This model is fine-tuned based on MLPerf Inference BERT PyTorch Model on SQuAD v1.1 dataset and converted to ONNX using the script in MLPerf inference repo: https://github.com/mlperf/inference The quantization method is: per-tensor, symmetric, zero_point=0. It uses ONNX QuantizeLinear and DequantizeLinear to achieve the quantization. Achieved accuracy is f1_score=90.482%. The description for fine-tuning step will be added
Application: Question & Answering ML Task: MobileBERT Framework: ONNX Training information: sourc...
Application: Single-stage Object Detection Base Model: Retinanet with Resnext50 backbone Framework...
3D-Unet PyTorch model trained on BraTS 2019 dataset (fold 1) for MLPerf Inference. See MLPerf Infere...
This model is fine-tuned based on MLPerf Inference BERT PyTorch Model on SQuAD v1.1 dataset and conv...
This model is finetuned and quantized based on a pretrained huggingface BERT model. The quantizatio...
This model is converted from the MLPerf Inference BERT Tensorflow Model on SQuAD v1.1 dataset using ...
This model is converted from the MLPerf Inference BERT Tensorflow Model on SQuAD v1.1 dataset using ...
BERT TensorFlow model trained on SQuAD v1.1 for MLPerf Inference. To re-create the model, train on S...
This model is frozen from official MLPerf inference benchmark BERT model at this zenodo link with ba...
Please read the readme.txt in the zip file for more information. There is no accuracy validation do...
BERT Large. { "hidden-size": 1024, "num-attention-heads": 16, "num-layers": 24, "max-seq-length": 3...
This 3D-Unet ONNX model is converted from https://zenodo.org/record/3904106. See MLPerf Inference Gi...
Application: Single-stage Object Detection Base Model: Retinanet with Resnext50 backbone Framework...
Application: Object Detection ML Task: ssd-resnet34 Framework: onnx Training Information: Quali...
Transformer based architectures have become de-facto models used for a range of Natural Language Pro...
Application: Question & Answering ML Task: MobileBERT Framework: ONNX Training information: sourc...
Application: Single-stage Object Detection Base Model: Retinanet with Resnext50 backbone Framework...
3D-Unet PyTorch model trained on BraTS 2019 dataset (fold 1) for MLPerf Inference. See MLPerf Infere...
This model is fine-tuned based on MLPerf Inference BERT PyTorch Model on SQuAD v1.1 dataset and conv...
This model is finetuned and quantized based on a pretrained huggingface BERT model. The quantizatio...
This model is converted from the MLPerf Inference BERT Tensorflow Model on SQuAD v1.1 dataset using ...
This model is converted from the MLPerf Inference BERT Tensorflow Model on SQuAD v1.1 dataset using ...
BERT TensorFlow model trained on SQuAD v1.1 for MLPerf Inference. To re-create the model, train on S...
This model is frozen from official MLPerf inference benchmark BERT model at this zenodo link with ba...
Please read the readme.txt in the zip file for more information. There is no accuracy validation do...
BERT Large. { "hidden-size": 1024, "num-attention-heads": 16, "num-layers": 24, "max-seq-length": 3...
This 3D-Unet ONNX model is converted from https://zenodo.org/record/3904106. See MLPerf Inference Gi...
Application: Single-stage Object Detection Base Model: Retinanet with Resnext50 backbone Framework...
Application: Object Detection ML Task: ssd-resnet34 Framework: onnx Training Information: Quali...
Transformer based architectures have become de-facto models used for a range of Natural Language Pro...
Application: Question & Answering ML Task: MobileBERT Framework: ONNX Training information: sourc...
Application: Single-stage Object Detection Base Model: Retinanet with Resnext50 backbone Framework...
3D-Unet PyTorch model trained on BraTS 2019 dataset (fold 1) for MLPerf Inference. See MLPerf Infere...