Updating model card with initial edits

README.md

@@ -1,3 +1,53 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+datasets:
+- lerobot/pusht
+pipeline_tag: robotics
+---
+# Model Card for Diffusion Policy / PushT
+
+Transformer basd Diffusion Policy (as per [Diffusion Policy: Visuomotor Policy
+Learning via Action Diffusion](https://arxiv.org/abs/2303.04137)) 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
+
+The model was trained using [LeRobot's training script](https://github.com/huggingface/lerobot/blob/d747195c5733c4f68d4bfbe62632d6fc1b605712/lerobot/scripts/train.py) and with the [pusht](https://huggingface.co/datasets/lerobot/pusht/tree/v1.3) dataset, using this command:
+
+```bash
+python lerobot/scripts/train.py \
+  hydra.run.dir=outputs/train/diffusion_pusht \
+  hydra.job.name=diffusion_pusht \
+  policy=diffusion training.save_model=true \
+  env=pusht \
+  env.task=PushT-v0 \
+  dataset_repo_id=lerobot/pusht \
+  training.save_freq=25000 \
+  training.eval_freq=10000 \
+  wandb.enable=true \
+  device=cuda
+```
+
+
+The training and eval curves may be found at https://api.wandb.ai/links/none7/rd2trav7
+
+This took about 6 hours to train on an Nvida Tesla P100 GPU.
+
+## Evaluation
+
+The model was evaluated on the `PushT` environment from [gym-pusht](https://github.com/huggingface/gym-pusht) and compared to a similar model trained with the original [Diffusion Policy code](https://github.com/real-stanford/diffusion_policy). 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|Theirs
+-|-|-
+Average max. overlap ratio | 0.000 | 0.000
+Success rate for 500 episodes (%) | 0.00 | 0.00
+
+The results of each of the individual rollouts may be found in [eval_info.json](eval_info.json).