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49 lines
1.8 KiB
Python
49 lines
1.8 KiB
Python
from ultralytics import YOLOv10, YOLO
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# from ultralytics.engine.pgt_trainer import PGTTrainer
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# from ultralytics import BaseTrainer
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# from ultralytics.engine.trainer import BaseTrainer
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import os
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from ultralytics.models.yolo.segment import PGTSegmentationTrainer
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# Set CUDA device (only needed for multi-gpu machines)
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os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
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os.environ["CUDA_VISIBLE_DEVICES"] = "4"
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# model = YOLOv10()
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# model = YOLO('yolov8n-seg.yaml').load('yolov8n.pt') # build from YAML and transfer weights
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# model = YOLO()
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# If you want to finetune the model with pretrained weights, you could load the
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# pretrained weights like below
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# model = YOLOv10.from_pretrained('jameslahm/yolov10{n/s/m/b/l/x}')
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# or
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# wget https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10{n/s/m/b/l/x}.pt
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model = YOLOv10('yolov10n.pt', task='segment')
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args = dict(model='yolov10n.pt', data='coco128-seg.yaml')
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trainer = PGTSegmentationTrainer(overrides=args)
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trainer.train(
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# debug=True,
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# args = dict(pgt_coeff=0.1),
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)
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# model.train(
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# # data='coco.yaml',
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# data='coco128-seg.yaml',
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# trainer=model._smart_load("pgt_trainer"), # This is needed to generate attributions (will be used later to train via PGT)
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# # Add return_images as input parameter
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# epochs=500, batch=16, imgsz=640,
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# debug=True, # If debug = True, the attributions will be saved in the figures folder
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# # cfg='/home/nielseni6/PythonScripts/yolov10/ultralytics/cfg/models/v8/yolov8-seg.yaml',
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# # overrides=dict(task="segment"),
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# )
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# Save the trained model
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model.save('yolov10_coco_trained.pt')
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# Evaluate the model on the validation set
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results = model.val(data='coco.yaml')
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# Print the evaluation results
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print(results) |