| | --- |
| | 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). |
