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docs(groot): update training command with image transformation parameters
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+11
-4
@@ -116,11 +116,21 @@ GR00T N1.7 has demonstrated strong performance on the LIBERO benchmark suite. To
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Example training command for a LIBERO suite (here `libero_spatial`):
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```bash
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IMAGE_TRANSFORMS='{
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"brightness": {"weight": 1.0, "type": "ColorJitter", "kwargs": {"brightness": [0.7, 1.3]}},
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"contrast": {"weight": 1.0, "type": "ColorJitter", "kwargs": {"contrast": [0.6, 1.4]}},
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"saturation": {"weight": 1.0, "type": "ColorJitter", "kwargs": {"saturation": [0.5, 1.5]}},
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"hue": {"weight": 1.0, "type": "ColorJitter", "kwargs": {"hue": [-0.08, 0.08]}}
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}'
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lerobot-train \
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--dataset.repo_id=IPEC-COMMUNITY/libero_spatial_no_noops_1.0.0_lerobot \
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--dataset.root=/datasets/libero_spatial \
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--dataset.revision=main \
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--dataset.video_backend=pyav \
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--dataset.image_transforms.enable=true \
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--dataset.image_transforms.max_num_transforms=4 \
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--dataset.image_transforms.tfs="$IMAGE_TRANSFORMS" \
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--policy.type=groot \
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--policy.base_model_path=nvidia/GR00T-N1.7-3B \
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--policy.embodiment_tag=libero_sim \
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@@ -140,10 +150,7 @@ lerobot-train \
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--prefetch_factor=2 \
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--persistent_workers=true \
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--output_dir=$OUTPUT_DIR \
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--job_name=$JOB_NAME \
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--dataset.image_transforms.enable=true \
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--dataset.image_transforms.max_num_transforms=4 \
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--dataset.image_transforms.tfs='{"brightness":{"weight":1.0,"type":"ColorJitter","kwargs":{"brightness":[0.7,1.3]}},"contrast":{"weight":1.0,"type":"ColorJitter","kwargs":{"contrast":[0.6,1.4]}},"saturation":{"weight":1.0,"type":"ColorJitter","kwargs":{"saturation":[0.5,1.5]}},"hue":{"weight":1.0,"type":"ColorJitter","kwargs":{"hue":[-0.08,0.08]}}}'
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--job_name=$JOB_NAME
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```
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### GR00T N1.7 LIBERO Results
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