Update dataset card

README.md

@@ -15,43 +15,37 @@ tags:
 
 # jepa.cpp parity fixtures
 
-PyTorch golden reference dumps for [**jepa.cpp**](https://github.com/aselimc/jepa.cpp), a ggml-based
-C/C++ inference engine for the JEPA family. `tests/test-parity` and `tests/test-predictor` replay
-these tensors through the engine and gate per-token cosine, pooled outputs and classifier top-1/top-5
-against per-family thresholds.
+PyTorch golden reference dumps for [**jepa.cpp**](https://github.com/aselimc/jepa.cpp), a ggml-based C/C++ inference engine for the
+JEPA family. `tests/test-parity` and `tests/test-predictor` replay these tensors through the engine and
+gate per-token cosine, pooled outputs and classifier top-1/top-5 against per-family thresholds.
 
-Generated from jepa.cpp `main` @ [`4665029`](https://github.com/aselimc/jepa.cpp/commit/4665029) with
-`scripts/dump_reference.py --model all`, in float32 eval mode on 32 CPU threads, no autocast:
-torch 2.13.0+cpu, transformers 5.16.1.
+Generated from jepa.cpp `main` @ [`3c9d60b`](https://github.com/aselimc/jepa.cpp/commit/3c9d60b) with
+`scripts/dump_reference.py --model all`, in float32 eval mode on 32 CPU threads, no autocast.
 
 ## Contents
 
-| directory | model | samples | size |
-|---|---|---|---|
-| `ref/ijepa-vith14-1k/` | I-JEPA ViT-H/14 | 8 COCO images | 15 MB |
-| `ref/lejepa-vits16/` | LeJEPA ViT-S/16 | 8 COCO images | 7.1 MB |
-| `ref/lewm-pusht/` | LeWorldModel Push-T | 2 images + a 3-frame causal rollout | 3.4 MB |
-| `ref/vjepa2-vitl-fpc64-256/` | V-JEPA 2 ViT-L/16 | 2 clips x {16, 64} frames, encoder + predictor | 350 MB |
-| `ref/vjepa2-vitl-fpc16-256-ssv2/` | V-JEPA 2 ViT-L SSv2 classifier | 2 clips x 16 frames, encoder + pooler + logits | 54 MB |
-| `ref/vjepa2_1-vitb-384/` | V-JEPA 2.1 ViT-B/16 @384 | 1 clip x 16 frames + 2 images | 102 MB |
-| `ref/levjepa-vitl16/` | LeVJEPA ViT-L/16 | 2 clips x 16 frames + 2 still images | 130 MB |
+| directory | model | files | size |
+|---|---|---:|---:|
+| `ref/ijepa-vith14-1k/` | I-JEPA ViT-H/14 (IN1k) | 25 | 15 MB |
+| `ref/lejepa-vits16/` | LeJEPA ViT-S/16 (IN1k) | 41 | 7 MB |
+| `ref/levjepa-vitl16/` | LeVJEPA ViT-L/16 (VideoMix) | 21 | 136 MB |
+| `ref/lewm-pusht/` | LeWorldModel Push-T | 20 | 3 MB |
+| `ref/vjepa2-vitl-fpc16-256-ssv2/` | V-JEPA 2 ViT-L/16 SSv2 classifier | 13 | 56 MB |
+| `ref/vjepa2-vitl-fpc64-256/` | V-JEPA 2 ViT-L/16 (fpc64, 256) | 21 | 366 MB |
+| `ref/vjepa2_1-vitb-384/` | V-JEPA 2.1 ViT-B/16 @384 | 15 | 107 MB |
 
-156 files, 660 MB in total.
+156 files, 691 MB in total.
 
 ## Layout
 
 One directory per model, each with a `manifest.json` and one `.npy` per tensor per sample, named
 `<sample>.<tensor>.npy`. All arrays are float32 C-order except `frames_u8` (uint8) and `top5_idx`
-(int64). Shapes carry no batch dimension except `input`, which is stored exactly as fed to the model
-(batch = 1).
+(int64). Shapes carry no batch dimension except `input`, which is stored exactly as fed to the model.
 
-The manifest records the model id, the exact hyper-parameters, the preprocessing pipeline that
-produced `input` (resize mode, resampler, crop, mean/std), the PyTorch forward wall time per sample
-(`timing_s.forward_s`, the baseline for the speed tables of the jepa.cpp docs), the frame indices
-sampled from each clip, and the 174 Something-Something-v2 label strings for the classifier. The
-`source` field is the absolute path of the checkout that generated the dump.
-
-Per-model tensor lists and the exact preprocessing recipe:
+The manifest records the model id, the hyper-parameters, the preprocessing pipeline that produced
+`input`, the PyTorch forward wall time per sample (`timing_s.forward_s`, the baseline for the speed
+tables of the jepa.cpp docs), the frame indices sampled from each clip, and the label strings for the
+classifier. Per-model tensor lists and the exact preprocessing recipe:
 [**docs/fixtures.md**](https://aselimc.github.io/jepa.cpp/fixtures/).
 
 ## How jepa.cpp consumes it
@@ -63,14 +57,7 @@ cmake -S . -B build -G Ninja -DCMAKE_BUILD_TYPE=Release && cmake --build build -
 scripts/download_fixtures.sh          # this dataset -> tests/fixtures/ref, plus the media it needs
 scripts/download_models.sh small      # GGUFs from https://huggingface.co/jepacpp
 
-cmake -S . -B build && ctest --test-dir build      # parity suites register once ref/ and models/gguf/ exist
-```
-
-A single replay, without ctest:
-
-```bash
-build/test-parity models/gguf/lejepa-vits16-pretrain-in1k-f16.gguf \
-    tests/fixtures/ref/lejepa-vits16 --threads 32 --json out.json
+cmake -S . -B build && ctest --test-dir build      # the parity suites register at configure time
 ```
 
 `test-parity` runs two passes per file: first the stored `input` tensor (bypassing preprocessing, so a
@@ -87,31 +74,26 @@ tests replay are.
 | input | source | fetched by |
 |---|---|---|
 | 8 images `coco_*.jpg` | [COCO val2017](https://cocodataset.org/), images subject to their original Flickr terms | tracked in the jepa.cpp git repo |
-| 6 clips (`archery`, `bowling`, `flying_kite`, `high_jump`, `high_jump2`, `marching`) | [`nateraw/kinetics-mini`](https://huggingface.co/datasets/nateraw/kinetics-mini), a small sample of Kinetics-400 | `scripts/download_fixtures.sh` |
+| 6 short clips | [`nateraw/kinetics-mini`](https://huggingface.co/datasets/nateraw/kinetics-mini), a small sample of Kinetics-400 | `scripts/download_fixtures.sh` |
 
-The `input` and `frames_u8` arrays inside `ref/` are preprocessed pixels of those 8 images and 6 clips
-(16 or 64 sampled frames each), kept because the parity tests must feed the network exactly the tensor
-PyTorch saw. If you hold rights in any of the underlying material and want it removed, open an issue on
-[github.com/aselimc/jepa.cpp](https://github.com/aselimc/jepa.cpp/issues).
+The `input` and `frames_u8` arrays inside `ref/` are preprocessed pixels of those images and clips, kept
+because the parity tests must feed the network exactly the tensor PyTorch saw. If you hold rights in any
+of the underlying material and want it removed, open an issue on [github.com/aselimc/jepa.cpp](https://github.com/aselimc/jepa.cpp/issues).
 
 ## Licence
 
-`cc-by-nc-4.0`, the most restrictive licence among the checkpoints whose outputs are stored here:
-`ref/ijepa-vith14-1k` and `ref/levjepa-vitl16` are outputs of CC BY-NC 4.0 models
-([facebook/ijepa_vith14_1k](https://huggingface.co/facebook/ijepa_vith14_1k),
-[galilai-group/LeVJEPA-VideoMix-Large](https://huggingface.co/galilai-group/LeVJEPA-VideoMix-Large));
-the other five directories are outputs of MIT / Apache-2.0 models. Research and non-commercial use.
+`cc-by-nc-4.0`, the most restrictive licence among the checkpoints whose outputs are stored here: the
+dumps of I-JEPA ViT-H/14 (IN1k) and LeVJEPA ViT-L/16 (VideoMix) are outputs of CC BY-NC 4.0 models, the rest of MIT / Apache-2.0 ones.
+Research and non-commercial use.
 
 ## Regenerating instead of downloading
 
 ```bash
-scripts/download_fixtures.sh                      # media
-scripts/download_models.sh all                    # source checkpoints
-.venv/bin/python scripts/dump_reference.py --model all   # ~1 min on 32 cores
+scripts/download_fixtures.sh media                        # the source media
+scripts/download_models.sh --convert all                  # the source checkpoints
+.venv/bin/python scripts/dump_reference.py --model all    # ~1 min on 32 cores
 ```
 
-Needs the project venv (torch CPU, transformers, av, pillow, timm, einops).
-
 ## Links
 
 - Code: <https://github.com/aselimc/jepa.cpp>