https://github.com/kadirnar/sahi-detectron
SAHIDET2: SAHI Kullanarak Yüksek Doğruluklu Nesne Tespit Uygulaması Yap!
https://github.com/kadirnar/sahi-detectron
deep-learning detectron2 object-detection pytorch
Last synced: about 2 months ago
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SAHIDET2: SAHI Kullanarak Yüksek Doğruluklu Nesne Tespit Uygulaması Yap!
- Host: GitHub
- URL: https://github.com/kadirnar/sahi-detectron
- Owner: kadirnar
- License: mit
- Created: 2022-02-20T18:10:48.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2022-03-17T22:37:00.000Z (over 4 years ago)
- Last Synced: 2025-07-27T09:41:19.231Z (11 months ago)
- Topics: deep-learning, detectron2, object-detection, pytorch
- Language: Python
- Homepage:
- Size: 2.38 MB
- Stars: 2
- Watchers: 2
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
SAHIDET2: SAHI Kullanarak Yüksek Doğruluklu Nesne Tespit Uygulaması Yap!
1. Kütüphanelerin Yüklenmesi:
```
pip install -U git+https://github.com/obss/sahi.git
pip install detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cpu/torch1.10/index.html # for Detectron2-cpu
#pip install detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu111/torch1.10/index.html # for Detectron2-cuda11.1
```
2. Kütüphanelerin Import Edilmesi:
```
from sahi.utils.detectron2 import Detectron2TestConstants
from sahi.model import Detectron2DetectionModel
from sahi.predict import get_sliced_prediction, predict, get_prediction
from sahi.utils.file import download_from_url
from sahi.utils.cv import read_image
from IPython.display import Image
```
3. Model ve Test Resimlerinin Yüklenmesi:
```
model_path = download_from_url(
"https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_FPN_3x/137849458/model_final_280758.pkl",
"model_final_280758.pkl")
image = download_from_url('https://raw.githubusercontent.com/obss/sahi/main/demo/demo_data/small-vehicles1.jpeg',
'demo_data/small-vehicles1.jpeg')
```
4. Cfg Dosyasının Yüklenmesi:
```
cfg = get_cfg()
cfg.MODEL.ROI_HEADS.NUM_CLASSES = 445
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.2
config_path = export_cfg_as_yaml(cfg, export_path='config.yaml')
```
5. Detectron2 Kullanarak Modelin Test Edilmesi:
```
detection_model = Detectron2DetectionModel(
model_path=model_path,
config_path=config_path,
confidence_threshold=0.2,
image_size=640,
device="cpu", # or 'cuda:0'
)
result = get_prediction("demo_data/small-vehicles1.jpeg", detection_model)
result.export_visuals(export_dir="demo_data/")
Image("demo_data/prediction_visual.png")
```
6. SAHI+ Detectron2 Model Test Edilmesi:
```
result = get_sliced_prediction(
"demo_data/small-vehicles1.jpeg",
detection_model,
slice_height = 256,
slice_width = 256,
overlap_height_ratio = 0.2,
overlap_width_ratio = 0.2,
)
result = get_prediction("demo_data/small-vehicles1.jpeg", detection_model)
result.export_visuals(export_dir="demo_data/")
Image("demo_data/prediction_visual.png")
```