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--- |
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license: apache-2.0 |
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datasets: |
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- lerobot/pusht_keypoints |
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base_model: |
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- lerobot/diffusion_pusht_keypoints |
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--- |
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# 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 |
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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 |
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Here’s a sample output from the model: |
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<video controls width="550"> |
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<source src="https://huggingface.co/the-future-dev/diffusion-pusht-keypoints/resolve/main/replay.mp4" type="video/mp4"> |
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Your browser does not support the video tag. |
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</video> |
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## 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]. |
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- 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 |
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Average max. overlap ratio | 0.9780 |
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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). |
