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README.md
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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
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To train the model:
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```
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python lerobot/scripts/train.py
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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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```
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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:
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