Upload policy weights, train config and readme

README.md

@@ -0,0 +1,62 @@
+---
+datasets: Basr88/Yellow-HighRez-Dataset
+library_name: lerobot
+license: apache-2.0
+model_name: act
+pipeline_tag: robotics
+tags:
+- lerobot
+- act
+- robotics
+---
+
+# Model Card for act
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+[Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates.
+
+
+This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
+See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
+
+---
+
+## How to Get Started with the Model
+
+For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
+Below is the short version on how to train and run inference/eval:
+
+### Train from scratch
+
+```bash
+lerobot-train \
+  --dataset.repo_id=${HF_USER}/<dataset> \
+  --policy.type=act \
+  --output_dir=outputs/train/<desired_policy_repo_id> \
+  --job_name=lerobot_training \
+  --policy.device=cuda \
+  --policy.repo_id=${HF_USER}/<desired_policy_repo_id>
+  --wandb.enable=true
+```
+
+_Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._
+
+### Evaluate the policy/run inference
+
+```bash
+lerobot-record \
+  --robot.type=so100_follower \
+  --dataset.repo_id=<hf_user>/eval_<dataset> \
+  --policy.path=<hf_user>/<desired_policy_repo_id> \
+  --episodes=10
+```
+
+Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.
+
+---
+
+## Model Details
+
+- **License:** apache-2.0

model.safetensors

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train_config.json

@@ -150,15 +150,16 @@
         "optimizer_weight_decay": 0.0001,
         "optimizer_lr_backbone": 1e-05
     },
-    "output_dir": "outputs/ACT_trainingRez",
+    "output_dir": "/content/drive/MyDrive/highrez1",
     "job_name": "ACT_training",
     "resume": false,
     "seed": 1000,
     "num_workers": 4,
     "batch_size": 16,
-    "steps": 100000,
+    "steps": 50000,
     "eval_freq": 20000,
     "log_freq": 200,
+    "tolerance_s": 0.0001,
     "save_checkpoint": true,
     "save_freq": 20000,
     "use_policy_training_preset": true,