The recommended way to load the labels is to use the pandas Python package:

import pandas as pd

labels = pd.read_pickle("labels.pkl")

See github repository for more information: https://github.com/wmcnally/deep-darts

The directory scenes/ contains the videos in mp4 format with actions of interest performed in context of other players present in the scene. The files are arranged in subdirectories according to the action class of the action of interest. The directory actions/ contains the videos of performances of actions by single players isolated from the videos in scenes directory. The files are arranged in subdirectories according to the performed action class. Files are named so that the beginning of the name matches the original video from which the action is extracted. The directory player_detections/ contains the object detections for each frame in the videos.

Basil/Tulsi Plant is harvested in India because of some spiritual facts behind this plant,this plant is used for essential oil and pharmaceutical purpose. There are two types of Basil plants cultivated in India as Krushna Tulsi/Black Tulsi and Ram Tulsi/Green Tulsi.

Many of the investigator working on disease detection in Basil leaves where the following diseases occur

 1) Gray Mold

2) Basal Root Rot, Damping Off

 3) Fusarium Wilt and Crown Rot

4) Leaf Spot

5) Downy Mildew

The Quality parameters (Healthy/Diseased) and also classification based on the texture and color of leaves. For the object detection purpose researcher using an algorithm like Yolo,  TensorFlow, OpenCV, deep learning, CNN

I had collected a dataset from the region Amravati, Pune, Nagpur Maharashtra state the format of the images is in .jpg.

If you use these provided data in a publication or a scientific paper, please cite the dataset accordingly.

This data set contains two separate sets of annotated images. Common features of the image sets:

• Images are saved in JPG format
• Annotations are made with labelImg
• Both sets contain videos in MP4 format to test trained detection models

Set 1

166 annotated images with 1028 objects of the following 13 classes:

1. cereal
2. chocolate_milk
3. heineken
4. iron_man
5. medicine
6. milk_bottle
7. milk_box
8. monster
9. purple_juice
10. red_juice
11. shampoo
12. tea_box
13. yellow_juice

There are also 28 videos for testing, shot with multiple smartphones.

Set 2

388 annotated images with 1737 objects of the following 20 classes:

1. apple
2. basket
3. cereal
4. chocolate_drink
5. cloth_opl
6. coke
7. crackers
8. grape_juice
9. help_me_carry_opl
10. noodles
11. orange
12. orange_juice
13. paprika
14. potato_chips
15. pringles
16. sausages
17. scrubby
18. sponge_opl
19. sprite
20. tray

There is also a single long video and 398 unannotated images for testing.

About 20 minutes of recorded material from the clear weather scenario, 13 minutes from the fog scenario, and about 15 minutes from rainy weather were processed. The longer videos were cut into sequences and from these sequences individual frames were extracted, resulting in 11,900 images for the clear weather, 4,905 images for the fog, and 7,030 images for the rainy weather scenarios.

A total of 6,111 frames were manual annotated so that could be used to train the supervised model for person detection. When selecting the frames, it was taken into account that the selected frames include different weather conditions so that in the set there were 2,663 frames shot in clear weather conditions, 1,135 frames of fog, and 2,313 frames of rain.

The annotations were made using the open-source Yolo BBox Annotation Tool that can simultaneously store annotations in the three most popular machine learning annotation formats YOLO, VOC, and MS COCO so all three annotation formats are available. The image annotation consists of a centroid position of the bounding box around each object of interest, size of the bounding box in terms of width and height, and corresponding class label (Human or Dog).