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@ -1,21 +1,22 @@
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train:
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epochs: 600
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batch-size: 8
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epochs: 1200
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batch-size: 3
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cfg: ./cfg/yolov3-spp-19cls.cfg
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data: ./data/widok_01_19.data
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multi-scale: false
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img-size: '512 960'
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rect: false
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resume: true
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img-size: '512 1920'
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rect: true
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resume: false
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nosave: false
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notest: false
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evolve: false
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bucket:
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cache-images: false
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weights: /home/tomekb/yolov3/weights/last.pt
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weights: /home/tomekb/yolov3/weights/yolov3-spp-ultralytics.pt
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device: 1
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adam: true
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single-cls: false
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save-every-nth-epoch: 50
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# inne hiperparametry
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other-hyps:
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@ -42,7 +43,7 @@ experiments:
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dir: ./experiments
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detect:
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source: /home/tomekb/yolov3/data/widok_01_19/widok_01_19_test_labels.txt
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test-img-size: 1024
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test-img-size: 1920
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conf-thres: 0.3
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iou-thres: 0.6
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classes:
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6
train.py
6
train.py
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@ -349,12 +349,15 @@ def train(hyp):
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'model': ema.ema.module.state_dict() if hasattr(model, 'module') else ema.ema.state_dict(),
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'optimizer': None if final_epoch else optimizer.state_dict()}
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if epoch % opt.save_every_nth_epoch == 0:
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torch.save(chkpt, f'yolo_{epoch}.pt')
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# Save last, best and delete
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torch.save(chkpt, last)
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if (best_fitness == fi) and not final_epoch:
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torch.save(chkpt, best)
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del chkpt
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# end epoch ----------------------------------------------------------------------------------------------------
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# end training
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@ -397,9 +400,10 @@ if __name__ == '__main__':
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parser.add_argument('--device', default='', help='device id (i.e. 0 or 0,1 or cpu)')
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parser.add_argument('--adam', action='store_true', help='use adam optimizer')
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parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset')
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parser.add_argument('--save-every-nth-epoch', type=int, help='Saving every n-th epoth')
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opt = parser.parse_args()
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#opt.weights = last if opt.resume else opt.weights
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check_git_status()
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#check_git_status()
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opt.cfg = check_file(opt.cfg) # check file
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opt.data = check_file(opt.data) # check file
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#print(opt)
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