models yolof_r50_c5_8x8_1x_coco
Description: yolof_r50_c5_8x8_1x_coco model is from OpenMMLab's MMDetection library. This model is reported to obtain box AP of 37.5 for object-detection task on COCO dataset. To understand the naming style used, please refer to MMDetection's Config Name Style. This paper revisits feature pyramids networks (FPN) for one-stage detectors and points out that the success of FPN is due to its divide-and-conquer solution to the optimization problem in object detection rather than multi-scale feature fusion. From the perspective of optimization, we introduce an alternative way to address the problem instead of adopting the complex feature pyramids - {\em utilizing only one-level feature for detection}. Based on the simple and efficient solution, we present You Only Look One-level Feature (YOLOF). In our method, two key components, Dilated Encoder and Uniform Matching, are proposed and bring considerable improvements. Extensive experiments on the COCO benchmark prove the effectiveness of the proposed model. Our YOLOF achieves comparable results with its feature pyramids counterpart RetinaNet while being 2.5× faster. Without transformer layers, YOLOF can match the performance of DETR in a single-level feature manner with 7× less training epochs. With an image size of 608×608, YOLOF achieves 44.3 mAP running at 60 fps on 2080Ti, which is 13% faster than YOLOv4. > 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
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Version: 4
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/yolof_r50_c5_8x8_1x_coco/version/4
License: apache-2.0
SHA: d7734ddf3b7b680440bf025ac90590bb45814462
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: yolof_r50_c5_8x8_1x_coco