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ilessio-aiflowlab
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project_hermes

HERMES
ONNX
PyTorch
navigation
indoor-robotics
scene-understanding
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  1. README.md +52 -0
  2. checkpoints/best.pth +3 -0
  3. configs/training.json +83 -0
  4. logs/training_history.json +602 -0
  5. onnx/hermes_nav_v1.onnx +3 -0
  6. pytorch/hermes_nav_v1.pth +3 -0
  7. pytorch/hermes_nav_v1.safetensors +3 -0
README.md ADDED
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+ ---
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+ tags:
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+ - hermes
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+ - navigation
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+ - indoor-robotics
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+ - scene-understanding
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+ - pytorch
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+ - onnx
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+ license: apache-2.0
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+ ---
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+
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+ # HERMES Navigation Model v1
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+
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+ Indoor semantic navigation model combining vision and 3D point cloud understanding.
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+
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+ ## Architecture
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+ - **Vision encoder**: CNN backbone (5-layer, 256-dim output)
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+ - **Point cloud encoder**: MLP with max-pooling (2048 points → 256-dim)
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+ - **Fusion**: 512-dim MLP with LayerNorm + Dropout
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+ - **Heads**: Direction (3D unit vector) + Traversability (scalar 0-1)
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+
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+ ## Training
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+ - **Dataset**: SUN RGB-D (5,509 indoor scenes)
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+ - **Split**: 90/5/5 (train/val/test)
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+ - **Optimizer**: AdamW (lr=2e-4, cosine schedule)
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+ - **Mixed precision**: bf16 on CUDA
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+
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+ ## Formats
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+ | Format | File | Use Case |
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+ |--------|------|----------|
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+ | PyTorch | `pytorch/hermes_nav_v1.pth` | Training/fine-tuning |
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+ | SafeTensors | `pytorch/hermes_nav_v1.safetensors` | Fast safe loading |
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+ | ONNX | `onnx/hermes_nav_v1.onnx` | Cross-platform inference |
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+
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+ ## Usage
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+ ```python
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+ import torch
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+ from hermes.training.model import HermesNavigationModel
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+
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+ model = HermesNavigationModel()
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+ model.load_state_dict(torch.load("hermes_nav_v1.pth"))
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+ model.eval()
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+
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+ image = torch.randn(1, 3, 256, 256)
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+ points = torch.randn(1, 2048, 3)
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+ output = model(image, points)
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+ # output["direction"]: [1, 3] goal direction
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+ # output["traversability"]: [1, 1] traversability score
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+ ```
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+
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+ ## Citation
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+ ANIMA Suite — Robot Flow Labs
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+ version https://git-lfs.github.com/spec/v1
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+ size 21145133
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+ {
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+ "project": {
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+ "name": "project_hermes",
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+ "version": "v1",
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+ "seed": 42
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+ },
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+ "hardware": {
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+ "model": {
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+ "vlm_name": "HuggingFaceTB/SmolVLM-256M-Instruct",
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+ "vlm_local_path": "/mnt/forge-data/models/HuggingFaceTB--SmolVLM-256M-Instruct",
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+ "llm_name": "HuggingFaceTB/SmolLM2-1.7B-Instruct",
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+ "llm_local_path": "/mnt/forge-data/models/HuggingFaceTB--SmolLM2-1.7B-Instruct",
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+ "freeze_vision_encoder": true,
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+ "gradient_checkpointing": true
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+ "log_every_steps": 50,
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+ "export": {
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