# Solar Panel Research **URL:** \ **Category:** 🤝 Community Help\ **Created:** [September 17, 2026, 8:14pm UTC](https://discuss.roboflow.com/t/solar-panel-research/12487 "2026-09-17T20:14:49Z")\ **Posts on this page:** 2\ **Page:** 1
**Author:** ![Henry\_Sanchez](https://yyz1.discourse-cdn.com/flex029/user_avatar/discuss.roboflow.com/henry_sanchez/32/9870_2.png) [@Henry\_Sanchez](https://discuss.roboflow.com/u/Henry_Sanchez)\ **Post date:** [September 17, 2026, 8:14pm UTC](https://discuss.roboflow.com/t/solar-panel-research/12487/1 "2026-09-17T20:14:49Z")
- **Project Type:Solar Panel Research-using object detection images to train and detect Solar Panel defect of Bypass\_Diode, Thermal\_Hotspot, Cell\_Fault. Using Classification and YOLO to compare results. understanding annotations and comparing both models** - **Operating System & Browser: Using VS Code to clean data and Google Colab to Train AI models** - **Project Universe Link or Workspace:henrys-workspace-tv1in/Project ID:solar-panel- thermal- detection** - **Do you grant Roboflow Support permission to access your Workspace for troubleshooting? (Yes/No):Yes** I have a thermal solar-panel dataset collected from Roboflow Universe. The images contain three defect classes: `Bypass_Diode`, `Thermal_Hotspot`, and `Cell_Fault`. The datasets were originally created for **Object Detection** , so the images have bounding-box annotations. My research goal is to use these images to compare **YOLO Object Detection vs Multi-Label Image Classification** while keeping the experiment as fair as possible. I am trying to understand the correct workflow for preparing the same Object Detection dataset for both types of models. **1. YOLO:** Can I use the existing images and bounding-box annotations directly to train YOLO models? **2. Multi-Label Classification:** How should I convert the same Object Detection images into **full-image multi-label classification** without cropping the bounding boxes? For example, if an image has 3 `Cell_Fault` boxes and 1 `Thermal_Hotspot` box, I want the classification label for the whole image to be: `Bypass_Diode = 0, Cell_Fault = 1, Thermal_Hotspot = 1` **3. Same dataset:** What is the best way to make sure YOLO and the classification models use the **same source images and the same Train/Validation/Test split** , so the comparison is fair? **4. Composite images:** Some images contain multiple different defects. Besides full-image multi-label classification, what other methods should I consider for handling these composite images? For example, should I consider bounding-box cropping + classification, YOLO alone, or a YOLO + classifier pipeline? **5. Training outside Roboflow:** I plan to clean the dataset in VS Code and train models in Google Colab. What Roboflow export formats/workflow would you recommend for maintaining the annotations and labels correctly for both experiments? **6. What exactly am I seeing when I view an annotated image in Roboflow?** When I open an image in Roboflow, I see the original thermal image with colored bounding boxes and class names such as `Cell_Fault`, `Thermal_Hotspot`, or `Bypass_Diode`. Are the boxes and class names I am seeing the **ground-truth annotations created by the person who labeled the dataset** , or are they already an **output/prediction from an AI model**? I am trying to understand how YOLO actually “sees” this during training. Does the model receive: **Original image pixels + class ID + bounding-box coordinates** and the colored rectangles/class names shown on the Roboflow screen are simply a visual representation of those numerical annotations? If so, after I train YOLO, how can I view the **actual model output/predictions** and compare them with the original ground-truth annotations? My overall goal is to compare the approaches using performance, inference speed, model size, and their ability to handle multiple solar-panel defects. Eventually, I am interested in edge/drone deployment. I would appreciate any recommendations on how you would structure this experiment. ![Screenshot 2026-08-28 114545](https://canada1.discourse-cdn.com/flex029/uploads/roboflow1/original/2X/1/145f62275fa8e24514cef6a1f6a2131089a44edd.jpeg) ![Screenshot 2026-08-28 113940](https://canada1.discourse-cdn.com/flex029/uploads/roboflow1/original/2X/3/344945997fbb40eb743f04d630c22462e0054e57.jpeg) ![Screenshot 2026-08-28 091904](https://canada1.discourse-cdn.com/flex029/uploads/roboflow1/original/2X/9/9bc2d884a5f418324c1954dc6bfaa5a3e94e03b1.jpeg) ![Screenshot 2026-08-28 091726](https://canada1.discourse-cdn.com/flex029/uploads/roboflow1/original/2X/e/ee5498104721a56af8f060bdec129277f1236e1d.jpeg) ---
**Author:** ![system](https://canada1.discourse-cdn.com/flex029/uploads/roboflow1/original/1X/bb6edbfa56d0cf2ee603f061e9e09103502cc8cc.png) [@system](https://discuss.roboflow.com/u/system)\ **Post date:** [October 8, 2026, 8:15pm UTC](https://discuss.roboflow.com/t/solar-panel-research/12487/2 "2026-10-08T20:15:43Z")
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