# Class Detection Issue with YOLOv8 Model - Inconsistent Results Across Environments **URL:** \ **Category:** 🤝 Community Help\ **Tags:** bugs\ **Created:** [March 1, 2025, 1:14pm UTC](https://discuss.roboflow.com/t/class-detection-issue-with-yolov8-model-inconsistent-results-across-environments/8837 "2025-03-01T13:14:51Z")\ **Posts on this page:** 3\ **Page:** 1
**Author:** ![kozuki](https://avatars.discourse-cdn.com/v4/letter/k/8797f3/32.png) [@kozuki](https://discuss.roboflow.com/u/kozuki)\ **Post date:** [March 1, 2025, 1:14pm UTC](https://discuss.roboflow.com/t/class-detection-issue-with-yolov8-model-inconsistent-results-across-environments/8837/1 "2025-03-01T13:14:51Z")
Hello Roboflow Community, I’m facing a strange issue with my YOLOv8 model and need your expertise. **Context:** - **Model** : Custom YOLOv8 object detection model. - **Framework** : Ultralytics YOLO (integrated with Roboflow). - **Issue** : When running inference **locally (CPU)** or on a **VPS (no GPU)**, the model fails to detect certain classes/labels. However, the **same model works perfectly** : - In **Roboflow’s workflow** (web interface). - On **Google Colab** (GPU environment). **Example:** For the same image: - **Local/VPS Script Output** : Only detects `next_button2`. - **Roboflow/Colab Output** : Detects both `next_button2` and `email_field2`. **What I’ve Tried:** 1. Verified model weights and dataset consistency. 2. Tested identical images across all environments. 3. Simplified inference code (no custom post-processing). **Code Used:** **Google Colab (Works Fine):** ```auto from ultralytics import YOLO import requests from PIL import Image from io import BytesIO import matplotlib.pyplot as plt import os # Load YOLOv8 model best_weights = "runs/detect/model_connect2/weights/best.pt" trained_model = YOLO(best_weights) # Test image URL image_url = "" # Download and predict response = requests.get(image_url, headers={"User-Agent": "Mozilla/5.0"}) image = Image.open(BytesIO(response.content)).convert("RGB") results = trained_model(image) # Display results plt.imshow(results[0].plot()) plt.axis('off') plt.show() ``` **Output:** ```auto 0: 384x640 1 email_field2, 1 next_button2, 89.6ms Speed: 3.4ms preprocess, 89.6ms inference, 1.0ms postprocess per image at shape (1, 3, 384, 640) ``` **VPS Script (Fails to Detect `email_field2`):** ```auto import sys import json import os import contextlib from ultralytics import YOLO # Suppress logs @contextlib.contextmanager def suppress_stdout_stderr(): with open(os.devnull, 'w') as fnull: old_stdout, old_stderr = sys.stdout, sys.stderr sys.stdout, sys.stderr = fnull, fnull try: yield finally: sys.stdout, sys.stderr = old_stdout, old_stderr # Load model with suppress_stdout_stderr(): model = YOLO("/home/root1/yo/best.pt") # Run inference image_path = sys.argv[1] with suppress_stdout_stderr(): results = model(image_path) # Prepare JSON output detections = [] for result in results: for box in result.boxes: class_id = int(box.cls) class_name = model.names[class_id] x, y, w, h = box.xywh[0].tolist() detections.append({"label": class_name, "x": x, "y": y, "w": w, "h": h}) # Print JSON print(json.dumps(detections)) ``` **Output** : ```auto [{"label": "next_button2", "x": 1082.9072265625, "y": 592.21142578125, "w": 109.9459228515625, "h": 36.38958740234375}] ``` **Additional Details:** - **OS** : Windows (local), Ubuntu (VPS). - **Roboflow Workflow Link** : [Workflow](https://app.roboflow.com/workflows/embed/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJ3b3JrZmxvd0lkIjoiSGdidXRodTFoNUZQckE1bWo2TXYiLCJ3b3Jrc3BhY2VJZCI6IlEzU1paTU9aWVdkWmtUcjlWMVZBcURhWllwNDMiLCJ1c2VySWQiOiJRM1NaWk1PWllXZFprVHI5VjFWQXFEYVpZcDQzIiwiaWF0IjoxNzQwODMzMjc5fQ.TtKjEYgIYOfFHW5iUP2mrkDSJLcLzslcR0kxGNU6I8c) **Key Questions:** 1. Why does the model fail to detect `email_field2` on CPU (local/VPS) but works on GPU (Colab)? 2. Could preprocessing differences (e.g., resizing, normalization) cause this issue? 3. Are there known compatibility issues with YOLOv8 on CPU-only setups? - **Roboflow JSON Output** : [ { “count\_objects”: 2, “predictions”: { “image”: {“width”: 827, “height”: 465}, “predictions”: [ {“class”: “next\_button2”, “confidence”: 0.9457724690437317}, {“class”: “email\_field2”, “confidence”: 0.9373074173927307} ] } } ] Screenshots for Reference: All Test Images and Outputs: [Imgur: The magic of the Internet](https://imgur.com/a/0ikKyes) ---
**Author:** ![Grzegorz](https://yyz1.discourse-cdn.com/flex029/user_avatar/discuss.roboflow.com/grzegorz/32/2877_2.png) [@Grzegorz](https://discuss.roboflow.com/u/Grzegorz)\ **Post date:** [March 3, 2025, 12:21pm UTC](https://discuss.roboflow.com/t/class-detection-issue-with-yolov8-model-inconsistent-results-across-environments/8837/2 "2025-03-03T12:21:58Z")
Hi @kozuki , In VPS code sample you are inferring using `ultralytics`, and this script seems to be producing incorrect result. You also mentioned that workflows (through web interface) produced correct results. When weights are trained we produce two artifacts, the `pt` file and also `onnx` file (as export from `pt` file). Both `ultralytics` as well as `inference` packages should produce the same result. I’d encourage trying `inference` package to see if it will produce correct results on your VPS. Many thanks, Grzegorz ---
**Author:** ![system](https://canada1.discourse-cdn.com/flex029/uploads/roboflow1/original/1X/bb6edbfa56d0cf2ee603f061e9e09103502cc8cc.png) [@system](https://discuss.roboflow.com/u/system)\ **Post date:** [March 24, 2025, 12:22pm UTC](https://discuss.roboflow.com/t/class-detection-issue-with-yolov8-model-inconsistent-results-across-environments/8837/3 "2025-03-24T12:22:51Z")
This topic was automatically closed 21 days after the last reply. New replies are no longer allowed.