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CowcatcherAI/
cowcatcherV17.pt

Name: cowcatcherV17.pt
Availability: InStock

lgpl-3.0

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Cowcatcher

Website

AI-Detector Repo

CowCatcherAI Repo

CalvingCatcherAI Repo

Check out the website for all the information: cowcatcherai.com The website is available in multiple languages such as Deutsch, Français, Nederlands, and Español.

Cowcatcher is a vision model that detects mounting behaviour in cattle from camera images. Mounting is one of the clearest visual signals that a cow is in heat, so a reliable detector lets a barn camera flag oestrus automatically instead of relying on someone watching the herd.

All models are single-class (mounting) Ultralytics models, trained at 640 px on farm camera footage from multiple dairy and beef sites, in daylight, night/IR and robot-barn conditions.

Latest and best model: cowcatcherV17.2.pt. It is the only model in the comparison that produces zero false alarms on the null set, at any threshold from 0.50 upward.

Available models

File	Task	Base	Train imgsz / epochs	Size	Notes
`cowcatcherV17.2.pt`	Detection	`yolo26m`	640 / 50	44 MB	Recommended. Best precision and best confidence separation
`cowcatcherV17.pt`	Detection	`yolo26m`	640 / 400	131 MB	Previous main model, higher recall at low thresholds
`cowcatcherV16.pt`	Detection	`yolo26m`	640 / 400	88 MB	Highest recall at 0.88, but 21 false alarms
`cowcatcherV15.pt`	Detection	`yolo12m`	640 / 100	81 MB	Older generation
`cowcatcherV14.pt`	Detection	`yolo12m`	640 / 100	41 MB	Older generation, very few false alarms but low recall
`cowcatcherV13.pt`	Detection	`yolo12m`	640 / 100	81 MB	Oldest published model, kept for reference
`cowcatcherV1-seg.pt`	Segmentation	`yolo11m`	640 / 100	45 MB	Experimental mask model, not part of the benchmark below

All six detection models are published here and all six appear in the benchmark tables below.

Quick start

pip install ultralytics

python

from ultralytics import YOLO

model = YOLO("cowcatcherV17.2.pt")

# Single image
results = model.predict("barn_frame.jpg", conf=0.50)

for r in results:
    for box in r.boxes:
        print("mounting", float(box.conf), box.xyxy.tolist())

python

# Live camera / RTSP stream
for result in model.predict(source="rtsp://camera/stream", stream=True, conf=0.50):
    if len(result.boxes):
        print("Mounting detected")

Which confidence threshold?

For cowcatcherV17.2 the false-positive count stays at 0 across the entire 0.50–0.99 sweep, so the threshold only trades away recall. F1 peaks at 0.50; anything up to ~0.80 is still a sensible operating point if you want extra headroom.

Threshold	Found (TP)	Missed (FN)	Recall	Precision	F1
0.50	896	4	99.56%	100%	99.78%
0.60	892	8	99.11%	100%	99.55%
0.70	879	21	97.67%	100%	98.82%
0.80	869	31	96.56%	100%	98.25%
0.88	822	78	91.33%	100%	95.47%
0.92	638	262	70.89%	100%	82.96%
0.95	125	775	13.89%	100%	24.39%

Training

Every model in this repository was trained with the same script (train.py) — only the base checkpoint and the epoch count change per version.

python

from ultralytics import YOLO

# Load model
model = YOLO("yolo26m.pt")

# Train with added augmentation
model.train(
    data="dataset.yaml",
    imgsz=640,
    batch=16,
    epochs=50,
    patience=10,
    #fraction=0.05, # currently uses 100% of the dataset; 0.8 = 80% of the set
    save_period=5,
    workers=0,
    device=0,
    #close_mosaic=10,

    # Augmentation settings
    augment=True,      # General augmentation on/off
    degrees=10,        # Rotation up to 10 degrees
    translate=0.1,     # Translation up to 10%
    scale=0.5,         # Scaling between 0.5 and 1.5
    fliplr=0.5,        # 50% chance of horizontal flip
    hsv_h=0.015,       # Minor hue variations
    hsv_s=0.7,         # Saturation variations
    hsv_v=0.4,         # Brightness variations
    mosaic=1.0         # Mosaic augmentation (combines 4 images)
)

Per-version settings — everything else in the script stays identical:

Model	Base checkpoint	Epochs
`cowcatcherV17.2.pt`	`yolo26m.pt`	50
`cowcatcherV17.pt`	`yolo26m.pt`	400
`cowcatcherV16.pt`	`yolo26m.pt`	400
`cowcatcherV15.pt`	`yolo12m.pt`	100
`cowcatcherV14.pt`	`yolo12m.pt`	100
`cowcatcherV13.pt`	`yolo12m.pt`	100
`cowcatcherV1-seg.pt`	`yolo11m-seg.pt`	100

dataset.yaml points at the single-class (mounting) farm-camera dataset, which is not published here. patience=10 stops a run early once validation stops improving, so the effective epoch count can be lower than the number listed. Training ran on a single GPU (device=0), with workers=0 for Windows compatibility.

Benchmark

Test set: 1000 held-out images — 900 real mounting events and 100 null/noise images (cattle, barns and robots without mounting). Six models evaluated on 2026-07-23.

Of the 900 mounting events, 11 are found by no model at 0.88, and 536 are missed by at least one model.

Performance at threshold 0.88

Model	Recall %	Precision %	F1 %	False alarms	Missed	Accuracy %	ms/image
cowcatcherV17.2	91.33	100.00	95.47	0	78	92.2	24.1
cowcatcherV16	96.33	97.64	96.98	21	33	94.6	24.4
cowcatcherV17	86.89	99.11	92.60	7	118	87.5	24.3
cowcatcherV15	74.44	97.95	84.60	14	230	75.6	27.3
cowcatcherV14	65.00	99.66	78.68	2	315	68.3	27.4
cowcatcherV13	50.11	99.34	66.62	3	449	54.8	29.0

Note that 0.88 is a legacy production threshold. It suits V16, but it sits far above the optimum for V17.2 — see the next table.

Best achievable operating point (max F1)

Model	Best threshold	Found	Missed	False alarms	Recall %	Precision %	F1 %
cowcatcherV17.2	0.50	896	4	0	99.56	100.00	99.78
cowcatcherV17	0.50	897	3	14	99.67	98.46	99.06
cowcatcherV14	0.50	886	14	24	98.44	97.36	97.90
cowcatcherV16	0.80	891	9	33	99.00	96.43	97.70
cowcatcherV15	0.61	894	6	41	99.33	95.61	97.44
cowcatcherV13	0.50	867	33	24	96.33	97.31	96.82

Confidence separation

The gap between the mean confidence on real mounts and on null images decides how easy the model is to threshold. V17.2 is in a different class here: it is almost silent on noise (mean 0.008) while staying confident on real events (mean 0.918).

Model	Avg conf — real mounts	Avg conf — null noise	Margin
cowcatcherV17.2	0.9180	0.0079	0.9101
cowcatcherV17	0.9026	0.1469	0.7557
cowcatcherV14	0.8718	0.2215	0.6503
cowcatcherV13	0.8409	0.2250	0.6159
cowcatcherV16	0.9316	0.4190	0.5126
cowcatcherV15	0.8893	0.3875	0.5018

The same effect shows in the safety margin (5th percentile of real mounts minus 95th percentile of nulls). Every other model is negative — its distributions overlap — while V17.2 reaches +0.827:

Model	Lowest conf on a real mount	Highest conf on a null	P05 mounts	P95 nulls	Safety margin
cowcatcherV17.2	0.1558	0.4382	0.8450	0.0176	+0.8274
cowcatcherV16	0.0051	0.9304	0.8866	0.9130	−0.0264
cowcatcherV17	0.4547	0.9107	0.8251	0.8907	−0.0656
cowcatcherV15	0.4584	0.9060	0.8194	0.8965	−0.0772
cowcatcherV14	0.0004	0.8925	0.7553	0.8407	−0.0854
cowcatcherV13	0.0001	0.8995	0.6154	0.8643	−0.2489

Confidence distribution on null images

Number of null images falling in each confidence bin — lower and further left is better.

Bin	V13	V14	V15	V16	V17	V17.2
0.00–0.20	68	70	51	47	79	99
0.20–0.60	11	6	6	7	9	1
0.60–0.86	15	19	28	17	4	0
0.86–0.94	6	5	15	29	8	0
0.94–1.00	0	0	0	0	0	0

Choosing a model

Default choice — cowcatcherV17.2. Best F1, zero false alarms, widest usable threshold window. Pick this unless you have a specific reason not to.
cowcatcherV16 still has the highest raw recall at the legacy 0.88 threshold, but pays for it with 21 false alarms out of 100 nulls and heavily overlapping confidence distributions.

Limitations

Single class only: the model detects mounting, not cow identity, lameness or other behaviours.
Trained and evaluated on fixed barn cameras. Very different mounting angles, heavy occlusion or unusual camera placements are under-represented.
The 100-image null set is small; a false-positive rate of 0 on it should be read as "no false alarms observed", not as a guarantee.
Benchmark timings (24–29 ms/image) were measured on the evaluation machine and will differ on other hardware.

License

AGPL-3.0.

These weights are trained with Ultralytics, which is licensed under AGPL-3.0. Every checkpoint in this repository carries the Ultralytics AGPL-3.0 notice in its metadata, so the derived weights inherit that license.

In practice this means that if you use these models in a network-accessible service, AGPL-3.0 requires you to make the corresponding source of that service available to its users. If that does not work for your deployment, Ultralytics sells a commercial Enterprise License that removes the copyleft obligation — see ultralytics.com/license.

CowcatcherAI/cowcatcherV17.pt

Cowcatcher

Available models

Quick start

Which confidence threshold?

Training

Benchmark

Performance at threshold 0.88

Best achievable operating point (max F1)

Confidence separation

Confidence distribution on null images

Choosing a model

Limitations

License

CowcatcherAI/
cowcatcherV17.pt