Instructions to use VEXAutoSort/gear_sortV3_dot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use VEXAutoSort/gear_sortV3_dot with LeRobot:
- Notebooks
- Google Colab
- Kaggle
File size: 1,728 Bytes
97594a6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 | {
"type": "dot",
"n_obs_steps": 3,
"input_features": {
"observation.state": {
"type": "STATE",
"shape": [
6
]
},
"observation.images.top_view": {
"type": "VISUAL",
"shape": [
3,
600,
960
]
}
},
"output_features": {
"action": {
"type": "ACTION",
"shape": [
6
]
}
},
"device": "cuda",
"use_amp": false,
"use_peft": false,
"push_to_hub": true,
"repo_id": "VEXAutoSort/gear_sortV3_dot",
"private": null,
"tags": null,
"license": null,
"pretrained_path": null,
"pretrained_revision": null,
"train_horizon": 100,
"inference_horizon": 100,
"lookback_obs_steps": 30,
"lookback_aug": 2,
"normalization_mapping": {
"VISUAL": "MEAN_STD",
"STATE": "MIN_MAX",
"ENV": "MIN_MAX",
"ACTION": "MIN_MAX"
},
"vision_backbone": "resnet18",
"pretrained_backbone_weights": "ResNet18_Weights.IMAGENET1K_V1",
"pre_norm": true,
"lora_rank": 20,
"merge_lora": false,
"dim_model": 128,
"n_heads": 8,
"dim_feedforward": 512,
"n_decoder_layers": 8,
"rescale_shape": [
150,
240
],
"crop_scale": 0.8,
"state_noise": 0.01,
"noise_decay": 0.999995,
"dropout": 0.1,
"alpha": 0.75,
"train_alpha": 0.9,
"predict_every_n": 1,
"return_every_n": 1,
"optimizer_lr": 0.0001,
"optimizer_min_lr": 1e-05,
"optimizer_lr_cycle_steps": 80000,
"optimizer_warmup_steps": 1000,
"optimizer_weight_decay": 1e-05
} |