| --- |
| model_name: DeepEthogram TinyMotionNet |
| language: en |
| license: other |
| license_name: deepethogram-academic-noncommercial |
| license_link: LICENSE |
| library_name: deepethogram |
| tags: |
| - deepethogram |
| - pytorch |
| - optical-flow |
| - tinymotionnet |
| - kinetics700 |
| - animal-behavior |
| - pretrained |
| --- |
| |
| # DeepEthogram TinyMotionNet |
|
|
| TinyMotionNet is an optical-flow generator pretrained on Kinetics700 videos for [DeepEthogram](https://github.com/jbohnslav/deepethogram). |
| This repository contains this model's checkpoint and historical training hyperparameters. |
| See the [DeepEthogram collection](https://huggingface.co/collections/jbohnslav/deepethogram-pretrained-models-6ad61e8af1ac368c56d8deec) for the other models. |
|
|
| ## Model details |
|
|
| | Property | Value | |
| | --- | --- | |
| | Architecture | TinyMotionNet | |
| | Task | optical-flow | |
| | 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 | |
| | Framework | PyTorch; DeepEthogram | |
|
|
| The model predicts motion fields from video frames. It does not classify behaviors. |
| Hugging Face has no dedicated optical-flow pipeline category in its current task registry; |
| this repo uses the explicit `optical-flow` tag and DeepEthogram library metadata. |
|
|
| ## Download and load |
|
|
| Install DeepEthogram and the [Hugging Face CLI](https://huggingface.co/docs/huggingface_hub/guides/cli). |
| Download only this model into an existing DeepEthogram project's pretrained directory: |
|
|
| ```bash |
| hf download jbohnslav/deepethogram-tinymotionnet --revision v1.0 --local-dir /path/to/project/models/pretrained_models/200221_115158_TinyMotionNet |
| ``` |
|
|
| From that run directory, verify the artifacts with `shasum -a 256 -c SHA256SUMS`. |
| The `checkpoint.pt` and `hyperparameters.yaml` at its root let DeepEthogram discover |
| this model using its existing project layout. |
|
|
| To load directly in Python, set `run_dir` to the directory downloaded above: |
|
|
| ```python |
| from pathlib import Path |
| import torch |
| from deepethogram import utils |
| |
| from deepethogram.flow_generator.models.TinyMotionNet import TinyMotionNet |
| model = TinyMotionNet() |
| run_dir = Path('/path/to/project/models/pretrained_models/200221_115158_TinyMotionNet') |
| state_dict, _, _ = utils.load_state_dict_from_file(run_dir / 'checkpoint.pt') |
| model.load_state_dict(state_dict, strict=True) |
| model.eval() |
| with torch.inference_mode(): |
| output = model(torch.zeros(1, 33, 64, 64)) |
| ``` |
|
|
| The zero-valued example checks shapes and loading. For real videos use DeepEthogram's |
| video preprocessing and normalization. Historical settings are in `hyperparameters.yaml`; |
| `model_config.json` summarizes architecture and input/output conventions. |
|
|
| ## Intended use and limitations |
|
|
| Historical YAML files contain old filesystem paths and legacy serialization tags; they are |
| provenance records, not portable project configs. Checkpoints retain their original PyTorch |
| serialization. Use DeepEthogram, rather than Transformers `AutoModel`, to load them. |
|
|
| This release contains initialization weights, not a complete trained behavior inference run: |
| newer inference helpers expect `config.yaml` and associated metrics from your fine-tuned run. |
| The complete historical plots, metrics, and additional stream snapshots remain in the |
| [legacy archive](https://huggingface.co/jbohnslav/deepethogram-pretrained). |
|
|
| ## Validation |
|
|
| `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. |
|
|
| ## License and citation |
|
|
| [LICENSE](LICENSE) reproduces the original DeepEthogram Harvard Academic and Non-Commercial |
| Research Use Software License and Terms of Use unchanged. It restricts commercial use and |
| end-user redistribution. This mirror grants no additional permissions and does not relicense |
| the assets; commercial inquiries should go to Harvard OTD as described in the license. |
|
|
| Bohnslav et al. (2021). *DeepEthogram, a machine learning pipeline for supervised behavior |
| classification from raw pixels*. eLife 10:e63377. |
| [doi:10.7554/eLife.63377](https://doi.org/10.7554/eLife.63377). |
|
|
