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@@ -19,18 +19,43 @@ This model is trained using the [LeRobot library](https://huggingface.co/lerobot
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  This is the best checkpoint for the model. These results are achievable assuming we have reliable validation and can select the best checkpoint based on the validation results (not always the case in robotics). If you are interested in more stable and reproducible results achievable without checkpoint selection, please refer to https://huggingface.co/IliaLarchenko/dot_pusht_keypoints
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- You can use this model by installing LeRobot from this branch: https://github.com/IliaLarchenko/lerobot/tree/dot
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  To train the model:
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- ```
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- python lerobot/scripts/train.py policy=dot_pusht_keypoints env=pusht env.gym.obs_type=environment_state_agent_pos
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ```
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  To evaluate the model:
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  ```bash
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- python lerobot/scripts/eval.py -p IliaLarchenko/dot_pusht_keypoints_best eval.n_episodes=1000 eval.batch_size=100 seed=1000000
 
 
 
 
 
 
 
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  ```
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  Model size:
 
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  This is the best checkpoint for the model. These results are achievable assuming we have reliable validation and can select the best checkpoint based on the validation results (not always the case in robotics). If you are interested in more stable and reproducible results achievable without checkpoint selection, please refer to https://huggingface.co/IliaLarchenko/dot_pusht_keypoints
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+ You can use this model by installing LeRobot from [this branch](https://github.com/IliaLarchenko/lerobot/tree/dot_new_config)
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  To train the model:
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+ ```bash
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+ python lerobot/scripts/train.py \
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+ --policy.type=dot \
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+ --dataset.repo_id=lerobot/pusht_keypoints \
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+ --env.type=pusht \
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+ --env.task=PushT-v0 \
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+ --output_dir=outputs/train/pusht_keyponts \
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+ --batch_size=24 \
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+ --log_freq=1000 \
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+ --eval_freq=10000 \
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+ --save_freq=50000 \
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+ --offline.steps=1000000 \
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+ --seed=100000 \
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+ --wandb.enable=true \
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+ --num_workers=24 \
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+ --use_amp=true \
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+ --device=cuda \
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+ --policy.return_every_n=2 \
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+ --policy.train_horizon=30 \
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+ --policy.inference_horizon=30
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  ```
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  To evaluate the model:
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  ```bash
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+ python lerobot/scripts/eval.py \
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+ --policy.path=IliaLarchenko/dot_pusht_keypoints_best \
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+ --env.type=pusht \
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+ --env.task=PushT-v0 \
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+ --eval.n_episodes=1000 \
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+ --eval.batch_size=100 \
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+ --env.obs_type=environment_state_agent_pos \
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+ --seed=1000000
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  ```
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  Model size: