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chore: remove usernames + use entrypoints in docs, comments & sample commands (#2988)
This commit is contained in:
Steven Palma
+22
-22
@@ -28,9 +28,9 @@ We don't expect the same optimal settings for a dataset of images from a simulat
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For these reasons, we run this benchmark on four representative datasets:
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- `lerobot/pusht_image`: (96 x 96 pixels) simulation with simple geometric shapes, fixed camera.
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- `aliberts/aloha_mobile_shrimp_image`: (480 x 640 pixels) real-world indoor, moving camera.
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- `aliberts/paris_street`: (720 x 1280 pixels) real-world outdoor, moving camera.
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- `aliberts/kitchen`: (1080 x 1920 pixels) real-world indoor, fixed camera.
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- `lerobot/aloha_mobile_shrimp_image`: (480 x 640 pixels) real-world indoor, moving camera.
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- `lerobot/paris_street`: (720 x 1280 pixels) real-world outdoor, moving camera.
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- `lerobot/kitchen`: (1080 x 1920 pixels) real-world indoor, fixed camera.
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Note: The datasets used for this benchmark need to be image datasets, not video datasets.
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@@ -179,7 +179,7 @@ python benchmark/video/run_video_benchmark.py \
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--output-dir outputs/video_benchmark \
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--repo-ids \
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lerobot/pusht_image \
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aliberts/aloha_mobile_shrimp_image \
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lerobot/aloha_mobile_shrimp_image \
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--vcodec libx264 libx265 \
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--pix-fmt yuv444p yuv420p \
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--g 2 20 None \
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@@ -203,9 +203,9 @@ python benchmark/video/run_video_benchmark.py \
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--output-dir outputs/video_benchmark \
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--repo-ids \
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lerobot/pusht_image \
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aliberts/aloha_mobile_shrimp_image \
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aliberts/paris_street \
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aliberts/kitchen \
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lerobot/aloha_mobile_shrimp_image \
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lerobot/paris_street \
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lerobot/kitchen \
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--vcodec libx264 libx265 \
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--pix-fmt yuv444p yuv420p \
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--g 1 2 3 4 5 6 10 15 20 40 None \
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@@ -221,9 +221,9 @@ python benchmark/video/run_video_benchmark.py \
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--output-dir outputs/video_benchmark \
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--repo-ids \
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lerobot/pusht_image \
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aliberts/aloha_mobile_shrimp_image \
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aliberts/paris_street \
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aliberts/kitchen \
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lerobot/aloha_mobile_shrimp_image \
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lerobot/paris_street \
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lerobot/kitchen \
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--vcodec libsvtav1 \
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--pix-fmt yuv420p \
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--g 1 2 3 4 5 6 10 15 20 40 None \
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@@ -253,36 +253,36 @@ Since we're using av1 encoding, we're choosing the `pyav` decoder as `video_read
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These tables show the results for `g=2` and `crf=30`, using `timestamps-modes=6_frames` and `backend=pyav`
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| video_images_size_ratio | vcodec | pix_fmt | | | |
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| ---------------------------------- | ---------- | ------- | --------- | --------- | --------- |
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| --------------------------------- | ---------- | ------- | --------- | --------- | --------- |
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| | libx264 | | libx265 | | libsvtav1 |
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| repo_id | yuv420p | yuv444p | yuv420p | yuv444p | yuv420p |
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| lerobot/pusht_image | **16.97%** | 17.58% | 18.57% | 18.86% | 22.06% |
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| aliberts/aloha_mobile_shrimp_image | 2.14% | 2.11% | 1.38% | **1.37%** | 5.59% |
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| aliberts/paris_street | 2.12% | 2.13% | **1.54%** | **1.54%** | 4.43% |
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| aliberts/kitchen | 1.40% | 1.39% | **1.00%** | **1.00%** | 2.52% |
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| lerobot/aloha_mobile_shrimp_image | 2.14% | 2.11% | 1.38% | **1.37%** | 5.59% |
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| lerobot/paris_street | 2.12% | 2.13% | **1.54%** | **1.54%** | 4.43% |
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| lerobot/kitchen | 1.40% | 1.39% | **1.00%** | **1.00%** | 2.52% |
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| video_images_load_time_ratio | vcodec | pix_fmt | | | |
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| ---------------------------------- | ------- | ------- | -------- | ------- | --------- |
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| --------------------------------- | ------- | ------- | -------- | ------- | --------- |
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| | libx264 | | libx265 | | libsvtav1 |
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| repo_id | yuv420p | yuv444p | yuv420p | yuv444p | yuv420p |
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| lerobot/pusht_image | 6.45 | 5.19 | **1.90** | 2.12 | 2.47 |
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| aliberts/aloha_mobile_shrimp_image | 11.80 | 7.92 | 0.71 | 0.85 | **0.48** |
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| aliberts/paris_street | 2.21 | 2.05 | 0.36 | 0.49 | **0.30** |
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| aliberts/kitchen | 1.46 | 1.46 | 0.28 | 0.51 | **0.26** |
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| lerobot/aloha_mobile_shrimp_image | 11.80 | 7.92 | 0.71 | 0.85 | **0.48** |
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| lerobot/paris_street | 2.21 | 2.05 | 0.36 | 0.49 | **0.30** |
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| lerobot/kitchen | 1.46 | 1.46 | 0.28 | 0.51 | **0.26** |
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| | | vcodec | pix_fmt | | | |
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| ---------------------------------- | -------- | -------- | ------------ | -------- | --------- | ------------ |
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| --------------------------------- | -------- | -------- | ------------ | -------- | --------- | ------------ |
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| | | libx264 | | libx265 | | libsvtav1 |
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| repo_id | metric | yuv420p | yuv444p | yuv420p | yuv444p | yuv420p |
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| lerobot/pusht_image | avg_mse | 2.90E-04 | **2.03E-04** | 3.13E-04 | 2.29E-04 | 2.19E-04 |
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| | avg_psnr | 35.44 | 37.07 | 35.49 | **37.30** | 37.20 |
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| | avg_ssim | 98.28% | **98.85%** | 98.31% | 98.84% | 98.72% |
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| aliberts/aloha_mobile_shrimp_image | avg_mse | 2.76E-04 | 2.59E-04 | 3.17E-04 | 3.06E-04 | **1.30E-04** |
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| lerobot/aloha_mobile_shrimp_image | avg_mse | 2.76E-04 | 2.59E-04 | 3.17E-04 | 3.06E-04 | **1.30E-04** |
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| | avg_psnr | 35.91 | 36.21 | 35.88 | 36.09 | **40.17** |
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| | avg_ssim | 95.19% | 95.18% | 95.00% | 95.05% | **97.73%** |
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| aliberts/paris_street | avg_mse | 6.89E-04 | 6.70E-04 | 4.03E-03 | 4.02E-03 | **3.09E-04** |
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| lerobot/paris_street | avg_mse | 6.89E-04 | 6.70E-04 | 4.03E-03 | 4.02E-03 | **3.09E-04** |
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| | avg_psnr | 33.48 | 33.68 | 32.05 | 32.15 | **35.40** |
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| | avg_ssim | 93.76% | 93.75% | 89.46% | 89.46% | **95.46%** |
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| aliberts/kitchen | avg_mse | 2.50E-04 | 2.24E-04 | 4.28E-04 | 4.18E-04 | **1.53E-04** |
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| lerobot/kitchen | avg_mse | 2.50E-04 | 2.24E-04 | 4.28E-04 | 4.18E-04 | **1.53E-04** |
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| | avg_psnr | 36.73 | 37.33 | 36.56 | 36.75 | **39.12** |
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| | avg_ssim | 95.47% | 95.58% | 95.52% | 95.53% | **96.82%** |
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@@ -185,7 +185,7 @@ echo $HF_USER
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Use the standard recording command:
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```bash
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python src/lerobot/scripts/lerobot_record.py \
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lerobot-record \
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--robot.type=earthrover_mini_plus \
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--teleop.type=keyboard_rover \
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--dataset.repo_id=your_username/dataset_name \
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@@ -224,7 +224,7 @@ lerobot-record \
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--teleop.port=/dev/tty.usbmodem1201 \
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--teleop.id=right \
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--teleop.side=right \
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--dataset.repo_id=nepyope/hand_record_test_with_video_data \
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--dataset.repo_id=<USER>/hand_record_test_with_video_data \
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--dataset.single_task="Hand recording test with video data" \
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--dataset.num_episodes=1 \
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--dataset.episode_time_s=5 \
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@@ -241,7 +241,7 @@ lerobot-replay \
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--robot.port=/dev/tty.usbmodem58760432281 \
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--robot.id=right \
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--robot.side=right \
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--dataset.repo_id=nepyope/hand_record_test_with_camera \
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--dataset.repo_id=<USER>/hand_record_test_with_camera \
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--dataset.episode=0
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```
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@@ -249,13 +249,13 @@ lerobot-replay \
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```bash
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lerobot-train \
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--dataset.repo_id=nepyope/hand_record_test_with_video_data \
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--dataset.repo_id=<USER>/hand_record_test_with_video_data \
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--policy.type=act \
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--output_dir=outputs/train/hopejr_hand \
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--job_name=hopejr \
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--policy.device=mps \
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--wandb.enable=true \
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--policy.repo_id=nepyope/hand_test_policy
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--policy.repo_id=<USER>/hand_test_policy
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```
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### Evaluate
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@@ -270,7 +270,7 @@ lerobot-record \
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--robot.side=right \
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--robot.cameras='{"main": {"type": "opencv", "index_or_path": 0, "width": 640, "height": 480, "fps": 30}}' \
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--display_data=false \
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--dataset.repo_id=nepyope/eval_hopejr \
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--dataset.repo_id=<USER>/eval_hopejr \
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--dataset.single_task="Evaluate hopejr hand policy" \
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--dataset.num_episodes=10 \
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--policy.path=outputs/train/hopejr_hand/checkpoints/last/pretrained_model
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+1
-1
@@ -60,7 +60,7 @@ policy.type=pi0
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For training π₀, you can use the standard LeRobot training script with the appropriate configuration:
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```bash
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python src/lerobot/scripts/lerobot_train.py \
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lerobot-train \
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--dataset.repo_id=your_dataset \
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--policy.type=pi0 \
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--output_dir=./outputs/pi0_training \
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@@ -56,7 +56,7 @@ policy.type=pi05
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Here's a complete training command for finetuning the base π₀.₅ model on your own dataset:
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```bash
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python src/lerobot/scripts/lerobot_train.py\
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lerobot-train \
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--dataset.repo_id=your_dataset \
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--policy.type=pi05 \
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--output_dir=./outputs/pi05_training \
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@@ -269,7 +269,7 @@ This generates visualizations showing video frames with subtask boundaries overl
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Train with **no annotations** - uses linear progress from 0 to 1:
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```bash
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python src/lerobot/scripts/lerobot_train.py \
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lerobot-train \
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--dataset.repo_id=your-username/your-dataset \
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--policy.type=sarm \
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--policy.annotation_mode=single_stage \
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@@ -288,7 +288,7 @@ python src/lerobot/scripts/lerobot_train.py \
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Train with **dense annotations only** (sparse auto-generated):
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```bash
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python src/lerobot/scripts/lerobot_train.py \
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lerobot-train \
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--dataset.repo_id=your-username/your-dataset \
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--policy.type=sarm \
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--policy.annotation_mode=dense_only \
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@@ -307,7 +307,7 @@ python src/lerobot/scripts/lerobot_train.py \
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Train with **both sparse and dense annotations**:
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```bash
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python src/lerobot/scripts/lerobot_train.py \
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lerobot-train \
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--dataset.repo_id=your-username/your-dataset \
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--policy.type=sarm \
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--policy.annotation_mode=dual \
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@@ -468,7 +468,7 @@ This script:
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Once you have the progress file, train your policy with RA-BC weighting. The progress file is auto-detected from the dataset path (`sarm_progress.parquet`). Currently PI0, PI0.5 and SmolVLA are supported with RA-BC:
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```bash
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python src/lerobot/scripts/lerobot_train.py \
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lerobot-train \
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--dataset.repo_id=your-username/your-dataset \
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--policy.type=pi0 \
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--use_rabc=true \
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@@ -216,7 +216,7 @@ lerobot-teleoperate \
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### Record Dataset in Simulation
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```bash
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python -m lerobot.scripts.lerobot_record \
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lerobot-record \
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--robot.type=unitree_g1 \
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--robot.is_simulation=true \
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--robot.cameras='{"global_view": {"type": "zmq", "server_address": "localhost", "port": 5555, "camera_name": "head_camera", "width": 640, "height": 480, "fps": 30}}' \
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@@ -266,7 +266,7 @@ lerobot-teleoperate \
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### Record Dataset on Real Robot
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```bash
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python -m lerobot.scripts.lerobot_record \
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lerobot-record \
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--robot.type=unitree_g1 \
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--robot.is_simulation=false \
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--robot.cameras='{"global_view": {"type": "zmq", "server_address": "172.18.129.215", "port": 5555, "camera_name": "head_camera", "width": 640, "height": 480, "fps": 30}}' \
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@@ -45,7 +45,7 @@ policy.type=wall_x
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For training WallX, you can use the standard LeRobot training script with the appropriate configuration:
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```bash
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python src/lerobot/scripts/lerobot_train.py \
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lerobot-train \
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--dataset.repo_id=your_dataset \
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--policy.type=wall_x \
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--output_dir=./outputs/wallx_training \
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@@ -154,7 +154,7 @@ lerobot-train \
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```bash
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lerobot-train \
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--dataset.repo_id=pepijn223/bimanual-so100-handover-cube \
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--dataset.repo_id=<USER>/bimanual-so100-handover-cube \
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--output_dir=./outputs/xvla_bimanual \
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--job_name=xvla_so101_training \
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--policy.path="lerobot/xvla-base" \
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@@ -22,7 +22,7 @@ lerobot-replay \
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--robot.type=so100_follower \
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--robot.port=/dev/tty.usbmodem58760431541 \
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--robot.id=black \
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--dataset.repo_id=aliberts/record-test \
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--dataset.repo_id=<USER>/record-test \
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--dataset.episode=2
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```
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"""
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@@ -27,8 +27,8 @@ measuring consistency and ground truth alignment.
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Usage:
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# Basic usage with smolvla policy
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uv run python examples/rtc/eval_dataset.py \
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--policy.path=helper2424/smolvla_check_rtc_last3 \
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--dataset.repo_id=helper2424/check_rtc \
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--policy.path=<USER>/smolvla_check_rtc_last3 \
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--dataset.repo_id=<USER>/check_rtc \
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--rtc.execution_horizon=8 \
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--device=mps \
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--rtc.max_guidance_weight=10.0 \
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@@ -58,16 +58,16 @@ Usage:
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--device=cuda
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uv run python examples/rtc/eval_dataset.py \
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--policy.path=lipsop/reuben_pi0 \
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--dataset.repo_id=ReubenLim/so101_cube_in_cup \
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--policy.path=<USER>/reuben_pi0 \
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--dataset.repo_id=<USER>/so101_cube_in_cup \
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--rtc.execution_horizon=8 \
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--device=cuda
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# With torch.compile for faster inference (PyTorch 2.0+)
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# Note: CUDA graphs disabled by default due to in-place ops in denoising loop
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uv run python examples/rtc/eval_dataset.py \
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--policy.path=helper2424/smolvla_check_rtc_last3 \
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--dataset.repo_id=helper2424/check_rtc \
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--policy.path=<USER>/smolvla_check_rtc_last3 \
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--dataset.repo_id=<USER>/check_rtc \
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--rtc.execution_horizon=8 \
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--device=mps \
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--use_torch_compile=true \
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@@ -75,8 +75,8 @@ Usage:
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# With torch.compile on CUDA (CUDA graphs disabled by default)
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uv run python examples/rtc/eval_dataset.py \
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--policy.path=helper2424/smolvla_check_rtc_last3 \
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--dataset.repo_id=helper2424/check_rtc \
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--policy.path=<USER>/smolvla_check_rtc_last3 \
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--dataset.repo_id=<USER>/check_rtc \
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--rtc.execution_horizon=8 \
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--device=cuda \
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--use_torch_compile=true \
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@@ -84,8 +84,8 @@ Usage:
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# Enable CUDA graphs (advanced - may cause tensor aliasing errors)
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uv run python examples/rtc/eval_dataset.py \
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--policy.path=helper2424/smolvla_check_rtc_last3 \
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--dataset.repo_id=helper2424/check_rtc \
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--policy.path=<USER>/smolvla_check_rtc_last3 \
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--dataset.repo_id=<USER>/check_rtc \
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--use_torch_compile=true \
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--torch_compile_backend=inductor \
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--torch_compile_mode=max-autotune \
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@@ -28,7 +28,7 @@ For simulation environments, see eval_with_simulation.py
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Usage:
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# Run RTC with Real robot with RTC
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uv run examples/rtc/eval_with_real_robot.py \
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--policy.path=helper2424/smolvla_check_rtc_last3 \
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--policy.path=<USER>/smolvla_check_rtc_last3 \
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--policy.device=mps \
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--rtc.enabled=true \
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--rtc.execution_horizon=20 \
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@@ -41,7 +41,7 @@ Usage:
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# Run RTC with Real robot without RTC
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uv run examples/rtc/eval_with_real_robot.py \
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--policy.path=helper2424/smolvla_check_rtc_last3 \
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--policy.path=<USER>/smolvla_check_rtc_last3 \
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--policy.device=mps \
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--rtc.enabled=false \
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--robot.type=so100_follower \
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@@ -53,7 +53,7 @@ Usage:
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# Run RTC with Real robot with pi0.5 policy
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uv run examples/rtc/eval_with_real_robot.py \
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--policy.path=helper2424/pi05_check_rtc \
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--policy.path=<USER>/pi05_check_rtc \
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--policy.device=mps \
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--rtc.enabled=true \
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--rtc.execution_horizon=20 \
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@@ -529,7 +529,7 @@ if __name__ == "__main__":
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type=str,
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required=True,
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help="Repository identifier on Hugging Face: a community or a user name `/` the name of the dataset "
|
||||
"(e.g. `lerobot/pusht`, `cadene/aloha_sim_insertion_human`).",
|
||||
"(e.g. `lerobot/pusht`, `<USER>/aloha_sim_insertion_human`).",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--branch",
|
||||
|
||||
@@ -27,18 +27,18 @@ Usage:
|
||||
# Full RA-BC computation with visualizations
|
||||
python src/lerobot/policies/sarm/compute_rabc_weights.py \\
|
||||
--dataset-repo-id lerobot/aloha_sim_insertion_human \\
|
||||
--reward-model-path pepijn223/sarm_single_uni4
|
||||
--reward-model-path <USER>/sarm_single_uni4
|
||||
|
||||
# Faster computation with stride (compute every 5 frames, interpolate the rest)
|
||||
python src/lerobot/policies/sarm/compute_rabc_weights.py \\
|
||||
--dataset-repo-id lerobot/aloha_sim_insertion_human \\
|
||||
--reward-model-path pepijn223/sarm_single_uni4 \\
|
||||
--reward-model-path <USER>/sarm_single_uni4 \\
|
||||
--stride 5
|
||||
|
||||
# Visualize predictions only (no RA-BC computation)
|
||||
python src/lerobot/policies/sarm/compute_rabc_weights.py \\
|
||||
--dataset-repo-id lerobot/aloha_sim_insertion_human \\
|
||||
--reward-model-path pepijn223/sarm_single_uni4 \\
|
||||
--reward-model-path <USER>/sarm_single_uni4 \\
|
||||
--visualize-only \\
|
||||
--num-visualizations 5
|
||||
|
||||
@@ -714,12 +714,12 @@ Examples:
|
||||
# Full RA-BC computation with visualizations
|
||||
python src/lerobot/policies/sarm/compute_rabc_weights.py \\
|
||||
--dataset-repo-id lerobot/aloha_sim_insertion_human \\
|
||||
--reward-model-path pepijn223/sarm_single_uni4
|
||||
--reward-model-path <USER>/sarm_single_uni4
|
||||
|
||||
# Visualize predictions only (no RA-BC computation)
|
||||
python src/lerobot/policies/sarm/compute_rabc_weights.py \\
|
||||
--dataset-repo-id lerobot/aloha_sim_insertion_human \\
|
||||
--reward-model-path pepijn223/sarm_single_uni4 \\
|
||||
--reward-model-path <USER>/sarm_single_uni4 \\
|
||||
--visualize-only \\
|
||||
--num-visualizations 10
|
||||
""",
|
||||
|
||||
@@ -30,7 +30,7 @@ Example of finetuning the smolvla pretrained model (`smolvla_base`):
|
||||
```bash
|
||||
lerobot-train \
|
||||
--policy.path=lerobot/smolvla_base \
|
||||
--dataset.repo_id=danaaubakirova/svla_so100_task1_v3 \
|
||||
--dataset.repo_id=<USER>/svla_so100_task1_v3 \
|
||||
--batch_size=64 \
|
||||
--steps=200000
|
||||
```
|
||||
@@ -40,7 +40,7 @@ and an action expert.
|
||||
```bash
|
||||
lerobot-train \
|
||||
--policy.type=smolvla \
|
||||
--dataset.repo_id=danaaubakirova/svla_so100_task1_v3 \
|
||||
--dataset.repo_id=<USER>/svla_so100_task1_v3 \
|
||||
--batch_size=64 \
|
||||
--steps=200000
|
||||
```
|
||||
|
||||
@@ -24,100 +24,100 @@ When new_repo_id is specified, creates a new dataset.
|
||||
Usage Examples:
|
||||
|
||||
Delete episodes 0, 2, and 5 from a dataset:
|
||||
python -m lerobot.scripts.lerobot_edit_dataset \
|
||||
lerobot-edit-dataset \
|
||||
--repo_id lerobot/pusht \
|
||||
--operation.type delete_episodes \
|
||||
--operation.episode_indices "[0, 2, 5]"
|
||||
|
||||
Delete episodes and save to a new dataset:
|
||||
python -m lerobot.scripts.lerobot_edit_dataset \
|
||||
lerobot-edit-dataset \
|
||||
--repo_id lerobot/pusht \
|
||||
--new_repo_id lerobot/pusht_filtered \
|
||||
--operation.type delete_episodes \
|
||||
--operation.episode_indices "[0, 2, 5]"
|
||||
|
||||
Split dataset by fractions:
|
||||
python -m lerobot.scripts.lerobot_edit_dataset \
|
||||
lerobot-edit-dataset \
|
||||
--repo_id lerobot/pusht \
|
||||
--operation.type split \
|
||||
--operation.splits '{"train": 0.8, "val": 0.2}'
|
||||
|
||||
Split dataset by episode indices:
|
||||
python -m lerobot.scripts.lerobot_edit_dataset \
|
||||
lerobot-edit-dataset \
|
||||
--repo_id lerobot/pusht \
|
||||
--operation.type split \
|
||||
--operation.splits '{"train": [0, 1, 2, 3], "val": [4, 5]}'
|
||||
|
||||
Split into more than two splits:
|
||||
python -m lerobot.scripts.lerobot_edit_dataset \
|
||||
lerobot-edit-dataset \
|
||||
--repo_id lerobot/pusht \
|
||||
--operation.type split \
|
||||
--operation.splits '{"train": 0.6, "val": 0.2, "test": 0.2}'
|
||||
|
||||
Merge multiple datasets:
|
||||
python -m lerobot.scripts.lerobot_edit_dataset \
|
||||
lerobot-edit-dataset \
|
||||
--repo_id lerobot/pusht_merged \
|
||||
--operation.type merge \
|
||||
--operation.repo_ids "['lerobot/pusht_train', 'lerobot/pusht_val']"
|
||||
|
||||
Remove camera feature:
|
||||
python -m lerobot.scripts.lerobot_edit_dataset \
|
||||
lerobot-edit-dataset \
|
||||
--repo_id lerobot/pusht \
|
||||
--operation.type remove_feature \
|
||||
--operation.feature_names "['observation.images.top']"
|
||||
|
||||
Modify tasks - set a single task for all episodes (WARNING: modifies in-place):
|
||||
python -m lerobot.scripts.lerobot_edit_dataset \
|
||||
lerobot-edit-dataset \
|
||||
--repo_id lerobot/pusht \
|
||||
--operation.type modify_tasks \
|
||||
--operation.new_task "Pick up the cube and place it"
|
||||
|
||||
Modify tasks - set different tasks for specific episodes (WARNING: modifies in-place):
|
||||
python -m lerobot.scripts.lerobot_edit_dataset \
|
||||
lerobot-edit-dataset \
|
||||
--repo_id lerobot/pusht \
|
||||
--operation.type modify_tasks \
|
||||
--operation.episode_tasks '{"0": "Task A", "1": "Task B", "2": "Task A"}'
|
||||
|
||||
Modify tasks - set default task with overrides for specific episodes (WARNING: modifies in-place):
|
||||
python -m lerobot.scripts.lerobot_edit_dataset \
|
||||
lerobot-edit-dataset \
|
||||
--repo_id lerobot/pusht \
|
||||
--operation.type modify_tasks \
|
||||
--operation.new_task "Default task" \
|
||||
--operation.episode_tasks '{"5": "Special task for episode 5"}'
|
||||
|
||||
Convert image dataset to video format and save locally:
|
||||
python -m lerobot.scripts.lerobot_edit_dataset \
|
||||
lerobot-edit-dataset \
|
||||
--repo_id lerobot/pusht_image \
|
||||
--operation.type convert_image_to_video \
|
||||
--operation.output_dir /path/to/output/pusht_video
|
||||
|
||||
Convert image dataset to video format and save with new repo_id:
|
||||
python -m lerobot.scripts.lerobot_edit_dataset \
|
||||
lerobot-edit-dataset \
|
||||
--repo_id lerobot/pusht_image \
|
||||
--new_repo_id lerobot/pusht_video \
|
||||
--operation.type convert_image_to_video
|
||||
|
||||
Convert image dataset to video format and push to hub:
|
||||
python -m lerobot.scripts.lerobot_edit_dataset \
|
||||
lerobot-edit-dataset \
|
||||
--repo_id lerobot/pusht_image \
|
||||
--new_repo_id lerobot/pusht_video \
|
||||
--operation.type convert_image_to_video \
|
||||
--push_to_hub true
|
||||
|
||||
Show dataset information:
|
||||
python -m lerobot.scripts.lerobot_edit_dataset \
|
||||
lerobot-edit-dataset \
|
||||
--repo_id lerobot/pusht_image \
|
||||
--operation.type info \
|
||||
--operation.show_features true
|
||||
|
||||
Show dataset information without feature details:
|
||||
python -m lerobot.scripts.lerobot_edit_dataset \
|
||||
lerobot-edit-dataset \
|
||||
--repo_id lerobot/pusht_image \
|
||||
--operation.type info \
|
||||
--operation.show_features false
|
||||
|
||||
Using JSON config file:
|
||||
python -m lerobot.scripts.lerobot_edit_dataset \
|
||||
lerobot-edit-dataset \
|
||||
--config_path path/to/edit_config.json
|
||||
"""
|
||||
|
||||
|
||||
@@ -22,7 +22,7 @@ lerobot-replay \
|
||||
--robot.type=so100_follower \
|
||||
--robot.port=/dev/tty.usbmodem58760431541 \
|
||||
--robot.id=black \
|
||||
--dataset.repo_id=aliberts/record-test \
|
||||
--dataset.repo_id=<USER>/record-test \
|
||||
--dataset.episode=0
|
||||
```
|
||||
|
||||
|
||||
Reference in New Issue
Block a user