{
  "model_name": "ShadeNet-5-35M",
  "task": "intrinsic-decomposition",
  "architecture": "ParallelUNet + frozen DINOv2 ViT-S/14",
  "backbone": "facebook/dinov2-small",
  "parameters": 34652431,
  "trainable_parameters": 12595855,
  "image_size": 448,
  "supported_image_sizes": [
    448,
    896
  ],
  "input": {
    "name": "input_rgb",
    "channels": 3,
    "range": [
      -1.0,
      1.0
    ],
    "note": "RGB photo, resized and padded to a supported size"
  },
  "output": {
    "name": "maps",
    "channels": 7,
    "range": [
      -1.0,
      1.0
    ],
    "layout": {
      "albedo": [
        0,
        3
      ],
      "depth": [
        3,
        4
      ],
      "normal": [
        4,
        7
      ]
    },
    "note": "depth is relative; normals are x right, y up, z toward the viewer"
  },
  "checkpoint": "checkpoints/best-l1-pruned10-ep19.ckpt",
  "inference_script": "inference/run_onnx.py",
  "onnx": {
    "onnx/shadenet5_mix_int4_int8.onnx": "ViT fc1/fc2 int8, everything else int4",
    "onnx/shadenet5_mix2_int4_int8.onnx": "ViT fc2 int8, everything else int4",
    "onnx/shadenet5_refiner.onnx": "the small refiner, fp32, fixed 448x448 (inputs photo and maps, output refined maps)",
    "onnx/shadenet5_mix_refined.onnx": "the MIX model + the refiner + clamp + unit-length normals in one file: input `input_rgb` [1,3,448,448] in [-1,1], output `refined` [1,7,448,448]"
  },
  "recommended_inference": {
    "mode": "refine",
    "command": "python inference/run_onnx.py <photos> --refine --out out/",
    "six_pass_alternative": {
      "flip_avg": true,
      "rot_avg_deg": 4.0,
      "unit_normals": true,
      "forward_passes": 6,
      "command": "python inference/run_onnx.py <photos> --enhanced --out out/"
    },
    "single_file": "onnx/shadenet5_mix_refined.onnx",
    "single_file_command": "python inference/run_onnx.py <photos> --merged --out out/"
  },
  "validation_mean_l1": {
    "single_pass_unit_normals": 0.1757,
    "single_pass_plus_refiner": 0.1673,
    "six_passes": 0.1628,
    "mix_onnx_single": 0.1755,
    "mix_onnx_plus_refiner": 0.1676,
    "mix_onnx_six_passes": 0.1645,
    "mix_onnx_single_merged_file": 0.1676
  },
  "refiner": {
    "pytorch": "refiner/refiner.pt",
    "onnx": "onnx/shadenet5_refiner.onnx",
    "parameters": 910000,
    "training_photos": "Open Images V7 (CC BY 2.0), credited in refiner/attribution.csv",
    "note": "applied to the single-pass maps; trained to reproduce the six-pass average"
  }
}