MAX_ITER: 90000 #iteration 在启用detectron2训练代码时会用以下命令 python tools/train_net.py --num-gpus 2 --config-file configs/xxxxx.yaml SOLVER.IMS_PER_BATCH 2 SOLVER.BASE_LR 0.0025 其中随着gpus数量的更改,batch_size和lr也应该随之更改,不然显存带不动,那么实验结果会和原论文自然也会不一致,那...
MAX_ITER:最大迭代次数; BASE_LR:基准学习率; IMS_PER_BATCH:批处理大小batch_size; CLIP_GRADIENTS:这个可以选一下,防止训练过程中梯度过大导致无法训练; 输入图片增强 控制一下MIN_SIZE_TRAIN(最小训练尺寸,一个列表,可以设置为多个值)和MAX_SIZE_TRAIN(最大训练尺寸) 验证测试设置 EVAL_PERIOD:每多少个iter...
self._hooks.extend(hooks) def train(self, start_iter: int, max_iter: int): with EventStorage(start_iter) as self.storage: try: self.before_train() for self.iter in range(start_iter, max_iter): self.before_step() self.run_step() self.after_step() # self.iter == max_iter can ...
# STEPS: (210000, 250000) # MAX_ITER: 270000 CHECKPOINT_PERIOD: 1000 TEST: EVAL_PERIOD: 3000 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 如需修改配置,在该文件中修改就好了。 Train & Eval 控制训练和测试的命令行代码置于 shell 文件中更优雅和容易控制。
__init__(checkpointer:Any, period:int, max_iter:int=None, max_to_keep:int=None) 参数: checkpointer(Any):用于保存的checkpointer对象 checkpoints period(int):保存检查点的时间段。 max_iter(int):最大迭代次数。到达后,将保存一个名为"model_final"的检查点。
Detectron2中的迭代次数可以通过配置项`SOLVER.MAX_ITER`来设置。 4.批量大小 批量大小是指每次输入模型的样本数量,在训练过程中需要根据硬件设备的内存大小合理设置。Detectron2中的批量大小可以通过配置项`SOLVER.IMS_PER_BATCH`和`TEST.IMS_PER_BATCH`来设置。 四、推理相关参数 在模型训练完成后,需要将模型用于...
MAX_ITER: 270000 MOMENTUM: 0.9 NESTEROV: False STEPS: (210000, 250000) WARMUP_FACTOR: 0.001 WARMUP_ITERS: 1000 WARMUP_METHOD: linear WEIGHT_DECAY: 0.0001 WEIGHT_DECAY_BIAS: 0.0001 WEIGHT_DECAY_NORM: 0.0 TEST: AUG: ENABLED: False
cfg.SOLVER.MAX_ITER = 1500 #No. of iterations cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 256 cfg.MODEL.ROI_HEADS.NUM_CLASSES = 3 # No. of classes = [HINDI, ENGLISH, OTHER] cfg.TEST.EVAL_PERIOD = 500 # No. of iterations after which the Validation Set is evaluated. ...
cfg.SOLVER.MAX_ITER = 300 cfg.SOLVER.STEPS = [] cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 128 # (default: 512) cfg.MODEL.ROI_HEADS.NUM_CLASSES = 1 # only has one class (ballon) os.makedirs(cfg.OUTPUT_DIR, exist_ok=True) trainer = DefaultTrainer(cfg) trainer.resume_or_load...
cfg.SOLVER.IMS_PER_BATCH=2cfg.SOLVER.BASE_LR=0.02cfg.SOLVER.MAX_ITER=(300)#300iterations seems good enough,but you can certainly train longer cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE=(128)# faster,and good enoughforthistoy dataset