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Download the mappings from the Hub and create the id2label and label2id dictionaries: import json from huggingface_hub import cached_download, hf_hub_url repo_id = "huggingface/label-files" filename = "ade20k-id2label.json" id2label = json.load(open(cached_download(hf_hub_url(repo_id, filename, repo_type="dataset")), "r")) id2label = {int(k): v for k, v in id2label.items()} label2id = {v: k for k, v in id2label.items()} num_labels = len(id2label) Custom dataset You could also create and use your own dataset if you prefer to train with the run_semantic_segmentation.py script instead of a notebook instance. |