Robotics
Transformers
Safetensors
Inference Endpoints
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@@ -14,8 +14,6 @@ See the [LeRobot library](https://github.com/huggingface/lerobot) (particularly
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  ## Training Details
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- Trained with [LeRobot@342f429](https://github.com/huggingface/lerobot/tree/342f429f1c321a2b4501c3007b1dacba7244b469).
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-
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  The model was trained using this command:
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  ```bash
@@ -26,6 +24,8 @@ python lerobot/scripts/train.py \
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  device=cuda
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  ```
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  ## Model Size
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  <blank>|Number of Parameters
@@ -40,9 +40,11 @@ The model was evaluated on the `PushT` environment from [gym-pusht](https://gith
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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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- The results of each of the individual rollouts may be found in [eval_info.json](eval_info.json).
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  <blank>|Ours
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  -|-
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  Average max. overlap ratio for 500 episodes | 0.887
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  Success rate for 500 episodes (%) | 66.0
 
 
 
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  ## Training Details
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  The model was trained using this command:
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  ```bash
 
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  device=cuda
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  ```
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+ This took about 7 hours to train on an Nvida A6000.
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+
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  ## Model Size
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  <blank>|Number of Parameters
 
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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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  <blank>|Ours
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  -|-
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  Average max. overlap ratio for 500 episodes | 0.887
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  Success rate for 500 episodes (%) | 66.0
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+
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+ The results of each of the individual rollouts may be found in [eval_info.json](eval_info.json).