models bytetrack_yolox_x_crowdhuman_mot17 private half

bytetrack_yolox_x_crowdhuman_mot17-private-half

Overview

bytetrack_yolox_x_crowdhuman_mot17-private-half model is from OpenMMLab's MMTracking library. This model is reported to obtain MOTA: 78.6, IDF1: 79.2 for video-multi-object-tracking task on MOT17-half-eval dataset.

Multi-object tracking (MOT) aims at estimating bounding boxes and identities of objects in videos. Most methods obtain identities by associating detection boxes whose scores are higher than a threshold. The objects with low detection scores, e.g. occluded objects, are simply thrown away, which brings non-negligible true object missing and fragmented trajectories. To solve this problem, we present a simple, effective and generic association method, tracking by associating every detection box instead of only the high score ones. For the low score detection boxes, we utilize their similarities with tracklets to recover true objects and filter out the background detections. When applied to 9 different state-of-the-art trackers, our method achieves consistent improvement on IDF1 score ranging from 1 to 10 points. To put forwards the state-of-the-art performance of MOT, we design a simple and strong tracker, named ByteTrack. For the first time, we achieve 80.3 MOTA, 77.3 IDF1 and 63.1 HOTA on the test set of MOT17 with 30 FPS running speed on a single V100 GPU.

The above abstract is from MMTracking 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	video-multi-object-tracking-online-endpoint.ipynb	video-multi-object-tracking-online-endpoint.sh

Finetuning samples

Task	Use case	Dataset	Python sample (Notebook)	CLI with YAML
Video multi-object tracking	Video multi-object tracking	MOT17 tiny	mot17-tiny-video-multi-object-tracking.ipynb	mot17-tiny-video-multi-object-tracking.sh

Sample inputs and outputs (for real-time inference)

Sample input

{
  "input_data": {
    "columns": [
      "video"
    ],
    "data": ["video_link"]
  }
}

Note: "video_link" should be a publicly accessible url.

Sample output

[
  {
    "det_bboxes": [
      {
        "box": {
          "topX": 703.9149780273,
          "topY": -5.5951070786,
          "bottomX": 756.9875488281,
          "bottomY": 158.1963806152
        },
        "label": 0,
        "score": 0.9597821236
      },
      {
        "box": {
          "topX": 1487.9072265625,
          "topY": 67.9468841553,
          "bottomX": 1541.1591796875,
          "bottomY": 217.5476837158
        },
        "label": 0,
        "score": 0.9568068385
      }
    ],
    "track_bboxes": [
      {
        "box": {
          "instance_id": 0,
          "topX": 703.9149780273,
          "topY": -5.5951070786,
          "bottomX": 756.9875488281,
          "bottomY": 158.1963806152
        },
        "label": 0,
        "score": 0.9597821236
      },
      {
        "box": {
          "instance_id": 1,
          "topX": 1487.9072265625,
          "topY": 67.9468841553,
          "bottomX": 1541.1591796875,
          "bottomY": 217.5476837158
        },
        "label": 0,
        "score": 0.9568068385
      }
    ],
    "frame_id": 0,
    "video_url": "video_link"
  }
]

Model inference - visualization for a video

Version: 4

Tags

Preview license : apache-2.0 model_specific_defaults : ordereddict({'apply_deepspeed': 'false', 'apply_ort': 'false'}) task : multi-object-tracking openmmlab_model_id : bytetrack_yolox_x_crowdhuman_mot17-private-half SharedComputeCapacityEnabled 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_NC24ads_A100_v4', 'Standard_NC48ads_A100_v4', 'Standard_NC96ads_A100_v4', 'Standard_ND96asr_v4', 'Standard_ND96amsr_A100_v4', 'Standard_ND40rs_v2'] inference_compute_allow_list : ['Standard_NC4as_T4_v3', 'Standard_NC6s_v3', 'Standard_NC12s_v3', 'Standard_NC24s_v3', 'Standard_NC16as_T4_v3', 'Standard_NC24ads_A100_v4', 'Standard_NC48ads_A100_v4', 'Standard_NC64as_T4_v3', 'Standard_NC8as_T4_v3', 'Standard_NC96ads_A100_v4', 'Standard_ND40rs_v2', 'Standard_ND96amsr_A100_v4', 'Standard_ND96asr_v4']

View in Studio: https://ml.azure.com/registries/azureml/models/bytetrack_yolox_x_crowdhuman_mot17-private-half/version/4

License: apache-2.0

Properties

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_NC24ads_A100_v4, Standard_NC48ads_A100_v4, Standard_NC96ads_A100_v4, Standard_ND96asr_v4, Standard_ND96amsr_A100_v4, Standard_ND40rs_v2

finetuning-tasks: video-multi-object-tracking

inference-min-sku-spec: 4|1|28|176

inference-recommended-sku: Standard_NC4as_T4_v3, Standard_NC6s_v3, Standard_NC12s_v3, Standard_NC24s_v3, Standard_NC16as_T4_v3, Standard_NC24ads_A100_v4, Standard_NC48ads_A100_v4, Standard_NC64as_T4_v3, Standard_NC8as_T4_v3, Standard_NC96ads_A100_v4, Standard_ND40rs_v2, Standard_ND96amsr_A100_v4, Standard_ND96asr_v4