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Yolo26n Coco128 Finetune

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

Sourcehuggingface.co/deki/yolo26n-coco128-finetune/tree/d4f684d1674aa6700d914283a566d5dd017d4052
KindOtherVersionRevision d4f684dBgrade

Model source

Kind
Other
File size
5.24 MB
Version
vRevision d4f684d
Updated
Oct 23, 2026
Downloads
11
Source
Hugging Face

Source description

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()

Sources

1 source
Hugging FacePrimaryVersion d4f684d1674aa6700d914283a566d5dd017d4052
huggingface.co/deki/yolo26n-coco128-finetune/tree/d4f684d1674aa6700d914283a566d5dd017d4052

Verified Aug 18

Model artifacts

1 artifact

best.pt

pt · 5.24 MB · SHA-256 188c52723f2b…ba00 · Hugging Face

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Source excerpts

2 excerpts
ReadmeAug 18huggingface.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()

NoteAug 18huggingface.co/deki/yolo26n-coco128-finetune

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