models vfnet_r50_fpn_mdconv_c3 c5_mstrain_2x_coco
Description: Accurately ranking the vast number of candidate detections is crucial for dense object detectors to achieve high performance. Prior work uses the classification score or a combination of classification and predicted localization scores to rank candidates. However, neither option results in a reliable ranking, thus degrading detection performance. In this paper, we propose to learn an Iou-aware Classification Score (IACS) as a joint representation of object presence confidence and localization accuracy. We show that dense object detectors can achieve a more accurate ranking of candidate detections based on the IACS. We design a new loss function, named Varifocal Loss, to train a dense object detector to predict the IACS, and propose a new star-shaped bounding box feature representation for IACS prediction and bounding box refinement. Combining these two new components and a bounding box refinement branch, we build an IoU-aware dense object detector based on the FCOS+ATSS architecture, that we call VarifocalNet or VFNet for short. Extensive experiments on MS COCO show that our VFNet consistently surpasses the strong baseline by ∼2.0 AP with different backbones. Our best model VFNet-X-1200 with Res2Net-101-DCN achieves a single-model single-scale AP of 55.1 on COCO test-dev, which is state-of-the-art among various object detectors. > The above abstract is from mmdetection website. Review the original-model-card to understand the data used to train the model, evaluation metrics, license, intended uses, limitations and bias before using the model. ### Inference samples Inference type|Python sample (Notebook)|CLI with YAML |--|--|--| Real time|image-object-detection-online-endpoint.ipynb|image-object-detection-online-endpoint.sh Batch |image-object-detection-batch-endpoint.ipynb|image-object-detection-batch-endpoint.sh ### Finetuning samples Task|Use case|Dataset|Python sample (Notebook)|CLI with YAML |---|--|--|--|--| Image object detection|Image object detection|fridgeObjects|fridgeobjects-object-detection.ipynb|fridgeobjects-object-detection.sh ### Model Evaluation |Task|Use case|Dataset|Python sample (Notebook)| |---|--|--|--| Image object detection|Image object detection|fridgeObjects|image-object-detection.ipynb| ### Sample inputs and outputs (for real-time inference) #### Sample input json { "input_data": { "columns": [ "image" ], "index": [0, 1] "data": ["image1", "image2"] } } Note: "image1" and "image2" string should be in base64 format or publicly accessible urls. #### Sample output json [ { "boxes": [ { "box": { "topX": 0.1, "topY": 0.2, "bottomX": 0.8, "bottomY": 0.7 }, "label": "carton", "score": 0.98 } ] }, { "boxes": [ { "box": { "topX": 0.2, "topY": 0.3, "bottomX": 0.6, "bottomY": 0.5 }, "label": "can", "score": 0.97 } ] } ] Note: Please refer to object detection output data schema for more detail. #### Model inference - visualization for a sample image
/plot_vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco_OD.png)
Version: 3
Preview license : apache-2.0 model_specific_defaults : ordereddict([('apply_deepspeed', 'false'), ('apply_ort', 'false')]) task : object-detection
View in Studio: https://ml.azure.com/registries/azureml/models/vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco/version/3
License: apache-2.0
SHA: abb9a8751a5273780160a7cb8121e6146e2fc7ee
datasets: COCO
evaluation-min-sku-spec: 4|1|28|176
evaluation-recommended-sku: Standard_NC6s_v3
finetune-min-sku-spec: 4|1|28|176
finetune-recommended-sku: Standard_NC6s_v3
finetuning-tasks: image-object-detection
inference-min-sku-spec: 2|0|14|28
inference-recommended-sku: Standard_DS3_v2
model_id: vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco