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model_weights.pth: PyTorch checkpoint containing target encoder weightsconfig.json: architecture configuration used for this checkpoint1import json
2import torch
3
4with open("config.json", "r") as f:
5 cfg = json.load(f)
6
7# Build your model class with cfg (must match training architecture)
8# model = build_target_encoder(**cfg)
9
10state = torch.load("model_weights.pth", map_location="cpu")
11# model.load_state_dict(state, strict=True)
12# model.eval()1@article{assran2023self,
2 title={Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture},
3 author={Assran, Mahmoud and Duval, Quentin and Misra, Ishan and Bojanowski, Piotr and Vincent, Pascal and Rabbat, Michael and LeCun, Yann and Ballas, Nicolas},
4 journal={arXiv preprint arXiv:2301.08243},
5 year={2023}
6}