| --- |
| pretty_name: P-JEPA feature archives |
| license: other |
| license_name: source-dataset-terms |
| license_link: LICENSE.md |
| tags: |
| - video |
| - feature-extraction |
| - temporal-action-segmentation |
| - surgical-phase-recognition |
| - pytorch |
| - pjepa |
| --- |
| |
| # P-JEPA feature archives |
|
|
| Precomputed **input features** and aligned annotations for the |
| [P-JEPA encoders](https://huggingface.co/FelTris/pjepa). These are backbone/pooler |
| outputs, before P-JEPA temporal encoding. No raw videos or P-JEPA output caches |
| are included. All feature tensors are float32. |
|
|
| The files use the directory layout expected by the |
| [P-JEPA code](https://github.com/FelTris/pjepa). That GitHub repository is currently |
| private pending the code release. The plain PyTorch example below works without |
| it; the full training/evaluation recipes require access to the code. |
|
|
| ## Contents and encoder inputs |
|
|
| | Dataset / feature family | Feature extractor | Width | Stored sampling | Videos / splits | Size | |
| |---|---|---:|---|---|---:| |
| | LEMON | PL-Stitch ViT-B/16, final CLS | 768 | 4 fps; released P-JEPA recipe takes every fourth token (1 fps) | 4,194 pretrain | 40.24 GB | |
| | Cholec80 | Same PL-Stitch | 768 | 1 fps | 80; train 01–40, test 41–80 | 0.57 GB | |
| | M2CAI16 | Same PL-Stitch | 768 | 1 fps | 27 train / 14 test | 0.29 GB | |
| | Assembly101 | Released TSM features, 8-frame input | 2048 | Stride 8 on a 30-Hz feature timeline: 3.75 fps | 204 train / 61 val; camera C10119 | 3.37 GB | |
| | EgoProceL / FACT features | I3D features distributed with FACT | 2048 | 10-Hz source resampled to nominal 4 fps | 337 train / 86 test | 8.33 GB | |
| | EgoProceL / pooled V-JEPA | V-JEPA 2.1 ViT-g + supervised framewise pooler | 1408 | Nominal 4 fps | 337 train / 86 test | 5.73 GB | |
|
|
| Sizes are decimal GB; total features are approximately **58.54 GB**, plus three |
| small CSV segment manifests. Coverage is the archived experimental subset, not |
| a claim to contain every video/view of each original dataset. Both EgoProceL families contain the same 423 video IDs and train/test assignments. |
| Use each family's own manifest for its matching annotation timeline. |
|
|
| ### EgoProceL camera coverage |
|
|
| Both feature families contain **337 training and 86 test videos**. The FACT |
| archive is restricted to the video IDs in the pooled archive. This excludes |
| 491 CMU-Kitchens static-camera recordings (394 train / 97 test) from the original |
| FACT distribution: cameras `7151020` (top), `7151062` (right side), and `8421130` |
| (left/top), according to the [upstream camera mapping](https://github.com/Sid2697/EgoProceL-egocentric-procedure-learning/blob/main/annotations/metadata/CMU_Kitchens/misc.py). |
| All non-CMU video IDs are retained. Both archives include CMU IDs `7150991` |
| and `7150996`, following the original pooled experiment's video selection. |
| Counts refer to video files/camera recordings, not independent sessions. |
|
|
| The retained FACT vectors and timestamps are unchanged, and all 116 label IDs |
| are preserved. **The released P-JEPA FACT-input checkpoint was trained on the |
| original 731-train / 183-test mixed-view split.** The current archive and recipes |
| use the smaller matched subset; training on them does not reproduce that |
| checkpoint's original training data. The pooled checkpoint's data is unchanged. |
| The archives have matching video coverage, but the pooled representation still |
| has supervised feature-extractor provenance. |
|
|
| ### Feature provenance and timing |
|
|
| - **PL-Stitch:** `pl_lemon.pth`, SHA-256 |
| `8c4166f24c02a7cb5d10a89ebe179dfb50de25319031f4128af22d57a992d8a3`; |
| upstream code commit `f4a3b19de71c041d77e091817dbe93a7da168498`. |
| Final CLS features precede the projection head. Images are RGB, resized |
| directly to 224×224 using bilinear antialiased interpolation, with ImageNet |
| channel mean/std. Feature computation and storage use float32. This image |
| preprocessing is already applied; pass the stored vectors directly to P-JEPA. |
| - **Assembly101:** the official TSM feature release is a supervised feature |
| representation. This archive samples every eighth feature on its 30-Hz |
| annotation timeline; `times` contains seconds. Do not substitute the video's |
| 60-fps container rate when interpreting annotation frame numbers. The exact |
| upstream extractor checkpoint hash is not recorded in the archive. |
| - **FACT features:** I3D vectors repacked from the [FACT EgoProceL `.npy` distribution](https://github.com/ZijiaLewisLu/CVPR2024-FACT). |
| FACT names the provider here, not an additional temporal model to run. |
| Features are trimmed to available ground-truth length before resampling. |
| Indices follow `floor(arange(0, duration, 0.25) * 10)`: stored timestamps can |
| alternate between 0.2- and 0.3-second gaps. Use `times`, not `arange(T)/4`. |
| - **Pooled V-JEPA:** extraction used `vjepa2_1_vit_giant_384_framewise`, a frozen |
| pretrained V-JEPA 2.1 ViT-g backbone with a supervised framewise pooler trained |
| using FACT step labels (`model-epoch=21.ckpt`). The extraction configuration |
| uses a 256-pixel test crop, 64-frame non-overlapping windows sampled at 4 fps, |
| and 64 learned frame queries (pooler depth 4, 16 heads). Saved features are |
| frame-pooler outputs before classifier pooling, not raw V-JEPA patch averages. |
| Timestamps follow sampled video frames and can deviate from an exact 0.25-s |
| grid. The final window is padded during extraction; padded outputs are omitted. |
| This representation has supervised provenance even when subsequent P-JEPA |
| fitting uses an SSL loss. Its extractor weights are not part of this dataset. |
|
|
| ## Download |
|
|
| Download everything into your feature-data root (allow at least 60 GB): |
|
|
| ```bash |
| python -m pip install huggingface_hub |
| export PJEPA_DATA_ROOT=/path/to/pjepa-data |
| hf download FelTris/pjepa_features --repo-type dataset --local-dir "$PJEPA_DATA_ROOT" |
| cd "$PJEPA_DATA_ROOT" |
| sha256sum -c SHA256SUMS |
| ``` |
|
|
| For only Assembly101, for example: |
|
|
| ```bash |
| hf download FelTris/pjepa_features --repo-type dataset \ |
| --include 'assembly101_small/*' 'assembly101_annotations/*' \ |
| --local-dir "$PJEPA_DATA_ROOT" |
| ``` |
|
|
| The corresponding pairs for EgoProceL are `egoprocel_small/fact_npy_features_4fps.pt` |
| with `egoprocel_annotations/egoprocel_fact_features_segments.csv`, and |
| `egoprocel_small/pooled_features_fact.pt` with |
| `egoprocel_annotations/egoprocel_segments.csv`. Download every LEMON shard along |
| with its index. Partial downloads will not pass the full-repository checksum check. |
|
|
| ## Format and loading |
|
|
| Use PyTorch 2.6+; these are packed torch archives rather than a Hugging Face |
| `datasets.load_dataset` table. `manifest.json` records file hashes, byte sizes, |
| dimensions, token/video counts, split counts, and extraction metadata. |
|
|
| ```python |
| from pathlib import Path |
| import torch |
| from huggingface_hub import hf_hub_download |
| |
| path = hf_hub_download( |
| "FelTris/pjepa_features", |
| "Cholec80/cholec80_pl_stitch_vitb16_1fps.pt", |
| repo_type="dataset", |
| ) |
| archive = torch.load(path, map_location="cpu", mmap=True, weights_only=True) |
| video_index = 0 |
| start, end = map(int, archive["offsets"][video_index:video_index + 2]) |
| features = archive["tokens"][start:end] # [time, 768], float32 |
| times = archive["times"][start:end] # seconds |
| labels = archive["phase_labels"][start:end] |
| video_id = archive["video_ids"][video_index] |
| ``` |
|
|
| For a packed Assembly101/EgoProceL file, use the same `tokens`, `times`, and |
| `offsets` slicing; `paths[video_index]` identifies the sequence. Labels and |
| intervals come from its matching CSV, joined by `video_path` (replace a video |
| extension with `.pt` to match archive `paths`). CSV `official_split` supplies |
| the split. Preserve class IDs and background handling from the supplied recipes; |
| EgoProceL uses `fact_step_id` with background 0, Assembly101 uses `action_id`. |
| The release loaders handle interval alignment and take grouping. |
|
|
| LEMON's index maps each video through `shard_ids` and `local_indices` to a |
| shard's `offsets`. Resolve `shards[shard_id]` relative to the downloaded index |
| directory. For 1-fps P-JEPA inputs, slice each video's tokens/times with `[::4]`. |
| Historical absolute paths in archive metadata document extraction provenance; |
| they are not required files on the user's machine. No raw-video decoder, |
| PL-Stitch installation, FACT installation, or V-JEPA installation is needed. |
|
|
| The released LEMON loader memory-maps shards with a bounded shard cache. The |
| Assembly101/EgoProceL training loader loads its packed archive into host memory; |
| budget RAM for the selected archive plus training buffers. The plain loading |
| example uses memory mapping to avoid eagerly reading the entire tensor. |
|
|
| ## Protocols and matching P-JEPA checkpoints |
|
|
| | Input | Encoder in `FelTris/pjepa` | |
| |---|---| |
| | PL-Stitch at 1 fps | `lemon_plstitch_block32.pt` | |
| | Assembly101 TSM at 3.75 fps | `assembly101_tsm_block64.pt` or oracle `assembly101_tsm_clip.pt` | |
| | EgoProceL FACT features | Oracle `egoprocel_fact_features_clip.pt` | |
| | EgoProceL pooled V-JEPA | Oracle `egoprocel_pooled_clip.pt` | |
|
|
| **Clip-causal models require ground-truth segment boundaries and are oracle |
| comparisons. Use block-causal models going forward.** Block-causal P-JEPA does |
| not require true boundaries for attention; historical training sequence |
| construction and upstream feature learning can still use annotations. |
|
|
| LEMON SSL uses only its `pretrain` split. Cholec80 development selection fits |
| probes on videos 01–32 and selects on 33–40; 41–80 remain held out. Full Cholec80 |
| probing uses 01–40 / 41–80. M2CAI16 uses all 27 / 14 official train/test videos; |
| inline `source_splits` records membership. Surgical phase names are included |
| in `phase_class_names` (7 Cholec80 classes; 8 M2CAI16 classes). |
|
|
| Historical EgoProceL configurations use the split named `test` for model/probe |
| selection. It must not be described as an untouched final test set. The pooled |
| feature extractor's configuration also uses `test` for validation. Feature-extractor supervision and the original training data of each saved |
| P-JEPA checkpoint must be accounted for when interpreting comparisons. |
|
|
| ## Sources and terms |
|
|
| Credit the original datasets and feature methods when using these archives: |
| [LEMON](https://github.com/visurg-ai/LEMON), |
| [PL-Stitch](https://github.com/visurg-ai/PL-Stitch), |
| [Cholec80 and M2CAI16](https://camma.unistra.fr/datasets/), |
| [Assembly101 and its TSM features](https://github.com/assembly-101/assembly101-temporal-action-segmentation), |
| [FACT's EgoProceL distribution](https://github.com/ZijiaLewisLu/CVPR2024-FACT), |
| and [V-JEPA 2.1](https://github.com/facebookresearch/vjepa2). |
| See [LICENSE.md](LICENSE.md) for source-specific terms. The MIT license on |
| P-JEPA code/encoder weights does not relicense the source datasets or annotations. |
|
|
