Upload policy weights, train config and readme

README.md

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+---
+datasets: tizzymouse/hil-serl2_cropped_resized
+library_name: lerobot
+license: apache-2.0
+model_name: reward_classifier
+pipeline_tag: robotics
+tags:
+- lerobot
+- reward_classifier
+- robotics
+---
+
+# Model Card for reward_classifier
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+A reward classifier is a lightweight neural network that scores observations or trajectories for task success, providing a learned reward signal or offline evaluation when explicit rewards are unavailable.
+
+
+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

config.json

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+{
+    "type": "reward_classifier",
+    "n_obs_steps": 1,
+    "input_features": {
+        "observation.images.front": {
+            "type": "VISUAL",
+            "shape": [
+                3,
+                128,
+                128
+            ]
+        },
+        "observation.images.wrist": {
+            "type": "VISUAL",
+            "shape": [
+                3,
+                128,
+                128
+            ]
+        },
+        "observation.images.top": {
+            "type": "VISUAL",
+            "shape": [
+                3,
+                128,
+                128
+            ]
+        }
+    },
+    "output_features": {
+        "action": {
+            "type": "ACTION",
+            "shape": [
+                6
+            ]
+        }
+    },
+    "device": "cpu",
+    "use_amp": false,
+    "push_to_hub": true,
+    "repo_id": "tizzymouse/hil-serl2-reward-classifier",
+    "private": null,
+    "tags": null,
+    "license": null,
+    "pretrained_path": null,
+    "name": "reward_classifier",
+    "num_classes": 2,
+    "hidden_dim": 256,
+    "latent_dim": 256,
+    "image_embedding_pooling_dim": 8,
+    "dropout_rate": 0.1,
+    "model_name": "helper2424/resnet10",
+    "model_type": "cnn",
+    "num_cameras": 3,
+    "learning_rate": 0.0001,
+    "weight_decay": 0.01,
+    "grad_clip_norm": 1.0,
+    "normalization_mapping": {
+        "VISUAL": "MEAN_STD"
+    }
+}

model.safetensors

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+oid sha256:546c534d7dd63e3f0f381b61719a1aa7a27700bc9c9e4f8c0659ef57f873a7d0
+size 33810140

train_config.json

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+{
+    "dataset": {
+        "repo_id": "tizzymouse/hil-serl2_cropped_resized",
+        "root": null,
+        "episodes": null,
+        "image_transforms": {
+            "enable": false,
+            "max_num_transforms": 3,
+            "random_order": false,
+            "tfs": {
+                "brightness": {
+                    "weight": 1.0,
+                    "type": "ColorJitter",
+                    "kwargs": {
+                        "brightness": [
+                            0.8,
+                            1.2
+                        ]
+                    }
+                },
+                "contrast": {
+                    "weight": 1.0,
+                    "type": "ColorJitter",
+                    "kwargs": {
+                        "contrast": [
+                            0.8,
+                            1.2
+                        ]
+                    }
+                },
+                "saturation": {
+                    "weight": 1.0,
+                    "type": "ColorJitter",
+                    "kwargs": {
+                        "saturation": [
+                            0.5,
+                            1.5
+                        ]
+                    }
+                },
+                "hue": {
+                    "weight": 1.0,
+                    "type": "ColorJitter",
+                    "kwargs": {
+                        "hue": [
+                            -0.05,
+                            0.05
+                        ]
+                    }
+                },
+                "sharpness": {
+                    "weight": 1.0,
+                    "type": "SharpnessJitter",
+                    "kwargs": {
+                        "sharpness": [
+                            0.5,
+                            1.5
+                        ]
+                    }
+                },
+                "affine": {
+                    "weight": 1.0,
+                    "type": "RandomAffine",
+                    "kwargs": {
+                        "degrees": [
+                            -5.0,
+                            5.0
+                        ],
+                        "translate": [
+                            0.05,
+                            0.05
+                        ]
+                    }
+                }
+            }
+        },
+        "revision": null,
+        "use_imagenet_stats": true,
+        "video_backend": "pyav",
+        "streaming": false
+    },
+    "env": null,
+    "policy": {
+        "type": "reward_classifier",
+        "n_obs_steps": 1,
+        "input_features": {
+            "observation.images.front": {
+                "type": "VISUAL",
+                "shape": [
+                    3,
+                    128,
+                    128
+                ]
+            },
+            "observation.images.wrist": {
+                "type": "VISUAL",
+                "shape": [
+                    3,
+                    128,
+                    128
+                ]
+            },
+            "observation.images.top": {
+                "type": "VISUAL",
+                "shape": [
+                    3,
+                    128,
+                    128
+                ]
+            }
+        },
+        "output_features": {
+            "action": {
+                "type": "ACTION",
+                "shape": [
+                    6
+                ]
+            }
+        },
+        "device": "cpu",
+        "use_amp": false,
+        "push_to_hub": true,
+        "repo_id": "tizzymouse/hil-serl2-reward-classifier",
+        "private": null,
+        "tags": null,
+        "license": null,
+        "pretrained_path": null,
+        "name": "reward_classifier",
+        "num_classes": 2,
+        "hidden_dim": 256,
+        "latent_dim": 256,
+        "image_embedding_pooling_dim": 8,
+        "dropout_rate": 0.1,
+        "model_name": "helper2424/resnet10",
+        "model_type": "cnn",
+        "num_cameras": 3,
+        "learning_rate": 0.0001,
+        "weight_decay": 0.01,
+        "grad_clip_norm": 1.0,
+        "normalization_mapping": {
+            "VISUAL": "MEAN_STD"
+        }
+    },
+    "output_dir": "outputs/train/2025-10-23/13-56-12_hil-serl2-reward-classifier",
+    "job_name": "hil-serl2-reward-classifier",
+    "resume": false,
+    "seed": 2,
+    "num_workers": 4,
+    "batch_size": 16,
+    "steps": 5000,
+    "eval_freq": 1000,
+    "log_freq": 10,
+    "save_checkpoint": true,
+    "save_freq": 1000,
+    "use_policy_training_preset": true,
+    "optimizer": {
+        "type": "adamw",
+        "lr": 0.0001,
+        "weight_decay": 0.01,
+        "grad_clip_norm": 1.0,
+        "betas": [
+            0.9,
+            0.999
+        ],
+        "eps": 1e-08
+    },
+    "scheduler": null,
+    "eval": {
+        "n_episodes": 50,
+        "batch_size": 50,
+        "use_async_envs": false
+    },
+    "wandb": {
+        "enable": false,
+        "disable_artifact": false,
+        "project": "hil-serl2-reward-classifier",
+        "entity": null,
+        "notes": null,
+        "run_id": null,
+        "mode": null
+    },
+    "checkpoint_path": null
+}