ModelListadosExterno

Yolo26n Coco128 Finetune

This repository contains best.pt, a YOLO26n object-detection model fine-tuned on the COCO128 dataset.

Fontehuggingface.co/deki/yolo26n-coco128-finetune/tree/d4f684d1674aa6700d914283a566d5dd017d4052

TipoOtherVersãoRevision d4f684dBnota

Fonte do modelo

Tipo: Other
Tamanho do arquivo: 5,24 MB
Versão: vRevision d4f684d
Atualizado: 1 de nov. de 2026
Downloads: 26
Fonte: Hugging Face

Descrição da fonte

YOLO26n COCO128 Fine-Tuned Object Detection

This repository contains best.pt, a YOLO26n object-detection model fine-tuned on the COCO128 dataset.

Training

The model was initialized from pretrained YOLO26n weights and fine-tuned on COCO128 using Ultralytics.

Training settings:

Base model: yolo26n.pt
Dataset: coco128.yaml
Epochs: 50
Image size: 512
Batch size: 4
Device: Apple MPS
Run name: yolo26n_coco128_finetune_v2

Results

Best validation metrics from the training run:

mAP50: 0.731
mAP50-95: 0.558

Usage

from ultralytics import YOLO

model = YOLO("best.pt")
results = model("image.jpg")
results[0].show()

Fontes

1 fonte

Hugging FacePrimárioVersão d4f684d1674aa6700d914283a566d5dd017d4052

huggingface.co/deki/yolo26n-coco128-finetune/tree/d4f684d1674aa6700d914283a566d5dd017d4052

Verificado 1 de nov.

Artefatos de modelo

1 artefato

best.pt

pt · 5,24 MB · SHA-256 188c52723f2b…ba00 · Hugging Face

Download file

Trechos de fonte

3 trechos

Readme18 de ago.huggingface.co/deki/yolo26n-coco128-finetune

--- license: agpl-3.0 library_name: ultralytics tags: - yolo - yolo26 - object-detection - coco128 - fine-tuned datasets: - coco128 --- # YOLO26n COCO128 Fine-Tuned Object Detection This repository contains `best.pt`, a YOLO26n object-detection model fine-tuned on the COCO128 dataset. ## Training The model was initialized from pretrained YOLO26n weights and fine-tuned on COCO128 using Ultralytics. Training settings: - Base model: `yolo26n.pt` - Dataset: `coco128.yaml` - Epochs: 50 - Image size: 512 - Batch size: 4 - Device: Apple MPS - Run name: `yolo26n_coco128_finetune_v2` ## Results Best validation metrics from the training run: - mAP50: 0.731 - mAP50-95: 0.558 ## Usage ```python from ultralytics import YOLO model = YOLO("best.pt") results = model("image.jpg") results[0].show()

Note1 de nov.huggingface.co/deki/yolo26n-coco128-finetune

Source context: 26 downloads · 1 likes · Pipeline object-detection · Library ultralytics · Repo deki/yolo26n-coco128-finetune

Note18 de ago.huggingface.co/deki/yolo26n-coco128-finetune

Source context: 11 downloads · 0 likes · Pipeline object-detection · Library ultralytics · Repo deki/yolo26n-coco128-finetune