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models vfnet_r50_fpn_mdconv_c3 c5_mstrain_2x_coco
vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco
model is from OpenMMLab's MMDetection library. This model is reported to obtain box AP of 48.0 for object-detection task on COCO dataset. To understand the naming style used, please refer to MMDetection's Config Name Style.
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.
Deprecation Warning: This model is only compatible with mmdet <= 2.28 and is deprecated, will be deleted from Model Catalog by the End of January 2024. We recommend using
mmd-3x-vfnet_r50-mdconv-c3-c5_fpn_ms-2x_coco
from the AzureML model catalog. In our model catalog, the models prefixed with mmdet-3x are compatible with mmdet >= 3.1.0.
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 |
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 |
Task | Use case | Dataset | Python sample (Notebook) |
---|---|---|---|
Image object detection | Image object detection | fridgeObjects | image-object-detection.ipynb |
{
"input_data": {
"columns": [
"image"
],
"index": [0, 1],
"data": ["image1", "image2"]
}
}
Note: "image1" and "image2" string should be in base64 format or publicly accessible urls.
[
{
"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.
Version: 11
Deprecated
SharedComputeCapacityEnabled
openmmlab_model_id : vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco
training_dataset : COCO
license : apache-2.0
model_specific_defaults : ordereddict({'apply_deepspeed': 'false', 'apply_ort': 'false'})
task : object-detection
inference_compute_allow_list : ['Standard_DS3_v2', 'Standard_D4a_v4', 'Standard_D4as_v4', 'Standard_DS4_v2', 'Standard_D8a_v4', 'Standard_D8as_v4', 'Standard_DS5_v2', 'Standard_D16a_v4', 'Standard_D16as_v4', 'Standard_D32a_v4', 'Standard_D32as_v4', 'Standard_D48a_v4', 'Standard_D48as_v4', 'Standard_D64a_v4', 'Standard_D64as_v4', 'Standard_D96a_v4', 'Standard_D96as_v4', 'Standard_FX4mds', 'Standard_F8s_v2', 'Standard_FX12mds', 'Standard_F16s_v2', 'Standard_F32s_v2', 'Standard_F48s_v2', 'Standard_F64s_v2', 'Standard_F72s_v2', 'Standard_FX24mds', 'Standard_FX36mds', 'Standard_FX48mds', 'Standard_E4s_v3', 'Standard_E8s_v3', 'Standard_E16s_v3', 'Standard_E32s_v3', 'Standard_E48s_v3', 'Standard_E64s_v3', 'Standard_NC4as_T4_v3', 'Standard_NC6s_v3', 'Standard_NC8as_T4_v3', 'Standard_NC12s_v3', 'Standard_NC16as_T4_v3', 'Standard_NC24s_v3', 'Standard_NC64as_T4_v3', 'Standard_NC24ads_A100_v4', 'Standard_NC48ads_A100_v4', 'Standard_NC96ads_A100_v4', 'Standard_ND96asr_v4', 'Standard_ND96amsr_A100_v4', 'Standard_ND40rs_v2']
evaluation_compute_allow_list : ['Standard_NC4as_T4_v3', 'Standard_NC6s_v3', 'Standard_NC8as_T4_v3', 'Standard_NC12s_v3', 'Standard_NC16as_T4_v3', 'Standard_NC24s_v3', 'Standard_NC64as_T4_v3', 'Standard_NC96ads_A100_v4', 'Standard_ND96asr_v4', 'Standard_ND96amsr_A100_v4', 'Standard_ND40rs_v2']
finetune_compute_allow_list : ['Standard_NC4as_T4_v3', 'Standard_NC6s_v3', 'Standard_NC8as_T4_v3', 'Standard_NC12s_v3', 'Standard_NC16as_T4_v3', 'Standard_NC24s_v3', 'Standard_NC64as_T4_v3', 'Standard_NC96ads_A100_v4', 'Standard_ND96asr_v4', 'Standard_ND96amsr_A100_v4', 'Standard_ND40rs_v2']
View in Studio: https://ml.azure.com/registries/azureml/models/vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco/version/11
License: apache-2.0
SharedComputeCapacityEnabled: True
SHA: abb9a8751a5273780160a7cb8121e6146e2fc7ee
finetuning-tasks: image-object-detection
finetune-min-sku-spec: 4|1|28|176
finetune-recommended-sku: Standard_NC4as_T4_v3, Standard_NC6s_v3, Standard_NC8as_T4_v3, Standard_NC12s_v3, Standard_NC16as_T4_v3, Standard_NC24s_v3, Standard_NC64as_T4_v3, Standard_NC96ads_A100_v4, Standard_ND96asr_v4, Standard_ND96amsr_A100_v4, Standard_ND40rs_v2
evaluation-min-sku-spec: 4|1|28|176
evaluation-recommended-sku: Standard_NC4as_T4_v3, Standard_NC6s_v3, Standard_NC8as_T4_v3, Standard_NC12s_v3, Standard_NC16as_T4_v3, Standard_NC24s_v3, Standard_NC64as_T4_v3, Standard_NC96ads_A100_v4, Standard_ND96asr_v4, Standard_ND96amsr_A100_v4, Standard_ND40rs_v2
inference-min-sku-spec: 4|0|14|28
inference-recommended-sku: Standard_DS3_v2, Standard_D4a_v4, Standard_D4as_v4, Standard_DS4_v2, Standard_D8a_v4, Standard_D8as_v4, Standard_DS5_v2, Standard_D16a_v4, Standard_D16as_v4, Standard_D32a_v4, Standard_D32as_v4, Standard_D48a_v4, Standard_D48as_v4, Standard_D64a_v4, Standard_D64as_v4, Standard_D96a_v4, Standard_D96as_v4, Standard_FX4mds, Standard_F8s_v2, Standard_FX12mds, Standard_F16s_v2, Standard_F32s_v2, Standard_F48s_v2, Standard_F64s_v2, Standard_F72s_v2, Standard_FX24mds, Standard_FX36mds, Standard_FX48mds, Standard_E4s_v3, Standard_E8s_v3, Standard_E16s_v3, Standard_E32s_v3, Standard_E48s_v3, Standard_E64s_v3, Standard_NC4as_T4_v3, Standard_NC6s_v3, Standard_NC8as_T4_v3, Standard_NC12s_v3, Standard_NC16as_T4_v3, Standard_NC24s_v3, Standard_NC64as_T4_v3, Standard_NC24ads_A100_v4, Standard_NC48ads_A100_v4, Standard_NC96ads_A100_v4, Standard_ND96asr_v4, Standard_ND96amsr_A100_v4, Standard_ND40rs_v2