Sam3.1 Gripper Universal

--- license: apache-2.0 tags: - robotics - segmentation - gripper - sam3 - droid - agibot datasets: - custom pipeline_tag: image-segmentation --- # SAM 3.1 Universal Gripper Segmentation Fine-tuned facebook/sam3 (840M params) for universal robot gripper segmentation across DROID and AgiBOT datasets. ## Files | File | Size | Description | |------|------|-------------| | model_weights.pt | 3.14 GB | Recommended: model state_dict only (no optimizer state) | | checkpoint.pt | 9.39 GB | Full training checkpoint (model + optimizer + scheduler, for resuming training) | | configs/gripper_universal.yaml | - | Training configuration (Hydra) | ## Quick Start import torch; from huggingface_hub import hf_hub_download ckpt_path = hf_hub_download("sazirarrwth99/sam3.1-gripper-universal", "model_weights.pt") ckpt = torch.load(ckpt_path, map_location="cpu", weights_only=False) state_dict = ckpt["model"] # 841M parameters, epoch 30 ## Training Details - Base model: facebook/sam3 (SAM 3, 840M params) - Architecture: Sam3Image (ViTDet backbone) - Resolution: 1008x1008 - Datasets: DROID gripper (10,010 images) + RoboEngine/AgiBOT (3,628 images) = 13,667 total - Split: 12,560 train / 1,107 val - Training: 30 epochs, batch size 2, gradient accumulation 4, AMP bfloat16 - Hardware: 1x NVIDIA A100-SXM4-40GB, ~47 hours total - Loss: 62.1 -> 35.7 -> 24.8 -> 22.6 -> 21.9 -> 20.9 (epoch 28 best) -> 21.7 (final) ## Classes 1. robot_arm: Robot arm / manipulator body 2. gripper: End-effector / gripper

Source context: 0 downloads · 0 likes · Pipeline image-segmentation · Repo sazirarrwth99/sam3.1-gripper-universal