Instructions to use iFaz/diffusion-pusht-seed42-half with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use iFaz/diffusion-pusht-seed42-half with LeRobot:
- Notebooks
- Google Colab
- Kaggle
File size: 1,517 Bytes
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datasets:
- lerobot/pusht
language: en
library_name: lerobot
license: apache-2.0
tags:
- robotics
- imitation-learning
- diffusion
- mujoco
- pytorch_model_hub_mixin
---
# DIFFUSION Policy β diffusion_pusht_seed42
Trained with [LeRobot](https://github.com/huggingface/lerobot).
Date: `2026-05-27 19:51`
Policy type: `diffusion` | Device: `cuda`
---
## π¦ Dataset
| Parameter | Value |
|---|---|
| `dataset.repo_id` | `lerobot/pusht` |
---
## ποΈ Training Config
| Parameter | Value |
|---|---|
| `steps` | `7000` |
| `batch_size` | `8` |
| `eval_freq` | `0` |
| `save_freq` | `2000` |
| `num_workers` | `4` |
| `seed` | `42` |
| `eval.n_episodes` | `1` |
| `eval.batch_size` | `1` |
| `eval.use_async_envs` | `True` |
---
## π Policy Architecture
_All defaults β no overrides applied._
---
## π― Eval Config
| Parameter | Value |
|---|---|
| `env.type` | `pusht` |
| `env.task` | `PushT-v0` |
| `eval.n_episodes` | `8` |
| `eval.batch_size` | `4` |
| `eval.use_async_envs` | `False` |
| `policy.path` | `/kaggle/working/outputs/train/pusht_seed42/checkpoints/last/pretrained_model` |
---
## π Eval Results
| Metric | Value |
|---|---|
| Episodes | `8` |
| Success rate | `0.0%` |
| Avg sum reward | `26.50` |
| Avg max reward | `0.30` |
| Eval time (s) | `171.4` |
---
## Citation
```bibtex
@misc{cadene2024lerobot,
author = {Cadene, Remi and Alibert, Simon and others},
title = {LeRobot},
year = {2024},
url = {https://github.com/huggingface/lerobot}
}
``` |