights and it runs at a good fps value of around 12 - 15 but when I cloned the ultralytics repository and ran the same code, I'm getting an fps of around 2 - 3 and I don't know the reason why this is happening, the code is same, python environment has same dependencies, model is same then why different fps values.

[glenn-jocher]

@Utsav-2301 hi there!

Thank you for bringing this to our attention. The discrepancy in FPS values you're experiencing could be due to several factors. Here are a few steps to help diagnose and potentially resolve the issue:

1. Reproducible Example: Could you please provide a minimum reproducible example of your code? This will help us better understand the context and pinpoint the issue. You can refer to our guide on creating a reproducible example here.

2. Environment Consistency: Ensure that both environments are indeed identical. Sometimes subtle differences in package versions can lead to performance variations. You can verify the versions of key packages like torch, ultralytics, and others.

3. Latest Versions: Make sure you are using the latest versions of the Ultralytics repository and dependencies. Performance improvements and bug fixes are continually being made, so it's crucial to stay updated.

4. Hardware Utilization: Check if there are any differences in hardware utilization between the two setups. For instance, ensure that GPU acceleration is being utilized in both cases.

5. Profiling: You might want to profile your code to see where the bottleneck lies. Tools like cProfile in Python can be very helpful for this.

Here's a quick example of how you might profile your code:

import cProfile
import pstats
from ultralytics import YOLO

def run_model():
    model = YOLO('yolov8m-pose.pt')
    results = model.predict(source='path/to/your/video.mp4', show=True)

cProfile.run('run_model()', 'profile_stats')
p = pstats.Stats('profile_stats')
p.sort_stats('cumulative').print_stats(10)

This will give you an idea of where the time is being spent in your code.

Please try these steps and let us know if the issue persists. We're here to help!

[glenn-jocher]

Hi @Utsav-2301,

Thank you for your patience and for providing detailed information. Given that you've confirmed GPU utilization, consistent environments, and the latest versions, let's delve deeper into potential causes.

Next Steps

1. Detailed Profiling:
   Since the profiler results indicate the results loop is taking more time, let's focus on optimizing this section. You can use line_profiler to get more granular insights.

2. Model Inference Optimization:
   Ensure that the model inference is optimized. Sometimes, minor tweaks in how the model is called can make a significant difference.

   # Ensure the model is loaded and moved to GPU correctly
   model = YOLO('yolov8m-pose.pt').to('cuda:0')
   
   # Profile the inference call
   import line_profiler
   profile = line_profiler.LineProfiler()
   
   @profile
   def run_inference(frame):
       return model.track(frame, classes=0, show=True, stream=True, persist=True, tracker='bytetrack.yaml', conf=0.6)
   
   # Call the profiling function
   results = run_inference(frame)
   profile.print_stats()

3. Batch Processing:
   If feasible, try processing frames in batches rather than individually. This can reduce the overhead of multiple inference calls.

   # Example of batch processing
   frames = [frame1, frame2, frame3]  # List of frames to process
   results = model.track(frames, classes=0, show=True, stream=True, persist=True, tracker='bytetrack.yaml', conf=0.6)

4. Compare with Minimal Example:
   Create a minimal reproducible example to isolate the issue. This can help identify if the problem lies within specific parts of your code.

   from ultralytics import YOLO
   import cv2
   
   model = YOLO('yolov8m-pose.pt').to('cuda:0')
   cap = cv2.VideoCapture('path/to/video.mp4')
   
   while cap.isOpened():
       ret, frame = cap.read()
       if not ret:
           break
       results = model.track(frame, classes=0, show=True, stream=True, persist=True, tracker='bytetrack.yaml', conf=0.6)
       # Display FPS or other metrics here
   cap.release()

Conclusion

If the issue persists, please provide a minimal reproducible example as it will help us diagnose the problem more effectively. You can refer to our guide on creating a reproducible example here.

Thank you for your cooperation, and we're here to help you resolve this! 🚀

[Utsav-2301]

Hi,
I tested on a new device so as to eliminate the earlier config setup issues, one thing I noticed is that the latest version of Ultralytics is 8.2.54 and with that the code processes the video file slowly, with ultralytics 8.1.19, that I was using earlier I got better results instantly just after changing the version of the package. So probably it might be an issue with the latest version of ultralytics itself. I'm not sure about it but I think it could be one of the issues.

[glenn-jocher]

Hi @Utsav-2301,

Thank you for your detailed observations and for testing on a new device. It's great to hear that you've identified a potential version-related issue.

To help us investigate further, could you please provide a minimum reproducible example? This will allow us to replicate the issue on our end and pinpoint the cause more effectively. You can refer to our guide on creating a reproducible example here.

Additionally, please ensure that all dependencies are up-to-date and consistent with the version you're testing. Sometimes, subtle differences in dependencies can lead to performance variations.

We appreciate your patience and cooperation in resolving this! 🚀

[Utsav-2301]

sure, here's the sample code and the requirements that I used along with and without the repository:
requirements_with_repo.txt
requirements_without_repo.txt

from ultralytics import YOLO
import cv2

model = YOLO('yolov8m-pose.pt').to('cuda:0')
cap = cv2.VideoCapture('Exit_ac_0vFKhKjs4Tso.mp4')

while cap.isOpened():
    ret, frame = cap.read()
    if not ret:
        break
    results = model.track(frame,classes=0,save=True,show=True,stream=True,persist=True,conf=0.6,tracker='bytetrack.yaml')
    for result in results:
        print("")

cap.release()
cv2.destroyAllWindows()

[glenn-jocher]

Hi @Utsav-2301,

Thank you for providing the sample code and requirements files. It's very helpful for diagnosing the issue.

Given your observations about the performance difference between Ultralytics versions 8.2.54 and 8.1.19, it's possible that recent changes may have impacted performance. We appreciate your patience as we investigate this further.

In the meantime, here are a few steps you can take:

1. Reproducible Example: Your provided code is a great start. If you can, please include any additional details or specific configurations that might be relevant. This helps us replicate the issue more accurately.

2. Profiling: You can use line_profiler to pinpoint where the slowdown occurs. This can provide insights into which part of the code is affected by the version change.

3. Environment Consistency: Ensure that all dependencies are consistent across versions. Sometimes, minor differences in package versions can lead to performance variations.

4. Latest Updates: If you haven't already, please verify that the issue persists with the latest versions of all relevant packages.

Here's a quick profiling example to help identify the bottleneck:

import line_profiler
profile = line_profiler.LineProfiler()

@profile
def run_inference(frame):
    return model.track(frame, classes=0, show=True, stream=True, persist=True, tracker='bytetrack.yaml', conf=0.6)

# Call the profiling function
results = run_inference(frame)
profile.print_stats()

We appreciate your cooperation and will keep you updated on our findings. If you have any further insights or observations, please feel free to share. 🚀