HongChuang/ultralytics-ascend
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions 3

ultralytics 8.2.2 replace COCO128 with COCO8 (#10167)

Browse source 
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
This commit is contained in:
Glenn Jocher 2024-04-18 20:47:21 -07:00 • committed by GitHub
parent 626309d221
commit 1110258d37
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
43 changed files with 154 additions and 156 deletions
Download patch file Download diff file Expand all files Collapse all files

14
docs/en/usage/python.md
View file

@ -32,8 +32,8 @@ For example, users can load a model, train it, evaluate its performance on a val
# Load a pretrained YOLO model (recommended for training)
model = YOLO('yolov8n.pt')
# Train the model using the 'coco128.yaml' dataset for 3 epochs
results = model.train(data='coco128.yaml', epochs=3)
# Train the model using the 'coco8.yaml' dataset for 3 epochs
results = model.train(data='coco8.yaml', epochs=3)
# Evaluate the model's performance on the validation set
results = model.val()
@ -66,7 +66,7 @@ Train mode is used for training a YOLOv8 model on a custom dataset. In this mode
from ultralytics import YOLO
model = YOLO('yolov8n.yaml')
results = model.train(data='coco128.yaml', epochs=5)
results = model.train(data='coco8.yaml', epochs=5)
```
=== "Resume"
@ -90,7 +90,7 @@ Val mode is used for validating a YOLOv8 model after it has been trained. In thi
from ultralytics import YOLO
model = YOLO('yolov8n.yaml')
model.train(data='coco128.yaml', epochs=5)
model.train(data='coco8.yaml', epochs=5)
model.val() # It'll automatically evaluate the data you trained.
```
@ -103,7 +103,7 @@ Val mode is used for validating a YOLOv8 model after it has been trained. In thi
# It'll use the data YAML file in model.pt if you don't set data.
model.val()
# or you can set the data you want to val
model.val(data='coco128.yaml')
model.val(data='coco8.yaml')
```
[Val Examples](../modes/val.md){ .md-button }
@ -259,7 +259,7 @@ Explorer API can be used to explore datasets with advanced semantic, vector-simi
from ultralytics import Explorer
# create an Explorer object
exp = Explorer(data='coco128.yaml', model='yolov8n.pt')
exp = Explorer(data='coco8.yaml', model='yolov8n.pt')
exp.create_embeddings_table()
similar = exp.get_similar(img='https://ultralytics.com/images/bus.jpg', limit=10)
@ -280,7 +280,7 @@ Explorer API can be used to explore datasets with advanced semantic, vector-simi
from ultralytics import Explorer
# create an Explorer object
exp = Explorer(data='coco128.yaml', model='yolov8n.pt')
exp = Explorer(data='coco8.yaml', model='yolov8n.pt')
exp.create_embeddings_table()
similar = exp.get_similar(idx=1, limit=10)