vet-parasites-blood – AI Model by ikchain | AlphaNeural AI

You can deploy this model and start earning money today!

ikchain/
vet-parasites-blood

like

0

image-classification

gemma-4-good-hackathon

Views

No views yet

Blood Parasite Classifier — Howl Vision

EfficientNetV2-S for blood smear microscopy (8 classes: 6 parasites + 2 controls). Part of Howl Vision for the Gemma 4 Good Hackathon.

One Health: Leishmania, Plasmodium, Toxoplasma, Trypanosome are zoonotic.

Metrics (held-out test, n=3032)

Metric	Value
Accuracy	99.87%
F1-macro	0.997

Details

Architecture: timm/tf_efficientnetv2_s.in21k_ft_in1k, num_classes=8
Input: 384x384 RGB, ImageNet normalization
Classes: Babesia, Leishmania, Leukocyte, Plasmodium, RBCs, Toxoplasma, Trichomonad, Trypanosome
Caveat: Validated on standardized microscopy. Generalization to variable staining/magnification requires separate validation.

python

import timm, torch
model = timm.create_model("tf_efficientnetv2_s.in21k_ft_in1k", pretrained=False, num_classes=8)
ckpt = torch.load("vet_parasites.pt", map_location="cpu", weights_only=False)
model.load_state_dict(ckpt["model_state_dict"])
model.eval()