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sparse_rcnn_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco

Overview

sparse_rcnn_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco model is from OpenMMLab's MMDetection library. This model is reported to obtain box AP of 46.2 for object-detection task on COCO dataset. To understand the naming style used, please refer to MMDetection's Config Name Style.

We present Sparse R-CNN, a purely sparse method for object detection in images. Existing works on object detection heavily rely on dense object candidates, such as k anchor boxes pre-defined on all grids of image feature map of size H×W. In our method, however, a fixed sparse set of learned object proposals, total length of N, are provided to object recognition head to perform classification and location. By eliminating HWk (up to hundreds of thousands) hand-designed object candidates to N (e.g. 100) learnable proposals, Sparse R-CNN completely avoids all efforts related to object candidates design and many-to-one label assignment. More importantly, final predictions are directly output without non-maximum suppression post-procedure. Sparse R-CNN demonstrates accuracy, run-time and training convergence performance on par with the well-established detector baselines on the challenging COCO dataset, e.g., achieving 45.0 AP in standard 3× training schedule and running at 22 fps using ResNet-50 FPN model. We hope our work could inspire re-thinking the convention of dense prior in 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-sparse-rcnn_r101_fpn_300-proposals_crop-ms-480-800-3x_coco from the AzureML model catalog. In our model catalog, the models prefixed with mmdet-3x are compatible with mmdet >= 3.1.0.

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

{
  "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

[
    {
        "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

od visualization

Version: 11

Tags

Deprecated SharedComputeCapacityEnabled openmmlab_model_id : sparse_rcnn_r101_fpn_300_proposals_crop_mstrain_480-800_3x_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/sparse_rcnn_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco/version/11

License: apache-2.0

Properties

SharedComputeCapacityEnabled: True

SHA: 11f3ca2ba61b01e84e425bca2b8c6109e525ae67

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

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