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Nemo (MAMBO_v3) identifies adult moths and butterflies in photographs, predicting 12,632 species, 4,476 genera and 104 families. Predictions use GBIF taxon IDs.

A binary healthy hard coral vs bleached hard coral classifier built on top of the ReefNet species LoRA model BobDerBaum/bioclip-2.5-vith14-reefnet-lora, which provides the fine-tuned vision-encoder LoRA adapters. Only a small linear head is trained on top (frozen backbone + LoRA + 2-way linear…

A LoRA fine-tune of imageomics/bioclip-2.5-vith14 trained contrastively on the ReefNet 1.0 coral-reef species dataset (ReefNet/ReefNet-1.0), 92-class global curated split (train 48,312 / image-val 32,792 / image-test 33,090 / source-val 8,074; split cache 56ea94e36f9f).

PhenoVisionL is a Vision Transformer (ViT-Large) model fine-tuned to detect leaf phenological states in plant photographs: green leaves, colored (senescent) leaves, and breaking leaf buds. It was trained on 165,988 iNaturalist records of deciduous woody plants using a two-stage semi-supervised…

PhenoVision is a Vision Transformer (ViT-Large) model fine-tuned to detect flowers and fruits in plant photographs. It was trained on 1.5 million human-annotated iNaturalist images and has been used to generate over 30 million new phenology records across 119,000+ plant species, vastly expanding…

For a convenient overview and download list, visit our model page for this model.

> Note: This checkpoint was donated to Huggingface-science to support open medical AI research

## Model Description This is a lightweight, high-performance image classification model built to diagnose histopathological scans of lung and colon tissues. This model was specifically designed for rapid web deployment without sacrificing clinical accuracy.

This is a MobileViT (Small) model fine-tuned on the Processed Diabetic Retinopathy dataset.

This model classifies facial skin images into 6 common dermatological conditions using a fine-tuned EfficientNetV2B0 architecture.

FloraSense is a fine-tuned Vision Transformer (ViT) model designed for accurate classification of plant species and flora-related imagery. It builds on top of the powerful google/vit-base-patch16-224 base model and is fine-tuned on the PlanterGARDENEDITION dataset curated by Sisigoks, which…

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This deep learning model is designed for ECG image classification, fine-tuned using ResNet-50. It can classify ECG images into different categories to assist in heart disease detection.

### Model Description A machine learning model for waste classification

Google ViT model is finetuned on lung and colon histopathology image classification dataset. The dataset is available on Kaggle.

This is Vision Transformer model trained for cancer classification. To make single model to predict any cancer, I trained this ViT model. following are the cancer types that model can predict: Brain cancer Breast Cancer (histopathology) Lung & Colon Cancer (histopathology) Cervical Caner Kidney…

A Vision Transformer (ViT) image classification model. \ Trained by Owkin on 40M pan-cancer histology tiles from TCGA. \ Fine-tuned on LC25000's lung subset.

## Model Description The "Bird Species Classifier" is a state-of-the-art image classification model designed to identify various bird species from images. It uses the EfficientNet architecture and has been fine-tuned to achieve high accuracy in recognizing a wide range of bird species.