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- dataconvert/README.dataset.txt +31 -0
- dataconvert/README.roboflow.txt +27 -0
- dataconvert/test/5_jpg.rf.56ba1c295d0fe12d5d3834a568ce3365.jpg +3 -0
- dataconvert/test/9_jpg.rf.22820f0dee5cca287d4840953d963830.jpg +3 -0
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- dataconvert/test/DJI_0026_R_JPG_jpg.rf.c2ac2862f1379eb3b66e63badd6d3e25.jpg +3 -0
- dataconvert/test/DJI_0032_R_JPG_jpg.rf.d7db83ce5d5783f915bca880cbdc7915.jpg +3 -0
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- dataconvert/test/DJI_0112_R_JPG_jpg.rf.b86e87205042a0b2e577780fac02fb93.jpg +3 -0
- dataconvert/test/DJI_0116_R_JPG_jpg.rf.4e2119ea1b6b1a571d3db6368338604e.jpg +3 -0
- dataconvert/test/DJI_0146_R_JPG_jpg.rf.f9576e8b60d660123879f2273a3ae56d.jpg +3 -0
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- dataconvert/test/DJI_0208_R_JPG_jpg.rf.5bd7795d6662ba78069f698c89923f46.jpg +3 -0
- dataconvert/test/DJI_0220_R_JPG_jpg.rf.6ebc1cfbeb6152c43bc34c94c6ca764e.jpg +3 -0
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- dataconvert/test/DJI_0238_R_JPG_jpg.rf.456fcc0fefead84f655462bd22d67438.jpg +3 -0
- dataconvert/test/DJI_0238_R_JPG_jpg.rf.db9ad72e7c34e890eaff8d1f9e2766db.jpg +3 -0
- dataconvert/test/_annotations.coco.json +1 -0
- dataconvert/train/10_jpg.rf.c548d10e355d2cd23b4e5683282630bd.jpg +3 -0
- dataconvert/train/1_jpg.rf.3108f176065cdf1cfc4b1dc2985a6e54.jpg +3 -0
- dataconvert/train/5_jpg.rf.309cf0d9efabd30173a184bb58d128c3.jpg +3 -0
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- dataconvert/train/9_jpg.rf.8fe6fe7aff260e553d90ef442563cdde.jpg +3 -0
- dataconvert/train/DJI_0002_R_JPG_jpg.rf.0e3641ced062e437f62c9d24a3ec9fa0.jpg +3 -0
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- dataconvert/train/DJI_0010_R_JPG_jpg.rf.0a4062a01645efec1df40a180f70203c.jpg +3 -0
- dataconvert/train/DJI_0010_R_JPG_jpg.rf.f07bc4512307570ff419d60ff59e20f1.jpg +3 -0
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- dataconvert/train/DJI_0020_R_JPG_jpg.rf.abdaeb2f8701c6ec51a6f4b532829d4a.jpg +3 -0
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dataconvert/README.dataset.txt
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# dataconvert-9e6zr-lstqv-hiro > 2025-03-13 2:52pm
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https://universe.roboflow.com/rf-100-vl/dataconvert-9e6zr-lstqv-hiro
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Provided by a Roboflow user
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License: MIT
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# Overview
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- [Introduction](#introduction)
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- [Object Classes](#object-classes)
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- [Normal Panel](#normal-panel)
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# Introduction
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This dataset is designed to annotate solar panels, specifically identifying "Normal Panel" in thermal images. The task focuses on effectively enclosing these panels with bounding boxes to facilitate automated analysis. The dataset consists of one class:
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- **Normal Panel**: Typical solar panels observed in thermal imaging with distinct grid patterns.
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# Object Classes
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## Normal Panel
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### Description
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Normal Panels are solar panels visible in thermal images. They are identified by their rectangular shape and distinct grid-like patterns that are consistent across different images. These panels may appear in varying orientations and should be recognized based on their thermal signature with visible grid lines.
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### Instructions
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- **Annotation Boundary**: Draw bounding boxes to tightly enclose the entire structure of the solar panel. Include the full rectangular form even if part of the panel is at the edge of the image.
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- **Grid Pattern**: Ensure the box covers the main grid-like pattern visible on the panel’s surface.
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- **Panel Texture**: The thermal signature should be continuous across the panel.
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- **Clarity**: Do not annotate areas where the structure is too blurry to ascertain the presence of a Normal Panel.
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- **Minimal Visibility**: Panels that are evident through small sections but indiscernible as a whole should not be annotated. Only annotate if the general structure of the panel is clear.
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By following these instructions, annotators will ensure the consistent identification of Normal Panels across all images in the dataset.
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dataconvert/README.roboflow.txt
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dataconvert-9e6zr-lstqv-hiro - v2 2025-03-13 2:52pm
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==============================
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This dataset was exported via roboflow.com on March 14, 2025 at 3:03 AM GMT
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Roboflow is an end-to-end computer vision platform that helps you
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* collaborate with your team on computer vision projects
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* collect & organize images
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* understand and search unstructured image data
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* annotate, and create datasets
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* export, train, and deploy computer vision models
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* use active learning to improve your dataset over time
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For state of the art Computer Vision training notebooks you can use with this dataset,
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visit https://github.com/roboflow/notebooks
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To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com
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The dataset includes 179 images.
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Dataconvert-9e6zr-lstqv-hiro are annotated in COCO format.
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The following pre-processing was applied to each image:
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No image augmentation techniques were applied.
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dataconvert/test/_annotations.coco.json
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{"info": {"year": "2025", "version": "2", "description": "Exported from roboflow.com", "contributor": "", "url": "https://public.roboflow.com/object-detection/undefined", "date_created": "2025-03-14T03:03:19+00:00"}, "licenses": [{"id": 1, "url": "https://choosealicense.com/licenses/mit/", "name": "MIT"}], "categories": [{"id": 0, "name": "Normal Panel", "supercategory": "dataconvert-9e6zr-lstqv-hiro"}], "images": [{"id": 0, "license": 1, "file_name": "DJI_0238_R_JPG_jpg.rf.456fcc0fefead84f655462bd22d67438.jpg", "height": 416, "width": 416, "date_captured": "2025-03-14T03:03:19+00:00", "extra": {"name": "DJI_0238_R_JPG_jpg"}}, {"id": 1, "license": 1, "file_name": "DJI_0172_R_JPG_jpg.rf.31be46a07305030693bec16371b0f1e3.jpg", "height": 416, "width": 416, "date_captured": "2025-03-14T03:03:19+00:00", "extra": {"name": "DJI_0172_R_JPG_jpg"}}, {"id": 2, "license": 1, "file_name": "DJI_0032_R_JPG_jpg.rf.d7db83ce5d5783f915bca880cbdc7915.jpg", "height": 416, "width": 416, "date_captured": 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