https://pytorch.org/vision/stable/models/generated/torchvision.models.detection.fasterrcnn_resnet50_fpn.html?highlight=models#torchvision.models.detection.fasterrcnn_resnet50_fpn 导入相关的包 """ @Author : Keep_T
-1, anchors.shape[-1]).repeat(batch_size, 1, 1) # (batch_size, 248*216, 7) box_preds = box_preds.view(batch_size, -1, box_preds.shape[-1] // self.num_anchors_per_location if not self.use_multihead else box_preds.shape[-1]) # 对角度进行处理sin...
Microsoft Vision Model ResNet-50 leverages multi-task learning and optimizes separately for four datasets, including ImageNet-22k (opens in new tab), Microsoft COCO (opens in new tab), and two web-supervised (opens in new tab) datasets containing 40...
For the ResNet34 and ResNet 50 network training, we used the FAST AI library built on top of PyTorch. All other processing and analysis were performed on the DNN and ResnNets using NumPy, OpenCV, Scikit-learn, and other open-source tools. The training was conducted on a 32 GB ...
norm 参数是 clip 自己训的,不是 pytorch 标准的 norm 参数。 视频的模型共有四种,具体为这四种, 但似乎主要都在用 ViT-B/32。 CLIP 使用了一种图片和文本分别进行编码,最后计算相似度的经典双流结构。 图片侧使用 Resnet 50 或者 Vit,文本编码器使用了 Tranformer 结构,未用到预训练的知识,看起来是从头...
(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(logits, labels) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return ImageClassifierOutput( loss=...
They designed a multi-layer self-attention system incorporated into a Bidirectional Long-term Short-Term Memory (Bi-LSTM) model. While effective, Bi-LSTMs can struggle with long-range dependencies in text, which newer models like DeBERTa address through more advanced attention mechanisms. A ...
Microsoft Vision Model ResNet-50 leverages multi-task learning and optimizes separately for four datasets, including ImageNet-22k (opens in new tab), Microsoft COCO (opens in new tab), and two web-supervised (opens in new tab) datase...