| --- |
| license: apache-2.0 |
| datasets: |
| - lerobot/pusht_keypoints |
| base_model: |
| - lerobot/diffusion_pusht_keypoints |
| --- |
| # Diffusion PushT-v0 using Keypoints |
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| 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 |
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| ## 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 |
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| 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: |
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| - 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%. |
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| Here are the metrics for 500 episodes worth of evaluation. |
|
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| Metric|Average over 500 episodes |
| -|- |
| Average max. overlap ratio | 0.9780 |
| Success rate (%) | 86.80% |
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| The results of each of the individual rollouts may be found in [eval_results.json](eval_results.json). |
