#一般流程: loss_fn=CrossEntropyLoss() #定义损失函数 optimizer=torch.optim.Adam(model.classifier.parameters(),lr=0.001) #定义优化器(调整参数)和设定学习率 model.train() for i,(img_tensor,label) in enumerate(tqdm(train_loader,desc=f'第{epoch+1}轮训练开始')): img_tensor=img_tensor.to(devi...
"loss", "loss_box_reg", "loss_classifier", "loss_objectness", "loss_rpn_box_reg" ] ), meters.get_metric(metric_names=["time", "data"]), ], iteration, ) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 还需要给meters添加get_metric方法,这个比较简单,...
Whereas when I use the class ID set to 1, I have the following error: InvalidArgumentError (see above for traceback): Incompatible shapes: [1,62,4] vs. [1,64,4] [[Node: gradients/Loss/BoxClassifierLoss/Loss/sub_grad/BroadcastGradientArgs = BroadcastGradientArgs[T=DT_INT32, _device="...
{'loss_classifier': tensor(nan, device='cuda:0', grad_fn=<NllLossBackward>), 'loss_box_reg': tensor(nan, device='cuda:0', grad_fn=<DivBackward0>), 'loss_objectness': tensor(nan, device='cuda:0', grad_fn=<BinaryCrossEntropyWithLogitsBackward>), 'loss_rpn_box_reg': tensor(inf,...
2. Related Work Classic Object Detectors: The sliding-window paradigm, in which a classifier is applied on a dense image grid, has a long and rich history. One of the earliest successes is the classic work of LeCun et al. who applied convolutional neu- ral networks to handwritten digit ...
That is, positive samples are far less than negative samples and the majority of negative samples belong to easy train- ing data, which contribute little to classifier learning. Despite the pertinence of data imbalance in regression learning as well, we note that current one-stage regression ...
reg_delta=paddle.gather(p_box_flatten,fg_inds) reg_target=paddle.gather(t_box_flatten,fg_inds) else: reg_delta=paddle.to_tensor([0,0,0,0],dtype='float32') reg_delta.stop_gradient=False reg_target=paddle.to_tensor([0,0,0,0],dtype='float32') ...
measurements were translated into reflectance values for radiometric correction. Using the Earth Resource Data Analysis System (ERDAS) IMAGINE 2014 software, all-time series satellite pictures were removed or subset to encompass only the watershed territory. Thus, the maximum likelihood classifier for supe...
(2) is converted to the inner product be- tween quantum states of the positive sample pair (xai , xpi ), which can be got by the method of the Hadamard classifier [12]. The triplet loss function can be viewed as the weighted sum of the inner product of sample pairs (xai , xpi )...
The results of this study show that the proposed classifier and most of the existing techniques for feature extraction and classification combine SIFT and tensor features. Yan and Xu [66] proposed a straight-through pipeline for video caption detection. To recognize video subtitles, the Connected ...