Initial upload of Rectified LP-JEPA checkpoints

.gitattributes

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+*.ckpt filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+license: apache-2.0
+library_name: pytorch
+tags:
+- jepa
+- rectified-lp-jepa
+- self-supervised-learning
+- computer-vision
+- pytorch-lightning
+- representation-learning
+---
+
+# Rectified LP-JEPA Checkpoints
+
+This repository contains checkpoints for Rectified LP-JEPA models trained for 1000 epochs (unless otherwise noted). These models correspond to the results presented in Table 1 of our paper.
+
+For the full training code and implementation details, please refer to our main codebase: [rectified-lp-jepa](https://github.com/YilunKuang/rectified-lp-jepa).
+
+## Performance Card
+
+The following table summarizes the performance of the provided checkpoints on ImageNet-1k (val_acc1) and other metrics:
+
+| Name | val_acc1 | val_proj_acc1 | train_l1_sparsity | train_l0_sparsity |
+| :--- | :--- | :--- | :--- | :--- |
+| p2.0-mu1.0 | 85.28 | 79.64 | 0.6291 | 0.8678 |
+| p2.0-mu0.5 | 85.40 | 80.02 | 0.4899 | 0.8024 |
+| p2.0-mu0.75 | 85.10 | 80.96 | 0.5619 | 0.8360 |
+| p2.0-mu0.25 | 84.94 | 80.10 | 0.4164 | 0.7685 |
+| p2.0-mu0.0 | 84.58 | 80.30 | 0.3424 | 0.7301 |
+| p2.0-mu-0.5 | 83.98 | 79.88 | 0.2079 | 0.6681 |
+| p2.0-mu-0.25 | 84.70 | 79.24 | 0.2716 | 0.6955 |
+| p2.0-mu-0.75 | 84.90 | 79.30 | 0.1529 | 0.6370 |
+| p2.0-mu-1.0 | 84.48 | 79.54 | 0.1068 | 0.6042 |
+| p2.0-mu-1.25 | 84.50 | 79.00 | 0.0717 | 0.4839 |
+| p2.0-mu-1.50 | 84.58 | 77.84 | 0.0469 | 0.2807 |
+| p2.0-mu-2.50 | 82.48 | 68.42 | 0.0126 | 0.0206 |
+| p2.0-mu-2.0 | 83.06 | 72.38 | 0.0194 | 0.0420 |
+| p2.0-mu-2.25 | 82.22 | 71.08 | 0.0158 | 0.0290 |
+| p2.0-mu-2.75 | 81.14 | 64.02 | 0.0128 | 0.0189 |
+| p1.0-mu-1.75 | 84.68 | 77.84 | 0.0300 | 0.1244 |
+| p1.0-mu-2.0 | 84.06 | 76.00 | 0.0248 | 0.0742 |
+| p1.0-mu-2.75 | 83.10 | 72.88 | 0.0068 | 0.0119 |
+| p2.0-mu-1.75 | 83.62 | 76.10 | 0.0290 | 0.0898 |
+| p1.0-mu0.0 | 84.76 | 80.14 | 0.2743 | 0.6943 |
+| p1.0-mu0.25 | 85.80 | 80.40 | 0.3752 | 0.7444 |
+| p1.0-mu-1.50 | 84.98 | 78.38 | 0.0397 | 0.2329 |
+| p1.0-mu-0.25 | 85.02 | 79.88 | 0.1982 | 0.6461 |
+| p1.0-mu-2.50 | 84.42 | 73.86 | 0.0134 | 0.0269 |
+| p1.0-mu-2.25 | 84.20 | 74.50 | 0.0207 | 0.0507 |
+| p1.0-mu-0.5 | 84.87 | 79.48 | 0.1430 | 0.6012 |
+| p1.0-mu1.0 | 85.23 | 79.52 | 0.6474 | 0.8770 |
+| p2.0-mu-3.0 | 78.44 | 56.40 | 0.0047 | 0.0060 |
+| p1.0-mu-1.0 | 84.64 | 79.66 | 0.0745 | 0.4784 |
+| p1.0-mu-1.25 | 84.54 | 78.60 | 0.0538 | 0.3675 |
+| p1.0-mu-0.75 | 84.48 | 79.42 | 0.1036 | 0.5463 |
+| p1.0-mu0.5 | 84.42 | 80.70 | 0.4774 | 0.7986 |
+| p1.0-mu0.75 | 85.58 | 80.38 | 0.5703 | 0.8423 |
+| p1.0-mu-3.0 | 82.66 | 72.32 | 0.0062 | 0.0109 |
+
+> **Note:** The `p1.0-mu-3.0` checkpoint is currently at 934 epochs. We plan to update it to the full 1000-epoch version soon.
+
+## Usage
+
+To use these checkpoints, first clone the original codebase:
+
+```bash
+git clone https://github.com/YilunKuang/rectified-lp-jepa
+cd rectified-lp-jepa
+```
+
+### Loading a Checkpoint for Inference
+
+Each model directory contains a `.ckpt` file and its corresponding `args.json`. You can load them as follows:
+
+```python
+import json
+import torch
+from pathlib import Path
+from omegaconf import OmegaConf
+from solo.methods import METHODS
+
+# Setup paths (example for p2.0-mu-1.75)
+ckpt_path = "p2.0-mu-1.75-1000epoch/p2.0-mu-1.75-1000epoch.ckpt"
+args_path = "p2.0-mu-1.75-1000epoch/args.json"
+
+# 1. Load Config
+with open(args_path) as f:
+    method_args = json.load(f)
+cfg = OmegaConf.create(method_args)
+
+# 2. Load Model
+print(f"Loading model from {ckpt_path}...")
+model = METHODS[method_args["method"]].load_from_checkpoint(ckpt_path, strict=False, cfg=cfg)
+model.cuda().eval()
+
+# 3. Inference
+# Input should be a normalized 224x224 tensor
+with torch.no_grad():
+    features = model.encoder(input_tensor)
+    if hasattr(model, "projector"):
+        projected = model.projector(features)
+```
+
+## Acknowledgements
+
+This codebase is built upon the [solo-learn](https://github.com/vturrisi/solo-learn) framework.
+We thank the solo-learn authors for releasing their code under the MIT license.
+
+## Citation
+
+Please cite our work if you find it helpful:
+
+```bibtex
+@misc{kuang2026rectifiedlpjepajointembeddingpredictive,
+      title={Rectified LpJEPA: Joint-Embedding Predictive Architectures with Sparse and Maximum-Entropy Representations}, 
+      author={Yilun Kuang and Yash Dagade and Tim G. J. Rudner and Randall Balestriero and Yann LeCun},
+      year={2026},
+      eprint={2602.01456},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2602.01456}, 
+}
+```

p1.0-mu-0.25-1000epoch/args.json

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+{"name": "p1.0-mu-0.25", "method": "rectified_lpjepa", "backbone": {"name": "resnet50", "kwargs": {}}, "method_kwargs": {"target_distribution": "rectified_lp_distribution", "lp_norm_parameter": 1.0, "proj_hidden_dim": 2048, "proj_output_dim": 2048, "invariance_loss_weight": 25.0, "rdm_reg_loss_weight": 125.0, "projector_type": "rectified_mlp", "num_projections": 8192, "projection_vectors_type": "random", "add_projector_classifier": true, "mean_shift_value": -0.25, "mode_of_sigma": "sigma_GN", "logging_interval": 50, "active_feature_threshold": 0.001}, "mlp_probe": {"enabled": false, "num_layers": 3}, "data": {"dataset": "imagenet100", "train_path": "/imagenet100_real/train", "val_path": "/imagenet100_real/val", "preload": true, "format": "image_folder", "num_workers": 8, "no_labels": false, "fraction": -1, "num_classes": 100, "num_large_crops": 2, "num_small_crops": 0}, "augmentations": [{"rrc": {"enabled": true, "crop_min_scale": 0.2, "crop_max_scale": 1.0}, "color_jitter": {"enabled": true, "brightness": 0.4, "contrast": 0.4, "saturation": 0.2, "hue": 0.1, "prob": 0.8}, "grayscale": {"enabled": true, "prob": 0.2}, "gaussian_blur": {"enabled": true, "prob": 0.5}, "solarization": {"enabled": true, "prob": 0.1}, "equalization": {"enabled": false, "prob": 0.0}, "horizontal_flip": {"enabled": true, "prob": 0.5}, "crop_size": 224, "num_crops": 2}], "optimizer": {"name": "lars", "batch_size": 128, "lr": 0.0825, "classifier_lr": 0.0275, "weight_decay": 0.0001, "kwargs": {"clip_lr": true, "eta": 0.02, "exclude_bias_n_norm": true, "momentum": 0.9}, "exclude_bias_n_norm_wd": false}, "scheduler": {"name": "warmup_cosine", "lr_decay_steps": null, "min_lr": 0.0, "warmup_start_lr": 3e-05, "warmup_epochs": 10, "interval": "step"}, "checkpoint": {"enabled": true, "dir": "trained_models", "frequency": 1, "keep_prev": false}, "auto_resume": {"enabled": true, "max_hours": 36}, "max_epochs": 1000, "devices": [0], "sync_batchnorm": true, "accelerator": "gpu", "strategy": "ddp", "precision": "16-mixed", "wandb": {"enabled": true, "entity": "yash21-dagade-duke-university", "project": "ICML-Table-1-Reproduction", "offline": false}, "auto_umap": {"enabled": false, "dir": "auto_umap", "frequency": 1}, "debug_augmentations": false, "seed": 5, "resume_from_checkpoint": null, "num_nodes": 1, "accumulate_grad_batches": 1, "knn_eval": {"enabled": false, "k": 20, "distance_func": "euclidean"}, "performance": {"disable_channel_last": false}, "wandb_run_id": "jtxvrrqi"}

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p1.0-mu-0.5-1000epoch/args.json

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p1.0-mu-0.75-1000epoch/args.json

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p1.0-mu-1.0-1000epoch/args.json

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p1.0-mu-1.25-1000epoch/args.json

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