{
  "_name_or_path": "microsoft/resnet-101",
  "architectures": [
    "ResNetForImageClassification"
  ],
  "depths": [
    3,
    4,
    23,
    3
  ],
  "downsample_in_first_stage": false,
  "embedding_size": 64,
  "hidden_act": "relu",
  "hidden_sizes": [
    256,
    512,
    1024,
    2048
  ],
  "id2label": {
    "0": "beverage cans",
    "1": "cardboard",
    "10": "medicines",
    "11": "metal containers",
    "12": "news paper",
    "13": "other metal objects",
    "14": "paper",
    "15": "paper cups",
    "16": "plastic bags",
    "17": "plastic bottles",
    "18": "plastic containers",
    "19": "plastic cups",
    "2": "cigarette butt",
    "20": "small appliances",
    "21": "smartphones",
    "22": "spray cans",
    "23": "syringe",
    "24": "tetra pak",
    "3": "construction scrap",
    "4": "electrical cables",
    "5": "electronic chips",
    "6": "glass",
    "7": "gloves",
    "8": "laptops",
    "9": "masks"
  },
  "label2id": {
    "beverage cans": "0",
    "cardboard": "1",
    "cigarette butt": "2",
    "construction scrap": "3",
    "electrical cables": "4",
    "electronic chips": "5",
    "glass": "6",
    "gloves": "7",
    "laptops": "8",
    "masks": "9",
    "medicines": "10",
    "metal containers": "11",
    "news paper": "12",
    "other metal objects": "13",
    "paper": "14",
    "paper cups": "15",
    "plastic bags": "16",
    "plastic bottles": "17",
    "plastic containers": "18",
    "plastic cups": "19",
    "small appliances": "20",
    "smartphones": "21",
    "spray cans": "22",
    "syringe": "23",
    "tetra pak": "24"
  },
  "layer_type": "bottleneck",
  "model_type": "resnet",
  "num_channels": 3,
  "out_features": [
    "stage4"
  ],
  "out_indices": [
    4
  ],
  "problem_type": "single_label_classification",
  "stage_names": [
    "stem",
    "stage1",
    "stage2",
    "stage3",
    "stage4"
  ],
  "torch_dtype": "float32",
  "transformers_version": "4.29.2"
}