Simplify model documentation and release metadata

README.md

@@ -31,7 +31,6 @@ See the [DeepEthogram collection](https://huggingface.co/collections/jbohnslav/d
 | Input | 11 RGB frames stacked as `(N, 33, H, W)` |
 | Output | a three-scale optical-flow pyramid; highest-resolution shape `(1, 20, 32, 32)` for the example |
 | Pretraining data | Kinetics700, as recorded in the historical hyperparameters |
-| Source run | `200310_174416_MotionNet_kinetics` |
 | Framework | PyTorch; DeepEthogram |
 
 The model predicts motion fields from video frames. It does not classify behaviors.
@@ -44,7 +43,7 @@ Install DeepEthogram and the [Hugging Face CLI](https://huggingface.co/docs/hugg
 Download only this model into an existing DeepEthogram project's pretrained directory:
 
 ```bash
-hf download jbohnslav/deepethogram-motionnet --revision nas-2026-10-19 --local-dir /path/to/project/models/pretrained_models/200310_174416_MotionNet_kinetics
+hf download jbohnslav/deepethogram-motionnet --revision v1.0 --local-dir /path/to/project/models/pretrained_models/200310_174416_MotionNet_kinetics
 ```
 
 From that run directory, verify the artifacts with `shasum -a 256 -c SHA256SUMS`.
@@ -83,11 +82,9 @@ newer inference helpers expect `config.yaml` and associated metrics from your fi
 The complete historical plots, metrics, and additional stream snapshots remain in the
 [legacy archive](https://huggingface.co/jbohnslav/deepethogram-pretrained).
 
-## Validation and provenance
+## Validation
 
-The checkpoint and hyperparameters were copied without conversion from the author's NAS
-pretrained directory for `niv_revision_deepethogram`. Their SHA-256 checksums match the
-verified legacy archive; `SHA256SUMS` also covers this repo's license and model configuration.
+`SHA256SUMS` covers the checkpoint, training hyperparameters, license and model configuration.
 Validated with PyTorch 2.4.1 and DeepEthogram source revision
 `1541eec6958f90673b79c402e10b434c8cb7c868`: strict parameter loading and finite CPU
 outputs on the synthetic input above. This is a compatibility check, not an accuracy benchmark.

SHA256SUMS

@@ -1,4 +1,4 @@
 899e15e791552500b93f3ddcc239f570670edd9d9016c05b482ba04cb87415b0  LICENSE
 c75e175724ae04a94fc622cfe8949e63656a60639556b65f8d640eb2944a511a  checkpoint.pt
 fc8f58e37e37dccc2f6bddb0ad8ce1b39b0d448d78048adedf8a60b32f6668eb  hyperparameters.yaml
-2cd6a01c655785a101d2efcf54f90f456dd8e2127553e6e249a0e69d256526d2  model_config.json
+91e733d0a63a7ed021314b1b488375720ac0099f5426fbfb505cdc49544c437c  model_config.json

model_config.json

@@ -4,7 +4,6 @@
   "model_name": "MotionNet",
   "architecture": "MotionNet",
   "task": "optical-flow",
-  "source_run": "200310_174416_MotionNet_kinetics",
   "checkpoint": "checkpoint.pt",
   "flow_generator": "MotionNet",
   "num_rgb_frames": 11,