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general-image-detector-detic_C2_SwinB-21K_COCO

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Notes

Detecting Twenty-thousand Classes using Image-level Supervision

Detic: A Detector with image classes that can use image-level labels to easily train detectors.

Detecting Twenty-thousand Classes using Image-level Supervision,
Xingyi Zhou, Rohit Girdhar, Armand Joulin, Philipp Krähenbühl, Ishan Misra,
ECCV 2022 (arXiv 2201.02605)

Features

  • Detects any class given class names (using CLIP).

  • We train the detector on ImageNet-21K dataset with 21K classes.

  • Cross-dataset generalization to OpenImages and Objects365 without finetuning.

  • State-of-the-art results on Open-vocabulary LVIS and Open-vocabulary COCO.

Detic_C2_SwinB_896_4x_IN-21K+COCO Performance

NameTraining timeObjects365 box mAPOpenImages box mAP50
Box-Supervised_C2_SwinB_896_4x43h19.146.2
Detic_C2_SwinB_896_4x47h21.253.0
Detic_C2_SwinB_896_4x_IN-21K47h21.455.2
Box-Supervised_C2_SwinB_896_4x+COCO43h19.746.4
Detic_C2_SwinB_896_4x_IN-21K+COCO47h21.654.6

Note

  • Box-Supervised_C2_SwinB_896_4x and Detic_C2_SwinB_896_4x are the same model in the Standard LVIS section, but evaluated with Objects365/ OpenImages vocabulary (i.e. CLIP embeddings of the corresponding class names as classifier).
  • Detic_C2_SwinB_896_4x_IN-21K trains on the full ImageNet-22K. We additionally use a dynamic class sampling ("Modified Federated Loss" in Section 4.4) and use a larger data sampling ratio of ImageNet images (1:16 instead of 1:4).

  • Detic_C2_SwinB_896_4x_IN-21K-COCO is a model trained on combined LVIS-COCO and ImageNet-21K for better demo purposes. LVIS models do not detect persons well due to its federated annotation protocol. LVIS+COCO models give better visual results.

  • ID
  • Name
    general-image-detector-detic_C2_SwinB-21K_COCO
  • Model Type ID
    Visual Detector
  • Description
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  • Last Updated
    Aug 29, 2022
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