Add colab template and minor fixes (#137)

Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
This commit is contained in:
Ayush Chaurasia
2023-01-04 02:00:39 +05:30
committed by GitHub
parent 3c18aa5278
commit dcd8ef68e6
4 changed files with 18 additions and 6 deletions

View File

@ -1,6 +1,16 @@
# Welcome to Ultralytics YOLO
<div align="center">
<a href="https://ultralytics.com/yolov5" target="_blank">
<img width="1024" src="https://user-images.githubusercontent.com/26833433/210431393-39c997b8-92a7-4957-864f-1f312004eb54.png"></a>
<br>
<a href="https://bit.ly/yolov5-paperspace-notebook"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"></a>
<a href="https://colab.research.google.com/github/glenn-jocher/glenn-jocher.github.io/blob/main/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
<a href="https://www.kaggle.com/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
<br>
</div>
Welcome to the Ultralytics YOLO documentation landing page! Ultralytics YOLOv8 is the latest version of the YOLO (You
# Welcome to Ultralytics YOLOv8
Welcome to the Ultralytics YOLOv8 documentation landing page! Ultralytics YOLOv8 is the latest version of the YOLO (You
Only Look Once) object detection and image segmentation model developed by Ultralytics. This page serves as the starting
point for exploring the various resources available to help you get started with YOLOv8 and understand its features and
capabilities.

View File

@ -47,7 +47,7 @@ dnn: False # use OpenCV DNN for ONNX inference
plots: True # show plots during training
# Prediction settings --------------------------------------------------------------------------------------------------
source: "ultralytics/assets" # source directory for images or videos
source: null # source directory for images or videos
show: False # show results if possible
save_txt: False # save results as .txt file
save_conf: False # save results with confidence scores

View File

@ -224,4 +224,5 @@ class YOLO:
args.pop("device", None)
args.pop("project", None)
args.pop("name", None)
args.pop("batch_size", None)
args.pop("batch", None)
args.pop("epochs", None)

View File

@ -2,7 +2,7 @@ import hydra
import torch
from ultralytics.yolo.engine.predictor import BasePredictor
from ultralytics.yolo.utils import DEFAULT_CONFIG, ops
from ultralytics.yolo.utils import DEFAULT_CONFIG, ROOT, ops
from ultralytics.yolo.utils.checks import check_imgsz
from ultralytics.yolo.utils.plotting import Annotator, colors, save_one_box
@ -83,8 +83,9 @@ class DetectionPredictor(BasePredictor):
@hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name)
def predict(cfg):
cfg.model = cfg.model or "n.pt"
cfg.model = cfg.model or "yolov8n.pt"
cfg.imgsz = check_imgsz(cfg.imgsz, min_dim=2) # check image size
cfg.source = cfg.source or ROOT / "assets"
predictor = DetectionPredictor(cfg)
predictor()