Related Objects of Interest: ==============================, the following pre-processing was applied to each image:, * auto-orientation of pixel data (with exif-orientation stripping), no image augmentation techniques were applied., * annotate, and create datasets, * collaborate with your team on computer vision projects, * collect & organize images, * export, train, and deploy computer vision models, * understand and search unstructured image data, * use active learning to improve your dataset over time
Top Yolo Datasets and Models
The datasets below can be used to train fine-tuned models for yolo detection. You can explore each dataset in your browser using Roboflow and export the dataset into one of many formats.
At the bottom of this page, we have guides on how to train a model using the yolo datasets below.
by HADJEM
1238 images 103 classes
* Auto-orientation of pixel data (with EXIF-orientation stripping) * Random brigthness adjustment of between -1 and +1 percent * Random rotation of between -1 and +1 degrees * Random shear of between -1° to +1° horizontally and -0° to +0° vertically * Resize to 640x640 (Stretch) * annotate, and create datasets * collaborate with your team on computer vision projects * collect & organize images * export, train, and deploy computer vision models * understand and search unstructured image data * use active learning to improve your dataset over time 22 23 24 25 26 27 28 29 30
by go4av05
8659 images 40 classes
* Random Gaussian blur of between 0 and 2.5 pixels * Random brigthness adjustment of between -25 and +25 percent * Random exposure adjustment of between -25 and +25 percent * Salt and pepper noise was applied to 5 percent of pixels * annotate, and create datasets * collaborate with your team on computer vision projects * collect & organize images * export, train, and deploy computer vision models * understand and search unstructured image data * use active learning to improve your dataset over time 17 18 19 20 21 22 23 24 25 26
181 images 3 classes
by Krish
376 images 7 classes
by josephin
58 images 13 classes
by recipeVision
1995 images 47 classes
* Auto-orientation of pixel data (with EXIF-orientation stripping) * Random rotation of between -15 and +15 degrees * Random rotation of between -37 and +37 degrees * Salt and pepper noise was applied to 5 percent of pixels * annotate, and create datasets * collaborate with your team on computer vision projects * collect & organize images * export, train, and deploy computer vision models * understand and search unstructured image data * use active learning to improve your dataset over time 22 23 24 25 26 27 28 29 30 31
880 images 11 classes
* Auto-orientation of pixel data (with EXIF-orientation stripping) * Resize to 416x416 (Stretch) 10 9 ============================== Ingredientsdataset are annotated in YOLO v5 PyTorch format. It includes 905 images. No image augmentation techniques were applied. The following pre-processing was applied to each image: This dataset was exported via roboflow.ai on May 30, 2021 at 10:39 AM GMT ingredients_dataset - v4 ingredients_data_meet_add
by sr
840 images 8 classes
* Auto-orientation of pixel data (with EXIF-orientation stripping) * Resize to 896x896 (Stretch) ============================== Fruits and Thumb detection - v5 v5 for yolov4 darknet Fruits-and-Thumb are annotated in YOLO v5 PyTorch format. It includes 859 images. The following pre-processing was applied to each image: This dataset was exported via roboflow.ai on April 15, 2022 at 7:08 AM GMT
by SnowWhite
200 images 85 classes
* Auto-orientation of pixel data (with EXIF-orientation stripping) * Resize to 160x160 (Stretch) * annotate, and create datasets * collaborate with your team on computer vision projects * collect & organize images * export, train, and deploy computer vision models * understand and search unstructured image data * use active learning to improve your dataset over time 01 03 04 05 07 09 19 20 21 22 23 24
8355 images 6 classes
by testing
680 images 8 classes
* Auto-orientation of pixel data (with EXIF-orientation stripping) * Resize to 640x640 (Stretch) ============================== Fruits and Thumb detection - v1 yolov5_v1 Fruits-and-Thumb are annotated in YOLO v5 PyTorch format. It includes 687 images. The following pre-processing was applied to each image: This dataset was exported via roboflow.ai on March 4, 2022 at 12:38 PM GMT
356 images 53 classes
* Auto-orientation of pixel data (with EXIF-orientation stripping) * Resize to 640x640 (Stretch) * annotate, and create datasets * collaborate with your team on computer vision projects * collect & organize images * export, train, and deploy computer vision models * understand and search unstructured image data * use active learning to improve your dataset over time 19 20 21 22 23 24 25 26 27 28 29 30
by yolov5
2587 images 10 classes
* Auto-orientation of pixel data (with EXIF-orientation stripping) * Resize to 640x640 (Stretch) 9 ============================== HanYang - v2 Yolov5 New It includes 2755 images. No image augmentation techniques were applied. The following pre-processing was applied to each image: This dataset was exported via roboflow.ai on March 30, 2022 at 5:53 AM GMT Tree are annotated in YOLO v5 PyTorch format.
by CV project
927 images 8 classes