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ultralytics 8.0.216 fix hard-coded batch=64 cls loss (ultralytics…
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…#6523)

Signed-off-by: Glenn Jocher <[email protected]>
Co-authored-by: HDW AI group <[email protected]>
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glenn-jocher and hujunyao authored Nov 22, 2023
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2 changes: 1 addition & 1 deletion docs/hi/models/yolov3.md
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Expand Up @@ -82,7 +82,7 @@ YOLOv3 श्रृंखला, इनमें YOLOv3, YOLOv3-Ultralytics औ

अगर आप अपने शोध में YOLOv3 का उपयोग करते हैं, तो कृपया मूल YOLO पेपर्स और Ultralytics YOLOv3 रिपॉज़िटरी को उद्धृत करें।

!!! उध्दरण ""
!!! Quote ""

=== "BibTeX"

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3 changes: 1 addition & 2 deletions docs/hi/models/yolov5.md
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Expand Up @@ -72,8 +72,7 @@ YOLOv5u वस्तु ज्ञापन के तरीकों में
model.info()

# COCO8 प्रायोगिक उदाहरण डेटासेट पर 100 एपॉक के लिए मॉडल

ka प्रशिक्षित करें results = model.train(data='coco8.yaml', epochs=100, imgsz=640)
results = model.train(data='coco8.yaml', epochs=100, imgsz=640)

# YOLOv5n मॉडल के साथ 'bus.jpg' छविमें ज्ञापन चलाएं
results = model('path/to/bus.jpg')
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2 changes: 1 addition & 1 deletion docs/hi/models/yolov8.md
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Expand Up @@ -36,7 +36,7 @@ Yएक मॉडल के हर मानक, विशिष्ट कार

## प्रदर्शन की मापदंड

!!! प्रदर्शन
!!! Note "प्रदर्शन"

=== "वस्तुनिर्धारण (COCO)"

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11 changes: 11 additions & 0 deletions docs/mkdocs_ar.yml
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Expand Up @@ -188,6 +188,17 @@ nav:
- الوضعية: tasks/pose.md
- النماذج:
- models/index.md
- YOLOv3: models/yolov3.md
- YOLOv4: models/yolov4.md
- YOLOv5: models/yolov5.md
- YOLOv6: models/yolov6.md
- YOLOv7: models/yolov7.md
- YOLOv8: models/yolov8.md
- SAM (Segment Anything Model): models/sam.md
- MobileSAM (Mobile Segment Anything Model): models/mobile-sam.md
- FastSAM (Fast Segment Anything Model): models/fast-sam.md
- YOLO-NAS (Neural Architecture Search): models/yolo-nas.md
- RT-DETR (Realtime Detection Transformer): models/rtdetr.md
- المجموعات البيانية:
- datasets/index.md

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11 changes: 11 additions & 0 deletions docs/mkdocs_de.yml
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Expand Up @@ -188,6 +188,17 @@ nav:
- Pose: tasks/pose.md
- Modelle:
- models/index.md
- YOLOv3: models/yolov3.md
- YOLOv4: models/yolov4.md
- YOLOv5: models/yolov5.md
- YOLOv6: models/yolov6.md
- YOLOv7: models/yolov7.md
- YOLOv8: models/yolov8.md
- SAM (Segment Anything Model): models/sam.md
- MobileSAM (Mobile Segment Anything Model): models/mobile-sam.md
- FastSAM (Fast Segment Anything Model): models/fast-sam.md
- YOLO-NAS (Neural Architecture Search): models/yolo-nas.md
- RT-DETR (Realtime Detection Transformer): models/rtdetr.md
- Datensätze:
- datasets/index.md

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11 changes: 11 additions & 0 deletions docs/mkdocs_es.yml
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Expand Up @@ -188,6 +188,17 @@ nav:
- Pose: tasks/pose.md
- Modelos:
- models/index.md
- YOLOv3: models/yolov3.md
- YOLOv4: models/yolov4.md
- YOLOv5: models/yolov5.md
- YOLOv6: models/yolov6.md
- YOLOv7: models/yolov7.md
- YOLOv8: models/yolov8.md
- SAM (Segment Anything Model): models/sam.md
- MobileSAM (Mobile Segment Anything Model): models/mobile-sam.md
- FastSAM (Fast Segment Anything Model): models/fast-sam.md
- YOLO-NAS (Neural Architecture Search): models/yolo-nas.md
- RT-DETR (Realtime Detection Transformer): models/rtdetr.md
- Conjuntos de datos:
- datasets/index.md

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11 changes: 11 additions & 0 deletions docs/mkdocs_fr.yml
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Expand Up @@ -188,6 +188,17 @@ nav:
- Pose: tasks/pose.md
- Modèles:
- models/index.md
- YOLOv3: models/yolov3.md
- YOLOv4: models/yolov4.md
- YOLOv5: models/yolov5.md
- YOLOv6: models/yolov6.md
- YOLOv7: models/yolov7.md
- YOLOv8: models/yolov8.md
- SAM (Segment Anything Model): models/sam.md
- MobileSAM (Mobile Segment Anything Model): models/mobile-sam.md
- FastSAM (Fast Segment Anything Model): models/fast-sam.md
- YOLO-NAS (Neural Architecture Search): models/yolo-nas.md
- RT-DETR (Realtime Detection Transformer): models/rtdetr.md
- Jeux de données:
- datasets/index.md

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11 changes: 11 additions & 0 deletions docs/mkdocs_hi.yml
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Expand Up @@ -188,6 +188,17 @@ nav:
- मुद्रा: tasks/pose.md
- मॉडल:
- models/index.md
- YOLOv3: models/yolov3.md
- YOLOv4: models/yolov4.md
- YOLOv5: models/yolov5.md
- YOLOv6: models/yolov6.md
- YOLOv7: models/yolov7.md
- YOLOv8: models/yolov8.md
- SAM (Segment Anything Model): models/sam.md
- MobileSAM (Mobile Segment Anything Model): models/mobile-sam.md
- FastSAM (Fast Segment Anything Model): models/fast-sam.md
- YOLO-NAS (Neural Architecture Search): models/yolo-nas.md
- RT-DETR (Realtime Detection Transformer): models/rtdetr.md
- डेटासेट्स:
- datasets/index.md

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11 changes: 11 additions & 0 deletions docs/mkdocs_ja.yml
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Expand Up @@ -188,6 +188,17 @@ nav:
- ポーズ: tasks/pose.md
- モデル:
- models/index.md
- YOLOv3: models/yolov3.md
- YOLOv4: models/yolov4.md
- YOLOv5: models/yolov5.md
- YOLOv6: models/yolov6.md
- YOLOv7: models/yolov7.md
- YOLOv8: models/yolov8.md
- SAM (Segment Anything Model): models/sam.md
- MobileSAM (Mobile Segment Anything Model): models/mobile-sam.md
- FastSAM (Fast Segment Anything Model): models/fast-sam.md
- YOLO-NAS (Neural Architecture Search): models/yolo-nas.md
- RT-DETR (Realtime Detection Transformer): models/rtdetr.md
- データセット:
- datasets/index.md

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11 changes: 11 additions & 0 deletions docs/mkdocs_ko.yml
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Expand Up @@ -188,6 +188,17 @@ nav:
- 포즈: tasks/pose.md
- 모델:
- models/index.md
- YOLOv3: models/yolov3.md
- YOLOv4: models/yolov4.md
- YOLOv5: models/yolov5.md
- YOLOv6: models/yolov6.md
- YOLOv7: models/yolov7.md
- YOLOv8: models/yolov8.md
- SAM (Segment Anything Model): models/sam.md
- MobileSAM (Mobile Segment Anything Model): models/mobile-sam.md
- FastSAM (Fast Segment Anything Model): models/fast-sam.md
- YOLO-NAS (Neural Architecture Search): models/yolo-nas.md
- RT-DETR (Realtime Detection Transformer): models/rtdetr.md
- 데이터셋:
- datasets/index.md

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11 changes: 11 additions & 0 deletions docs/mkdocs_pt.yml
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Expand Up @@ -188,6 +188,17 @@ nav:
- Pose: tasks/pose.md
- Modelos:
- models/index.md
- YOLOv3: models/yolov3.md
- YOLOv4: models/yolov4.md
- YOLOv5: models/yolov5.md
- YOLOv6: models/yolov6.md
- YOLOv7: models/yolov7.md
- YOLOv8: models/yolov8.md
- SAM (Segment Anything Model): models/sam.md
- MobileSAM (Mobile Segment Anything Model): models/mobile-sam.md
- FastSAM (Fast Segment Anything Model): models/fast-sam.md
- YOLO-NAS (Neural Architecture Search): models/yolo-nas.md
- RT-DETR (Realtime Detection Transformer): models/rtdetr.md
- Conjuntos de Dados:
- datasets/index.md

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11 changes: 11 additions & 0 deletions docs/mkdocs_ru.yml
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Expand Up @@ -188,6 +188,17 @@ nav:
- Поза: tasks/pose.md
- Модели:
- models/index.md
- YOLOv3: models/yolov3.md
- YOLOv4: models/yolov4.md
- YOLOv5: models/yolov5.md
- YOLOv6: models/yolov6.md
- YOLOv7: models/yolov7.md
- YOLOv8: models/yolov8.md
- SAM (Segment Anything Model): models/sam.md
- MobileSAM (Mobile Segment Anything Model): models/mobile-sam.md
- FastSAM (Fast Segment Anything Model): models/fast-sam.md
- YOLO-NAS (Neural Architecture Search): models/yolo-nas.md
- RT-DETR (Realtime Detection Transformer): models/rtdetr.md
- Данные:
- datasets/index.md

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11 changes: 11 additions & 0 deletions docs/mkdocs_zh.yml
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Expand Up @@ -188,6 +188,17 @@ nav:
- 姿态: tasks/pose.md
- 模型:
- models/index.md
- YOLOv3: models/yolov3.md
- YOLOv4: models/yolov4.md
- YOLOv5: models/yolov5.md
- YOLOv6: models/yolov6.md
- YOLOv7: models/yolov7.md
- YOLOv8: models/yolov8.md
- SAM (Segment Anything Model): models/sam.md
- MobileSAM (Mobile Segment Anything Model): models/mobile-sam.md
- FastSAM (Fast Segment Anything Model): models/fast-sam.md
- YOLO-NAS (Neural Architecture Search): models/yolo-nas.md
- RT-DETR (Realtime Detection Transformer): models/rtdetr.md
- 数据集:
- datasets/index.md

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2 changes: 1 addition & 1 deletion ultralytics/__init__.py
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@@ -1,6 +1,6 @@
# Ultralytics YOLO 🚀, AGPL-3.0 license

__version__ = '8.0.215'
__version__ = '8.0.216'

from ultralytics.models import RTDETR, SAM, YOLO
from ultralytics.models.fastsam import FastSAM
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2 changes: 1 addition & 1 deletion ultralytics/utils/loss.py
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Expand Up @@ -523,6 +523,6 @@ class v8ClassificationLoss:

def __call__(self, preds, batch):
"""Compute the classification loss between predictions and true labels."""
loss = torch.nn.functional.cross_entropy(preds, batch['cls'], reduction='sum') / 64
loss = torch.nn.functional.cross_entropy(preds, batch['cls'], reduction='mean')
loss_items = loss.detach()
return loss, loss_items

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