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Top Wood Computer Vision Models
The models below have been fine-tuned for various wood 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 wood detection model.
At the bottom of this page, we have guides on how to count woods in images and videos.
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Bird Species detector
9880 images1 model
ABBOTTS BABBLERABBOTTS BOOBYABYSSINIAN GROUND HORNBILLAFRICAN CROWNED CRANEAFRICAN EMERALD CUCKOOAFRICAN FIREFINCHAFRICAN OYSTER CATCHERAFRICAN PIED HORNBILLAFRICAN PYGMY GOOSEALBATROSSALBERTS TOWHEEALEXANDRINE PARAKEETALPINE CHOUGHALTAMIRA YELLOWTHROATAMERICAN AVOCETAMERICAN BITTERNAMERICAN COOTAMERICAN DIPPERAMERICAN FLAMINGOAMERICAN GOLDFINCH
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BirdDetection
3345 images1 model
ABBOTTS BABBLERABBOTTS BOOBYABYSSINIAN GROUND HORNBILLAFRICAN CROWNED CRANEAFRICAN EMERALD CUCKOOAFRICAN FIREFINCHAFRICAN OYSTER CATCHERAFRICAN PIED HORNBILLAFRICAN PYGMY GOOSEALBATROSSALBERTS TOWHEEALEXANDRINE PARAKEETALPINE CHOUGHALTAMIRA YELLOWTHROATAMERICAN AVOCETAMERICAN BITTERNAMERICAN COOTAMERICAN DIPPERAMERICAN FLAMINGOAMERICAN GOLDFINCH
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Tools
9534 images2 models
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kona_2.11
4800 images1 model
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ecommerce-photography
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
NE Bird
3040 images1 model
American RobinAmerican Tree SparrowAmerican_CrowAmerican_GoldfinchAmerican_RedstartBaltimore_OrioleBarn_SwallowBewick_WrenBlack Headed GrosbeakBlack-Capped ChickadeeBlack_billed_CuckooBlue-Grey GnatcatcherBlue_JayBohemian_WaxwingBrewer_BlackbirdBrown_CreeperBrown_ThrasherCardinalCarolina ChickadeeCarolina_Wren
Guide: How to Count Woods with Computer Vision
With a model hosted on Roboflow like the ones above and the open source supervision Python package, you can count woods in your images and videos.
The following code snippet counts the number of woods 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 wood project you choose above
project = rf.workspace("large-benchmark-datasets").project("logistics-sz9jr")
wood_model = project.version(2).model
results = wood_model.predict("wood.jpg").json()
woods = sv.Detections.from_roboflow(results)
# print number of woods
print(len(woods))Guide: How to Count Woods in a Zone
With a bit more code, you can count the number of wood present in a specific zone of your image or video.
The following code snippet counts the number of wood 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 = "wood.mp4"
TARGET_VIDEO_PATH = "wood_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 wood project you choose above
project = rf.workspace("large-benchmark-datasets").project("logistics-sz9jr")
wood_model = project.version(2).model
# create BYTETracker instance
wood_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 = wood_model.predict(frame).json()
woods = sv.Detections.from_roboflow(results)
# show wood detections in real time
print(woods)
# tracking wood detections
woods = wood_tracker.update_with_detections(woods)
annotated_frame = box_annotator.annotate(scene=frame, detections=woods)
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 Woods Crossing a Line
You can count how many woods have crossed a line using the supervision LineCounter method.
The following code snippet counts the number of woods 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 = "wood.mp4"
TARGET_VIDEO_PATH = "wood_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 wood project you choose above
project = rf.workspace("large-benchmark-datasets").project("logistics-sz9jr")
wood_model = project.version(2).model
# create BYTETracker instance
wood_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 = wood_model.predict(frame).json()
woods = sv.Detections.from_roboflow(results)
# show wood detections in real time
print(woods)
# tracking wood detections
woods = wood_tracker.update_with_detections(woods)
annotated_frame = trace_annotator.annotate(
scene=frame.copy(),
detections=woods
)
annotated_frame=box_annotator.annotate(
scene=annotated_frame,
detections=woods
)
# update line counter
line_zone.trigger(woods)
# 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
)