Top It Datasets and Models
The datasets below can be used to train fine-tuned models for it 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 it datasets below.
by osmando
706 images 1342 classes
animal car drink mushroom rock wine glass 2 bottles on a rock next to fruit in front of orange background. 2 brown paper boxes in front of light brown background 2 cream bottles in front of blue tiles 2 macarons on a rock block in front of light orange background 3 black bottles next to yellow stairs and dry flower with shadow 3 blue bottles on blue plates next to dry nuts 3 bottles in front of light background with shadows 3 bottles in front of light brown background with shadows. 3 bottles in front of light gray background with shadows of a plant. 3 boxes in front of dark background 3 perfume bottles on reflective gray surface 4 paper boxes in front of light yellow background 4 potteries on a marble table 6 white bowls of spices on a wooden surface
120 images 5 classes
4417 images 302 classes
555 Sardines in Tomato Sauce 555 Tuna Afritada 555 Tuna Caldereta 7UP Absolute Pure Distilled Drinking Water Alaska Classic Sweetened Condensed Filled Milk Alaska Condensada Alaska Evaporada All Natural Seasons Tropical Fruit Angel All Purpose Creamer Apple Aqua Life Filtration Systems Argentina Corned Beef Argentina Giniling Argentina Meat Loaf Bear Brand Fortified Powdered Drink Milk Bear Brand Swak Pack Chocomilk Bell Pepper Bingo Double Choco Bingo Orange Cream
739 images 16 classes
by Fort1
5306 images 28 classes
head person player * Auto-orientation of pixel data (with EXIF-orientation stripping) * Resize to 416x416 (Stretch) * annotate, and create datasets * collaborate with your team on computer vision projects * collect & organize images * export, train, and deploy computer vision models * understand unstructured image data * use active learning to improve your dataset over time 0 1 16 18 19 20 ============================== Fortnite Player Tracker - v1 2024-02-10 10:30pm It includes 280 images.
by HibaB
3084 images 18 classes
10 11 12 13 14 7 8 9 ============================== BCCD - v3 raw Card-Types are annotated in YOLO v5 PyTorch format. It includes 8992 images. No image augmentation techniques were applied. The following pre-processing was applied to each image: This dataset was exported via roboflow This dataset was exported via roboflow.ai on February 24, 2021 at 10:05 AM GMT This dataset was exported via roboflow.ai on October 16, 2020 at 3:14 PM GMT Uno Cards - v2 raw
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 pcbdataset
1033 images 4547 classes
object "capacitor jumper" CJ1 "capacitor jumper" CJ2 "component text" " CC BE 10000000" "component text" "-309 LL6" "component text" "0 1 2 3 4 5 6 7" "component text" "0 N" "component text" "0001 5293 170A" "component text" "0123456789ABCDEF" "component text" "021 LDBM N389" "component text" "0821-1X1T-43-F 1402 WM" "component text" "0833 LTC2274 UJ BT267910" "component text" "085811 B4T EHCR" "component text" "1 2 3 4 5 6 7 8" "component text" "1 2 3 4" "component text" "1 2 3" "component text" "1 2" "component text" "100 25V UT" "component text" "100 50V UT" "component text" "100 6V"
1149 images 7961 classes
109 images 11 classes
1720 images 26 classes
* 50% probability of horizontal flip * Auto-orientation of pixel data (with EXIF-orientation stripping) * Random Gaussian blur of between 0 and 1.25 pixels * Random brigthness adjustment of between -25 and +25 percent * Random rotation of between -5 and +5 degrees * Random shear of between -5° to +5° horizontally and -5° to +5° vertically * Randomly crop between 0 and 20 percent of the image * Resize to 416x416 (Stretch) * annotate, and create datasets * collaborate with your team on computer vision projects * collect & organize images * export, train, and deploy computer vision models * understand unstructured image data * use active learning to improve your dataset over time 22 23 24 25 ============================== American Sign Language Letters - v1 v1
by dhruti
1720 images 26 classes
* 50% probability of horizontal flip * Auto-orientation of pixel data (with EXIF-orientation stripping) * Random Gaussian blur of between 0 and 1.25 pixels * Random brigthness adjustment of between -25 and +25 percent * Random rotation of between -5 and +5 degrees * Random shear of between -5° to +5° horizontally and -5° to +5° vertically * Randomly crop between 0 and 20 percent of the image * Resize to 416x416 (Stretch) 15 16 17 18 19 20 21 22 23 24 25 ==============================
by Amrutha
1720 images 26 classes
* 50% probability of horizontal flip * Auto-orientation of pixel data (with EXIF-orientation stripping) * Random Gaussian blur of between 0 and 1.25 pixels * Random brigthness adjustment of between -25 and +25 percent * Random rotation of between -5 and +5 degrees * Random shear of between -5° to +5° horizontally and -5° to +5° vertically * Randomly crop between 0 and 20 percent of the image * Resize to 416x416 (Stretch) * annotate, and create datasets * collaborate with your team on computer vision projects * collect & organize images * export, train, and deploy computer vision models * understand unstructured image data * use active learning to improve your dataset over time 22 23 24 25 ============================== American Sign Language Letters - v1 v1