output = F.sigmoid(output) predict=torch.where(output>0.5,torch.ones_like(output),torch.zeros_like(output)) 第二种输出是多通道,即网络的输出output为 [batch_size, num_class, height, width] 形状。其中batch_szie为批量大小,num_class表示输出的通道数与分类数量一致,height和width与输入图像的高和宽...
predict_y = torch.max(outputs, dim=1)[1] # 以output中值最大位置对应的索引(标签)作为预测输出 accuracy = (predict_y == test_label).sum().item() / test_label.size(0) print('[%d, %5d] train_loss: %.3f test_accuracy: %.3f' % # 打印epoch,step,loss,accuracy (epoch + 1, step ...
output=net(input)# net的最后一层没有使用sigmoid output=F.sigmoid(output)predict=torch.where(output>0.5,torch.ones_like(output),torch.zeros_like(output)) 第二种输出是多通道,即网络的输出output为 [batch_size, num_class, height, width] 形状。其中batch_szie为批量大小,num_class表示输出的通道数...
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we propose a Detail Aggregation module by integrating the learning of spatial information into low-level layers in single-stream manner. Finally, the low-level features and deep features are fused to predict the final segmentation results. Extensive experiments on Cityscapes and CamVid dataset demonstra...
#n = np.random.choice(len(test_dataset)) n = i image_vis = test_dataset_vis[n][0].astype('uint8') image, gt_mask = test_dataset[n] gt_mask = gt_mask.squeeze() x_tensor = torch.from_numpy(image).to(DEVICE).unsqueeze(0) pr_mask = best_model.predict(x_tensor) pr_mask =...
pr_mask = best_model.predict(x_tensor) pr_mask = (pr_mask.squeeze().cpu().numpy()) pr_mask = (np.argmax(pr_mask, axis=0) * 255 / (pr_mask.shape[0])).astype(np.uint8) # 恢复图片原来的分辨率 gt_mask = cv2.resize(gt_mask, (480, 360)) ...
【摘要】 @[toc] 摘要segmentation_models_pytorch是一款非常优秀的图像分割库,albumentations 是一款非常优秀的图像增强库,这篇文章将这两款优秀结合起来实现多类别的图像分割算法。数据集选用CamVid数据集,类别有:‘sky’, ‘building’, ‘pole’, ‘road’, ‘pavement’,‘tree’, ‘signsymbol’, ‘fen... ...
sigmoid: for the binary segmentation tasks, you better go with the sigmoid and predict one heat map while the softmax must be used for multi-class segmentation tasks. However the softmax can be used for binary segmentation tasks when the number of classes K is set to 2, but this is a ...
Name Last commit message Last commit date Latest commit xiaoyufenfei update May 10, 2020 0f0c32e·May 10, 2020 History 33 Commits builders dataset docs model tools utils .gitignore LICENSE README.md predict.py requirements.txt test.py