Update README.md

README.md

@@ -1,3 +1,54 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+datasets:
+- JayLee131/vqbet_pusht
+pipeline_tag: robotics
+---
+# Model Card for VQ-BeT/PushT
+
+VQ-BeT (as per [Behavior Generation with Latent Actions](https://arxiv.org/abs/2403.03181)) trained for the `PushT` environment from [gym-pusht](https://github.com/huggingface/gym-pusht).
+
+## How to Get Started with the Model
+
+See the [LeRobot library](https://github.com/huggingface/lerobot) (particularly the [evaluation script](https://github.com/huggingface/lerobot/blob/main/lerobot/scripts/eval.py)) for instructions on how to load and evaluate this model.
+
+## Training Details
+
+Trained with [LeRobot@342f429](https://github.com/huggingface/lerobot/tree/342f429f1c321a2b4501c3007b1dacba7244b469).
+
+The model was trained using this command:
+
+```bash
+python lerobot/scripts/train.py \
+  policy=vqbet \
+  env=pusht dataset_repo_id=lerobot/pusht \
+  wandb.enable=true \
+  device=cuda
+```
+
+The training curves may be found at https://wandb.ai/jaylee0301/lerobot/runs/9r0ndphr?nw=nwuserjaylee0301.
+
+Training VQ-BeT on PushT took about 7-8 hours to train on an Nvida A6000.
+
+## Model Size
+
+<blank>|Number of Parameters
+-|-
+RGB Encoder | 11.2M
+Remaining VQ-BeT Parts | 26.3M
+
+## Evaluation
+
+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:
+
+- 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%.
+
+Here are the metrics for 500 episodes worth of evaluation.
+
+<blank>|Ours
+-|-
+Average max. overlap ratio for 500 episodes | 0.895
+Success rate for 500 episodes (%) | 63.8
+
+The results of each of the individual rollouts may be found in [eval_info.json](eval_info.json).