README.md

---
license: apache-2.0
datasets:
- lerobot/pusht_keypoints
base_model:
- lerobot/diffusion_pusht_keypoints
---
# Diffusion PushT-v0 using Keypoints

This repository contains the latest checkpoint of the training visible at: https://wandb.ai/fiatlux/diffusion-pusht-keypoints/workspace?nw=nwuserandrearitossa

I am researching for more efficient ways of training diffusion and therefore I am experimenting with the architecture. As a result to replicate or use the model use this branch of "huggingface/lerobot": https://github.com/the-future-dev/lerobot/tree/cloth-diff


## Demo Video
Here’s a sample output from the model:

<video controls width="550">
  <source src="https://huggingface.co/the-future-dev/diffusion-pusht-keypoints/resolve/main/replay.mp4" type="video/mp4">
  Your browser does not support the video tag.
</video>

## Evaluation

The model was evaluated on the `PushT` environment from [gym-pusht](https://github.com/huggingface/gym-pusht). There are two evaluation metrics on a per-episode basis:

- Maximum overlap with target (seen as `eval/avg_max_reward` in the charts above). This ranges in [0, 1].
- Success: whether or not the maximum overlap is at least 95%.

Here are the metrics for 500 episodes worth of evaluation.

Metric|Average over 500 episodes
-|-
Average max. overlap ratio | 0.9780
Success rate (%) | 86.80%

The results of each of the individual rollouts may be found in [eval_results.json](eval_results.json).