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

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#378 Feature/sg 281 add kd notebook

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Ghost merged 1 commits into Deci-AI:master from deci-ai:feature/SG-281-add_kd_notebook
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super-gradients
/
src
/
super_gradients
/
training
/
pretrained_models.py
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  1. MODEL_URLS = {"regnetY800_imagenet": "https://deci-pretrained-models.s3.amazonaws.com/RegnetY800/average_model.pth",
    2. 

  2.               "regnetY600_imagenet": "https://deci-pretrained-models.s3.amazonaws.com/RegnetY600/average_model_regnety600.pth",
    3. 

  3.               "regnetY400_imagenet": "https://deci-pretrained-models.s3.amazonaws.com/RegnetY400/average_model_regnety400.pth",
    4. 

  4.               "regnetY200_imagenet": "https://deci-pretrained-models.s3.amazonaws.com/RegnetY200/average_model_regnety200.pth",
    5. 

  5. 

  6.               "resnet50_imagenet": "https://deci-pretrained-models.s3.amazonaws.com/KD_ResNet50_Beit_Base_ImageNet/resnet.pth",
    7. 

  7.               "resnet34_imagenet": "https://deci-pretrained-models.s3.amazonaws.com/resent_34/average_model.pth",
    8. 

  8.               "resnet18_imagenet": "https://deci-pretrained-models.s3.amazonaws.com/resnet18/average_model.pth",
    9. 

  9. 

  10.               "repvgg_a0_imagenet": "https://deci-pretrained-models.s3.amazonaws.com/repvgg_a0_imagenet.pth",
    11. 

  11. 

  12.               "shelfnet34_lw_coco_segmentation_subclass": "https://deci-pretrained-models.s3.amazonaws.com"
    13. 

  13.                                                           "/shelfnet34_coco_segmentation_subclass.pth",
    14. 

  14. 

  15.               "ddrnet_23_cityscapes": "https://deci-pretrained-models.s3.amazonaws.com/ddrnet/cityscapes/ddrnet23/average_model.pth",
    16. 

  16.               "ddrnet_23_slim_cityscapes": "https://deci-pretrained-models.s3.amazonaws.com/ddrnet/cityscapes/ddrnet23_slim/average_model.pth",
    17. 

  17.               "stdc1_seg50_cityscapes": "https://deci-pretrained-models.s3.amazonaws.com/cityscapes_stdc1_seg50_dice_edge/ckpt_best.pth",
    18. 

  18.               "stdc1_seg75_cityscapes": "https://deci-pretrained-models.s3.amazonaws.com/stdc1_seg75_cityscapes/ckpt_best.pth",
    19. 

  19.               "stdc2_seg50_cityscapes": "https://deci-pretrained-models.s3.amazonaws.com/cityscapes_stdc2_seg50_dice_edge/ckpt_best.pth",
    20. 

  20.               "stdc2_seg75_cityscapes": "https://deci-pretrained-models.s3.amazonaws.com/stdc2_seg75_cityscapes/ckpt_best.pth",
    21. 

  21.               "efficientnet_b0_imagenet": "https://deci-pretrained-models.s3.amazonaws.com/efficientnet_b0/average_model-3.pth",
    22. 

  22.               "ssd_lite_mobilenet_v2_coco":
    23. 

  23.                   "https://deci-pretrained-models.s3.amazonaws.com/"
    24. 

  24.                   "ssd_lite_mobilenet_v2_coco_res320_new_coco_filtered_affine_scale_5_15_no_mosaic/ckpt_best.pth",
    25. 

  25.               "ssd_mobilenet_v1_coco": "https://deci-pretrained-models.s3.amazonaws.com/ssd_mobilenet_v1_coco_res320/ckpt_best.pth",
    26. 

  26. 

  27.               "mobilenet_v3_large_imagenet": "https://deci-pretrained-models.s3.amazonaws.com/mobilenetv3+large+300epoch/average_model.pth",
    28. 

  28.               "mobilenet_v3_small_imagenet": "https://deci-pretrained-models.s3.amazonaws.com/mobilenetv3+small/ckpt_best.pth",
    29. 

  29.               "mobilenet_v2_imagenet": "https://deci-pretrained-models.s3.amazonaws.com/mobilenetv2+w1/ckpt_best.pth",
    30. 

  30. 

  31.               "regseg48_cityscapes": "https://deci-pretrained-models.s3.amazonaws.com/regseg48_cityscapes/ckpt_best.pth",
    32. 

  32.               "vit_base_imagenet21k": "https://deci-pretrained-models.s3.amazonaws.com/vit_pretrained_imagenet21k/vit_base_16_imagenet21K.pth",
    33. 

  33.               "vit_large_imagenet21k": "https://deci-pretrained-models.s3.amazonaws.com/vit_pretrained_imagenet21k/vit_large_16_imagenet21K.pth",
    34. 

  34.               "vit_base_imagenet": "https://deci-pretrained-models.s3.amazonaws.com/vit_base_imagenet1k/ckpt_best.pth",
    35. 

  35.               "vit_large_imagenet": "https://deci-pretrained-models.s3.amazonaws.com/vit_large_cutmix_randaug_v2_lr%3D0.03/average_model.pth",
    36. 

  36.               "beit_base_patch16_224_imagenet": 'https://deci-pretrained-models.s3.amazonaws.com/beit_base_patch16_224_imagenet.pth',
    37. 

  37.               "beit_base_patch16_224_cifar10": 'https://deci-pretrained-models.s3.amazonaws.com/beit_cifar10.pth',
    38. 

  38.               "yolox_s_coco": "https://deci-pretrained-models.s3.amazonaws.com/yolox_coco/yolox_s_coco/average_model.pth",
    39. 

  39.               "yolox_m_coco": "https://deci-pretrained-models.s3.amazonaws.com/yolox_coco/yolox_m_coco/average_model.pth",
    40. 

  40.               "yolox_l_coco": "https://deci-pretrained-models.s3.amazonaws.com/yolox_coco/yolox_l_coco/average_model.pth",
    41. 

  41.               "yolox_t_coco": "https://deci-pretrained-models.s3.amazonaws.com/yolox_coco/yolox_tiny_coco/ckpt_best.pth",
    42. 

  42.               "yolox_n_coco": "https://deci-pretrained-models.s3.amazonaws.com/yolox_coco/yolox_n_coco/ckpt_best.pth",
    43. 

  43. 

  44.               "pp_lite_t_seg50_cityscapes": "https://deci-pretrained-models.s3.amazonaws.com/ppliteseg/cityscapes/pplite_t_seg50/average_model.pth",
    45. 

  45.               "pp_lite_t_seg75_cityscapes": "https://deci-pretrained-models.s3.amazonaws.com/ppliteseg/cityscapes/pplite_t_seg75/average_model.pth",
    46. 

  46.               "pp_lite_b_seg50_cityscapes": "https://deci-pretrained-models.s3.amazonaws.com/ppliteseg/cityscapes/pplite_b_seg50/average_model.pth",
    47. 

  47.               "pp_lite_b_seg75_cityscapes": "https://deci-pretrained-models.s3.amazonaws.com/ppliteseg/cityscapes/pplite_b_seg75/average_model.pth",
    48. 

  48.               }
    49. 

  49. 

  50. PRETRAINED_NUM_CLASSES = {"imagenet": 1000,
    51. 

  51.                           "imagenet21k": 21843,
    52. 

  52.                           "coco_segmentation_subclass": 21,
    53. 

  53.                           "cityscapes": 19,
    54. 

  54.                           "coco": 80,
    55. 

  55.                           "cifar10": 10}
    56.
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