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Deci-AI
/
super-gradients
connected to https://github.com/Deci-AI/super-gradients.git

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Issues 62 Pull Requests 44 Discussions 0

pretrained_models.py 6.5 KB
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  1. # TODO: It would be nice to create keys here as: make_pretrained_model_key(Models.RESNET18, Dataset.COCO)
    2. 

  2. # TODO: Not only this would reduce risk of making a typo error, it would bring more clarity how the key is created
    3. 

  3. # TODO: And allow to "query" pretrained models by dataset
    4. 

  4. MODEL_URLS = {
    5. 

  5.     "regnetY800_imagenet": "https://sg-hub-nv.s3.amazonaws.com/models/regnetY800_imagenet.pth",
    6. 

  6.     "regnetY600_imagenet": "https://sg-hub-nv.s3.amazonaws.com/models/regnetY600_imagenet.pth",
    7. 

  7.     "regnetY400_imagenet": "https://sg-hub-nv.s3.amazonaws.com/models/regnetY400_imagenet.pth",
    8. 

  8.     "regnetY200_imagenet": "https://sg-hub-nv.s3.amazonaws.com/models/regnetY200_imagenet.pth",
    9. 

  9.     "resnet50_imagenet": "https://sg-hub-nv.s3.amazonaws.com/models/resnet50_imagenet.pth",
    10. 

  10.     "resnet34_imagenet": "https://sg-hub-nv.s3.amazonaws.com/models/resnet34_imagenet.pth",
    11. 

  11.     "resnet18_imagenet": "https://sg-hub-nv.s3.amazonaws.com/models/resnet18_imagenet.pth",
    12. 

  12.     "repvgg_a0_imagenet": "https://sg-hub-nv.s3.amazonaws.com/models/repvgg_a0_imagenet.pth",
    13. 

  13.     "shelfnet34_lw_coco_segmentation_subclass": "https://sg-hub-nv.s3.amazonaws.com/models/shelfnet34_lw_coco_segmentation_subclass.pth",
    14. 

  14.     "ddrnet_23_cityscapes": "https://sg-hub-nv.s3.amazonaws.com/models/ddrnet_23_cityscapes.pth",
    15. 

  15.     "ddrnet_23_slim_cityscapes": "https://sg-hub-nv.s3.amazonaws.com/models/ddrnet_23_slim_cityscapes.pth",
    16. 

  16.     "ddrnet_39_cityscapes": "https://sg-hub-nv.s3.amazonaws.com/models/ddrnet_39_cityscapes.pth",
    17. 

  17.     "stdc1_seg50_cityscapes": "https://sg-hub-nv.s3.amazonaws.com/models/stdc1_seg50_cityscapes.pth",
    18. 

  18.     "stdc1_seg75_cityscapes": "https://sg-hub-nv.s3.amazonaws.com/models/stdc1_seg75_cityscapes.pth",
    19. 

  19.     "stdc2_seg50_cityscapes": "https://sg-hub-nv.s3.amazonaws.com/models/stdc2_seg50_cityscapes.pth",
    20. 

  20.     "stdc2_seg75_cityscapes": "https://sg-hub-nv.s3.amazonaws.com/models/stdc2_seg75_cityscapes.pth",
    21. 

  21.     "efficientnet_b0_imagenet": "https://sg-hub-nv.s3.amazonaws.com/models/efficientnet_b0_imagenet.pth",
    22. 

  22.     "ssd_lite_mobilenet_v2_coco": "https://sg-hub-nv.s3.amazonaws.com/models/ssd_lite_mobilenet_v2_coco.pth",
    23. 

  23.     "ssd_mobilenet_v1_coco": "https://sg-hub-nv.s3.amazonaws.com/models/ssd_mobilenet_v1_coco.pth",
    24. 

  24.     "mobilenet_v3_large_imagenet": "https://sg-hub-nv.s3.amazonaws.com/models/mobilenet_v3_large_imagenet.pth",
    25. 

  25.     "mobilenet_v3_small_imagenet": "https://sg-hub-nv.s3.amazonaws.com/models/mobilenet_v3_small_imagenet.pth",
    26. 

  26.     "mobilenet_v2_imagenet": "https://sg-hub-nv.s3.amazonaws.com/models/mobilenet_v2_imagenet.pth",
    27. 

  27.     "regseg48_cityscapes": "https://sg-hub-nv.s3.amazonaws.com/models/regseg48_cityscapes.pth",
    28. 

  28.     "vit_base_imagenet21k": "https://sg-hub-nv.s3.amazonaws.com/models/vit_base_imagenet21k.pth",
    29. 

  29.     "vit_large_imagenet21k": "https://sg-hub-nv.s3.amazonaws.com/models/vit_large_imagenet21k.pth",
    30. 

  30.     "vit_base_imagenet": "https://sg-hub-nv.s3.amazonaws.com/models/vit_base_imagenet.pth",
    31. 

  31.     "vit_large_imagenet": "https://sg-hub-nv.s3.amazonaws.com/models/vit_large_imagenet.pth",
    32. 

  32.     "beit_base_patch16_224_imagenet": "https://sg-hub-nv.s3.amazonaws.com/models/beit_base_patch16_224_imagenet.pth",
    33. 

  33.     "beit_base_patch16_224_cifar10": "https://sg-hub-nv.s3.amazonaws.com/models/beit_base_patch16_224_cifar10.pth",
    34. 

  34.     "yolox_s_coco": "https://sg-hub-nv.s3.amazonaws.com/models/yolox_s_coco.pth",
    35. 

  35.     "yolox_m_coco": "https://sg-hub-nv.s3.amazonaws.com/models/yolox_m_coco.pth",
    36. 

  36.     "yolox_l_coco": "https://sg-hub-nv.s3.amazonaws.com/models/yolox_l_coco.pth",
    37. 

  37.     "yolox_t_coco": "https://sg-hub-nv.s3.amazonaws.com/models/yolox_t_coco.pth",
    38. 

  38.     "yolox_n_coco": "https://sg-hub-nv.s3.amazonaws.com/models/yolox_n_coco.pth",
    39. 

  39.     "pp_lite_t_seg50_cityscapes": "https://sg-hub-nv.s3.amazonaws.com/models/pp_lite_t_seg50_cityscapes.pth",
    40. 

  40.     "pp_lite_t_seg75_cityscapes": "https://sg-hub-nv.s3.amazonaws.com/models/pp_lite_t_seg75_cityscapes.pth",
    41. 

  41.     "pp_lite_b_seg50_cityscapes": "https://sg-hub-nv.s3.amazonaws.com/models/pp_lite_b_seg50_cityscapes.pth",
    42. 

  42.     "pp_lite_b_seg75_cityscapes": "https://sg-hub-nv.s3.amazonaws.com/models/pp_lite_b_seg75_cityscapes.pth",
    43. 

  43.     "ppyoloe_s_coco": "https://sg-hub-nv.s3.amazonaws.com/models/ppyoloe_s_coco.pth",
    44. 

  44.     "ppyoloe_m_coco": "https://sg-hub-nv.s3.amazonaws.com/models/ppyoloe_m_coco.pth",
    45. 

  45.     "ppyoloe_l_coco": "https://sg-hub-nv.s3.amazonaws.com/models/ppyoloe_l_coco.pth",
    46. 

  46.     "ppyoloe_x_coco": "https://sg-hub-nv.s3.amazonaws.com/models/ppyoloe_x_coco.pth",
    47. 

  47.     "yolo_nas_s_coco": "https://sg-hub-nv.s3.amazonaws.com/models/yolo_nas_s_coco.pth",
    48. 

  48.     "yolo_nas_m_coco": "https://sg-hub-nv.s3.amazonaws.com/models/yolo_nas_m_coco.pth",
    49. 

  49.     "yolo_nas_l_coco": "https://sg-hub-nv.s3.amazonaws.com/models/yolo_nas_l_coco.pth",
    50. 

  50.     "dekr_w32_no_dc_coco_pose": "https://sg-hub-nv.s3.amazonaws.com/models/dekr_w32_no_dc_coco_pose.pth",
    51. 

  51.     "pose_rescoring_coco_coco_pose": "https://sg-hub-nv.s3.amazonaws.com/models/pose_rescoring_coco_coco_pose.pth",
    52. 

  52.     "segformer_b0_cityscapes": "https://sg-hub-nv.s3.amazonaws.com/models/segformer_b0_cityscapes.pth",
    53. 

  53.     "segformer_b1_cityscapes": "https://sg-hub-nv.s3.amazonaws.com/models/segformer_b1_cityscapes.pth",
    54. 

  54.     "segformer_b2_cityscapes": "https://sg-hub-nv.s3.amazonaws.com/models/segformer_b2_cityscapes.pth",
    55. 

  55.     "segformer_b3_cityscapes": "https://sg-hub-nv.s3.amazonaws.com/models/segformer_b3_cityscapes.pth",
    56. 

  56.     "segformer_b4_cityscapes": "https://sg-hub-nv.s3.amazonaws.com/models/segformer_b4_cityscapes.pth",
    57. 

  57.     "segformer_b5_cityscapes": "https://sg-hub-nv.s3.amazonaws.com/models/segformer_b5_cityscapes.pth",
    58. 

  58.     "yolo_nas_pose_n_coco_pose": "https://sg-hub-nv.s3.amazonaws.com/models/yolo_nas_pose_n_coco_pose.pth",
    59. 

  59.     "yolo_nas_pose_s_coco_pose": "https://sg-hub-nv.s3.amazonaws.com/models/yolo_nas_pose_s_coco_pose.pth",
    60. 

  60.     "yolo_nas_pose_m_coco_pose": "https://sg-hub-nv.s3.amazonaws.com/models/yolo_nas_pose_m_coco_pose.pth",
    61. 

  61.     "yolo_nas_pose_l_coco_pose": "https://sg-hub-nv.s3.amazonaws.com/models/yolo_nas_pose_l_coco_pose.pth",
    62. 

  62. }
    63. 

  63. 

  64. PRETRAINED_NUM_CLASSES = {
    65. 

  65.     "imagenet": 1000,
    66. 

  66.     "imagenet21k": 21843,
    67. 

  67.     "coco_segmentation_subclass": 21,
    68. 

  68.     "cityscapes": 19,
    69. 

  69.     "coco": 80,
    70. 

  70.     "coco_pose": 17,
    71. 

  71.     "cifar10": 10,
    72. 

  72. }
    73. 

  73. 

  74. DATASET_LICENSES = {
    75. 

  75.     "imagenet": "https://www.image-net.org/download.php",
    76. 

  76.     "imagenet21k": "https://github.com/Alibaba-MIIL/ImageNet21K",
    77. 

  77.     "coco": "https://cocodataset.org/#termsofuse",
    78. 

  78.     "coco_segmentation_subclass": "https://cocodataset.org/#termsofuse",
    79. 

  79.     "coco_pose": "https://cocodataset.org/#termsofuse",
    80. 

  80.     "cityscapes": "https://www.cs.toronto.edu/~kriz/cifar.html",
    81. 

  81.     "objects365": "https://www.objects365.org/download.html",
    82. 

  82. }
    83.
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