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Top Flower Computer Vision Models
The models below have been fine-tuned for various flower detection tasks. You can try out each model in your browser, or test an edge deployment solution (i.e. to an NVIDIA Jetson). You can use the datasets associated with the models below as a starting point for building your own flower detection model.
At the bottom of this page, we have guides on how to count flowers in images and videos.
252 images1 model
706 images1 model
animalcardrinkmushroomrockwine glass2 bottles on a rock next to fruit in front of orange background.2 brown paper boxes in front of light brown background2 cream bottles in front of blue tiles2 macarons on a rock block in front of light orange background3 black bottles next to yellow stairs and dry flower with shadow3 blue bottles on blue plates next to dry nuts3 bottles in front of light background with shadows3 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 background3 perfume bottles on reflective gray surface4 paper boxes in front of light yellow background4 potteries on a marble table6 white bowls of spices on a wooden surface
3124 images1 model
Aegiceras Corniculatum FlowerAegiceras Corniculatum LeavesAegiceras Corniculatum StemAvecennia Marina FruitAvecennia Marina LeavesAvecennia Marina StemLoop-Root LeavesLumnitzera Racemosa FlowerLumnitzera Racemosa LeavesLumnitzera Racemosa StemNypa FruitNypa LeavesNypa StemRhizophora Apiculata FlowerRhizophora Apiculata LeavesRhizophora Apiculata RootRhizophora Mucronata FlowerRhizophora Mucronata FruitRhizophora Mucronata LeavesRhizophora Mucronata Root
2352 images2 models
Aloe-veraAlthaea-Rosea-Hollyhock-Anglica-Gigas-korean-rootsAnti aging part of Brassica- FlowersAnti-aging part of camellia-sinensis: LEAVESAronia-Melanocarpa-Black-Chokeberry-Artemisia-Capillaris-Seepweed-Astragalus-Membranaceus-Astragalus-Azalea-Rhododendron-yedoense-f.-poukhanense-H.-Le-v.-M.-Sugim.Brassica NapusCamellia-Sinensis-Green-Tea-Camellia-japonicaCarpinus-tschonoskii-Maxim.Castanea-Crenata-Japanese-Chestnut-Cinnamomum-Cassia-Cinnamon-Citrus-Unshiu-Jeju-Hallabong-Dioscorea-Opposita-Chinese-Yam-Diospyros-kaki-ThunbEquisetum-Arvense-HorsetailEucommia-ulmoides-Oliv
Guide: How to Count Flowers with Computer Vision
With a model hosted on Roboflow like the ones above and the open source supervision Python package, you can count flowers in your images and videos.
The following code snippet counts the number of flowers present in an image.
To use the snippet below, you will need to run pip install roboflow supervision. Replace the project name and model name with any model trained on Universe, such as those listed above.
import supervision as sv
import roboflow
roboflow.login()
rf = roboflow.Roboflow()
# replace with the flower project you choose above
project = rf.workspace("roboflow-100").project("cloud-types")
flower_model = project.version(2).model
results = flower_model.predict("flower.jpg").json()
flowers = sv.Detections.from_roboflow(results)
# print number of flowers
print(len(flowers))Guide: How to Count Flowers in a Zone
With a bit more code, you can count the number of flower present in a specific zone of your image or video.
The following code snippet counts the number of flower present in each frame in a video.
To use the snippet below, you will need to run pip install roboflow supervision. Replace the project name and model name with any model trained on Universe, such as those listed above.
Read our blog post on counting objects in a zone
import numpy as np
import supervision as sv
import roboflow
SOURCE_VIDEO_PATH = "flower.mp4"
TARGET_VIDEO_PATH = "flower_out.mp4"
# use https://roboflow.github.io/polygonzone/ to get the points for your shape
polygon = np.array([
# draw 50x50 box in top left corner
[0, 0],
[50, 0],
[50, 50],
[0, 50]
])
roboflow.login()
rf = roboflow.Roboflow()
# replace with the flower project you choose above
project = rf.workspace("roboflow-100").project("cloud-types")
flower_model = project.version(2).model
# create BYTETracker instance
flower_tracker = sv.ByteTrack(track_thresh=0.25, track_buffer=30, match_thresh=0.8, frame_rate=30)
# create VideoInfo instance
video_info = sv.VideoInfo.from_video_path(SOURCE_VIDEO_PATH)
# create frame generator
generator = sv.get_video_frames_generator(SOURCE_VIDEO_PATH)
# create PolygonZone instance
zone = sv.PolygonZone(polygon=polygon, frame_resolution_wh=(video_info.width, video_info.height))
# create box annotator
box_annotator = sv.BoxAnnotator(thickness=4, text_thickness=4, text_scale=2)
colors = sv.ColorPalette.default()
# create instance of BoxAnnotator
zone_annotator = sv.PolygonZoneAnnotator(thickness=4, text_thickness=4, text_scale=2, zone=zone, color=colors.colors[0])
# define call back function to be used in video processing
def callback(frame: np.ndarray, index:int) -> np.ndarray:
# model prediction on single frame and conversion to supervision Detections
results = flower_model.predict(frame).json()
flowers = sv.Detections.from_roboflow(results)
# show flower detections in real time
print(flowers)
# tracking flower detections
flowers = flower_tracker.update_with_detections(flowers)
annotated_frame = box_annotator.annotate(scene=frame, detections=flowers)
annotated_frame = zone_annotator.annotate(scene=annotated_frame)
# return frame with box and line annotated result
return annotated_frame
# process the whole video
sv.process_video(
source_path = SOURCE_VIDEO_PATH,
target_path = TARGET_VIDEO_PATH,
callback=callback
)Guide: How to Track Flowers Crossing a Line
You can count how many flowers have crossed a line using the supervision LineCounter method.
The following code snippet counts the number of flowers that cross a line in a video.
To use the snippet below, you will need to run pip install roboflow supervision. Replace the project name and model name with any model trained on Universe, such as those listed above.
import numpy as np
import supervision as sv
import roboflow
SOURCE_VIDEO_PATH = "flower.mp4"
TARGET_VIDEO_PATH = "flower_out.mp4"
# use https://roboflow.github.io/polygonzone/ to get the points for your line
LINE_START = sv.Point(0, 300)
LINE_END = sv.Point(800, 300)
roboflow.login()
rf = roboflow.Roboflow()
# replace with the flower project you choose above
project = rf.workspace("roboflow-100").project("cloud-types")
flower_model = project.version(2).model
# create BYTETracker instance
flower_tracker = sv.ByteTrack(track_thresh=0.25, track_buffer=30, match_thresh=0.8, frame_rate=30)
# create VideoInfo instance
video_info = sv.VideoInfo.from_video_path(SOURCE_VIDEO_PATH)
# create frame generator
generator = sv.get_video_frames_generator(SOURCE_VIDEO_PATH)
# create LineZone instance, it is previously called LineCounter class
line_zone = sv.LineZone(start=LINE_START, end=LINE_END)
# create instance of BoxAnnotator
box_annotator = sv.BoxAnnotator(thickness=4, text_thickness=4, text_scale=2)
# create instance of TraceAnnotator
trace_annotator = sv.TraceAnnotator(thickness=4, trace_length=50)
line_zone_annotator = sv.LineZoneAnnotator(thickness=4, text_thickness=4, text_scale=2)
# define call back function to be used in video processing
def callback(frame: np.ndarray, index:int) -> np.ndarray:
# model prediction on single frame and conversion to supervision Detections
results = flower_model.predict(frame).json()
flowers = sv.Detections.from_roboflow(results)
# show flower detections in real time
print(flowers)
# tracking flower detections
flowers = flower_tracker.update_with_detections(flowers)
annotated_frame = trace_annotator.annotate(
scene=frame.copy(),
detections=flowers
)
annotated_frame=box_annotator.annotate(
scene=annotated_frame,
detections=flowers
)
# update line counter
line_zone.trigger(flowers)
# return frame with box and line annotated result
return line_zone_annotator.annotate(annotated_frame, line_counter=line_zone)
# process the whole video
sv.process_video(
source_path = SOURCE_VIDEO_PATH,
target_path = TARGET_VIDEO_PATH,
callback=callback
)