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Top Stapler Computer Vision Models
The models below have been fine-tuned for various stapler 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 stapler detection model.
At the bottom of this page, we have guides on how to count staplers in images and videos.
4800 images1 model
4619 images1 model
Allis ForcepsArmy Navy RetractorArtery ForcepsBabcock ForcepsBackhaus Towel ClampBandage SciccorsCatheter TrayCheatle ForcepsCurved MayoCzerney RetractorDeaver RetractorDesjardin ForcepsDisscting Forceps -Plain-Disscting Forceps -tooth-Dissecting Forceps PlainDissecting Forceps ToothDoyel Instestinal ForcepsDoyen Cross Action Towel ClampEye Intestinal ForcepsHarrington Retractor
1067 images2 models
Abdominal WallBiliopancreatic LimbBloodBypassed StomachCircular Opening on Gastric PouchCircular Stapler AnvilCircular Stapler Anvil ShaftCircular Stapler ShaftCurved Grasping ForcepsDistal Small IntestineDistal Small Intestine (Revisar)FatGastric PouchLaparoscopic Cautery HookLinear Grasping ForcepsLinear Grasping forcepsLinear StaplerLiverMeasuring BandMesentery Fat
1050 images2 models
Abdominal WallBiliopancreatic LimbBloodBypassed StomachCircular Opening on Gastric PouchCircular Stapler AnvilCircular Stapler Anvil ShaftCircular Stapler ShaftCurved Grasping ForcepsDistal Small IntestineFatGastric PouchLaparoscopic Cautery HookLinear Grasping ForcepsLinear StaplerLiverMeasuring BandMesentery FatRoux LimbSmall Intestine
3999 images1 model
bedcabinetmirrormobilesofaA black desk lamp on top of a desk next to white sheets of paper and a science posterA black laptop, a black computer screen, a gray and black keyboard and a computer mouse on top of a black desk and chairA black laptop, a black computer screen, a gray and black keyboard and a computer mouse on top of a deskA black laptop, a gray and black keyboard and a computer mouse on top of a white piece of paper on a deskA black laptop, some cables, a black computer screen on top of a deskA black stapler, some glasses and a black cable on a deskA blue and yellow can on top of a desk next to some white sheets of paper, a computer screen and a keyboardA blue chair and a black floorA blue vending machineA bottle of water on top of a desk next to a black chairA cabinet of foldersA cabinet of folders and a chairA cabinet of folders and a window with curtainsA cabinet of folders and some posters fixed on the wallA cabinet of folders and some white sheets and some chairs
Guide: How to Count Staplers with Computer Vision
With a model hosted on Roboflow like the ones above and the open source supervision Python package, you can count staplers in your images and videos.
The following code snippet counts the number of staplers 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 stapler project you choose above
project = rf.workspace("krispcode-agr").project("kaptcha")
stapler_model = project.version(2).model
results = stapler_model.predict("stapler.jpg").json()
staplers = sv.Detections.from_roboflow(results)
# print number of staplers
print(len(staplers))Guide: How to Count Staplers in a Zone
With a bit more code, you can count the number of stapler present in a specific zone of your image or video.
The following code snippet counts the number of stapler 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 = "stapler.mp4"
TARGET_VIDEO_PATH = "stapler_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 stapler project you choose above
project = rf.workspace("krispcode-agr").project("kaptcha")
stapler_model = project.version(2).model
# create BYTETracker instance
stapler_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 = stapler_model.predict(frame).json()
staplers = sv.Detections.from_roboflow(results)
# show stapler detections in real time
print(staplers)
# tracking stapler detections
staplers = stapler_tracker.update_with_detections(staplers)
annotated_frame = box_annotator.annotate(scene=frame, detections=staplers)
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 Staplers Crossing a Line
You can count how many staplers have crossed a line using the supervision LineCounter method.
The following code snippet counts the number of staplers 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 = "stapler.mp4"
TARGET_VIDEO_PATH = "stapler_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 stapler project you choose above
project = rf.workspace("krispcode-agr").project("kaptcha")
stapler_model = project.version(2).model
# create BYTETracker instance
stapler_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 = stapler_model.predict(frame).json()
staplers = sv.Detections.from_roboflow(results)
# show stapler detections in real time
print(staplers)
# tracking stapler detections
staplers = stapler_tracker.update_with_detections(staplers)
annotated_frame = trace_annotator.annotate(
scene=frame.copy(),
detections=staplers
)
annotated_frame=box_annotator.annotate(
scene=annotated_frame,
detections=staplers
)
# update line counter
line_zone.trigger(staplers)
# 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
)