{ "model": "setup_61", "task": "nuc", "architecture": "ResUNet3DValid", "checkpoint": "model_checkpoint_20000", "params": 10702001, "channels": [ "nuc" ], "input_voxel_size_nm": [ 256, 256, 256 ], "output_voxel_size_nm": [ 256, 256, 256 ], "output": "raw logits (apply sigmoid / threshold at 0)", "input_norm": [ { "name": "MinMaxNormalizer", "min_value": 0, "max_value": 255 }, { "name": "LambdaNormalizer", "expression": "2*x-1" } ], "io": "input 'input' float32 [1,1,z,y,x]; output 'output' float32 [1,C,z',y',x']", "variants": [ { "model": "setup_61", "variant": "fp32", "input_size": 148, "output_size": 60, "file_mb": 42.91, "file": "setup_61_nuc_256nm_in148_fp32.onnx", "max_abs": 0.05411338806152344, "mean_abs": 0.005284508690237999, "flip_frac": 0.0, "ort_cpu_s": 1.53 }, { "model": "setup_61", "variant": "fp16", "input_size": 148, "output_size": 60, "file_mb": 21.55, "file": "setup_61_nuc_256nm_in148_fp16.onnx", "max_abs": 0.09769058227539062, "mean_abs": 0.008527121506631374, 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"setup_61_nuc_256nm_in116_fp16.onnx", "max_abs": 0.028288841247558594, "mean_abs": 0.005165788810700178, "flip_frac": 0.0, "ort_cpu_s": 0.93 }, { "model": "setup_61", "variant": "int8w", "input_size": 116, "output_size": 28, "file_mb": 10.88, "file": "setup_61_nuc_256nm_in116_int8w.onnx", "max_abs": 0.474090576171875, "mean_abs": 0.06662466377019882, "flip_frac": 0.0, "ort_cpu_s": 0.58 }, { "model": "setup_61", "variant": "int8w_fp16", "input_size": 116, "output_size": 28, "file_mb": 10.91, "file": "setup_61_nuc_256nm_in116_int8w_fp16.onnx", "max_abs": 0.49416446685791016, "mean_abs": 0.06854952126741409, "flip_frac": 0.0, "ort_cpu_s": 0.98 } ], "source_repo": "cellmap/nuc-resunet-setup-61", "torch_version": "2.9.1", "exporter": "torch.onnx.export (TorchScript, dynamo=False)", "opset": 18, "finetune": { "head": { "final_layer": "final_conv.convs.0", "onnx_node": "/final_conv/convs.0/Conv", "features": "/final_conv/Slice_output_0", "weight": "final_conv.convs.0.weight", "bias": "final_conv.convs.0.bias", "in_channels": 40, "out_channels": 1, "activation": "none (logits)", "files": [ "setup_61_nuc_256nm_in116_fp16.onnx", "setup_61_nuc_256nm_in116_fp32.onnx", "setup_61_nuc_256nm_in132_fp16.onnx", "setup_61_nuc_256nm_in132_fp32.onnx", "setup_61_nuc_256nm_in148_fp16.onnx", "setup_61_nuc_256nm_in148_fp32.onnx" ], "initializers": "finetune/initializers.json", "loss": "masked BCE on logits", "note": "train the final 1x1 conv on frozen features", "torch_weight": { "torch": "final_conv.weight", "transform": "[:, :, 0]", "to_onnx": [ { "index": [ 2, 0 ] } ] }, "torch_bias": { "torch": "final_conv.bias", "transform": "identity", "to_onnx": [] }, "weight_map": "finetune/weight_map.json" }, "full": { "library": "tfjs", "files": [ "finetune/train_graph.json", "finetune/cellmap_tfjs_train.js" ], "weight_map": "finetune/weight_map.json", "torch_params": "finetune/torch_params.json", "initializers": "finetune/initializers.json", "write_back": [ "setup_61_nuc_256nm_in116_fp16.onnx", "setup_61_nuc_256nm_in116_fp32.onnx", "setup_61_nuc_256nm_in132_fp16.onnx", "setup_61_nuc_256nm_in132_fp32.onnx", "setup_61_nuc_256nm_in148_fp16.onnx", "setup_61_nuc_256nm_in148_fp32.onnx" ], "frozen": [], "note": "NDHWC graph, params named as the PyTorch state_dict (Conv3d [O,I,kd,kh,kw], ConvTranspose3d [I,O,2,2,2]); read them from the .onnx with torch_params.json from_onnx, write back with weight_map.json to_onnx; BatchNorm running stats frozen. Plain TF.js autodiff keeps every intermediate until the backward pass: use the smallest block (3D memory grows with the block volume)" } } }