src/transformers/models/yolos/convert_yolos_to_pytorch.py/get_yolos_config
def get_yolos_config(yolos_name: str) -> YolosConfig:
    config = YolosConfig()

    # size of the architecture
    if "yolos_ti" in yolos_name:
        config.hidden_size = 192
        config.intermediate_size = 768
        config.num_hidden_layers = 12
        config.num_attention_heads = 3
        config.image_size = [800, 1333]
        config.use_mid_position_embeddings = False
    elif yolos_name == "yolos_s_dWr":
        config.hidden_size = 330
        config.num_hidden_layers = 14
        config.num_attention_heads = 6
        config.intermediate_size = 1320
    elif "yolos_s" in yolos_name:
        config.hidden_size = 384
        config.intermediate_size = 1536
        config.num_hidden_layers = 12
        config.num_attention_heads = 6
    elif "yolos_b" in yolos_name:
        config.image_size = [800, 1344]

    config.num_labels = 91
    repo_id = "huggingface/label-files"
    filename = "coco-detection-id2label.json"
    id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r"))
    id2label = {int(k): v for k, v in id2label.items()}
    config.id2label = id2label
    config.label2id = {v: k for k, v in id2label.items()}

    return config