{
  "checkpoint": {
    "path": "joint_root_area_poc_epoch200.pt",
    "sha256": "1bed00bc8f934243f079592c84f06090d720798f63044ffcb462a11ddf0105f8",
    "size_bytes": 382445498
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  "config": {
    "area_lambda": 0.15,
    "backbone": "facebook/dinov3-vitb16-pretrain-lvd1689m",
    "batch_size": 2,
    "classification_positive_weight": 10.0,
    "classification_probability_threshold": 0.5,
    "classification_sigma": 3.0,
    "decoder_activation_checkpointing": true,
    "density_alpha": 0.5,
    "derived_polygon_labelme": "tile_1001_0_0_pseudo_area_labelme.json",
    "epochs": 200,
    "experiment": "joint-root-area-poc",
    "freeze_backbone": true,
    "gradient_accumulation_steps": 2,
    "image_height": 3943,
    "image_id": "day2/tiles_png_1001/tile_1001_0_0.png",
    "image_width": 3797,
    "inference_patch_size": 512,
    "inference_stride": 512,
    "learning_rate": 0.0001,
    "logical_device": "cuda:0",
    "match_tolerance_px": 15.0,
    "patch_count": 64,
    "patch_size": 512,
    "peak_min_distance": 8,
    "physical_gpu": 5,
    "physical_microbatch_size": 1,
    "polygon_array_count": 457,
    "polygon_prediction_count": 401,
    "polygon_source": "test_tile_1001_0_0.json",
    "precision": "CUDA autocast float16; GradScaler; losses accumulated as float32 where PyTorch requires",
    "preprocessing": "RGB; pixels with all channels >=250 filled black; ImageNet normalization",
    "raw_image": "tile_1001_0_0_raw.png",
    "root_count": 209,
    "root_xml": "point_id18_annotations.xml",
    "run_id": "0003-point18-joint-area-poc",
    "schema": "wheat-exp.run-config",
    "schema_version": "1.0.0",
    "seed": 1001,
    "split": "row-major edge-push coordinates; first 32 train, last 32 validation",
    "stride": 512,
    "train_patch_count": 32,
    "unseen_image": "/mnt/hdb/chenyizi/sam3-fewshot/data/tile_1020_test/tile_1020_0_0.png",
    "use_area": true,
    "validation_patch_count": 32,
    "weight_decay": 0.0001
  },
  "epoch_1": {
    "epoch": 1,
    "finite": true,
    "train": {
      "loss": 1.4028209700281877,
      "loss_area": 0.9841924272077449,
      "loss_cls": 0.080084727796077,
      "loss_density": 0.016705973462740595,
      "loss_reg": 1.166754387319088
    },
    "validation": {
      "loss": 2.1073910218233323,
      "loss_area": 1.0187577032193076,
      "loss_cls": 0.0756952810331768,
      "loss_density": 9.822749692952291e-09,
      "loss_reg": 1.878882072865963
    }
  },
  "epoch_200": {
    "epoch": 200,
    "finite": true,
    "train": {
      "loss": 0.07707233374471402,
      "loss_area": 0.17010920215398073,
      "loss_cls": 0.002606162868971751,
      "loss_density": 1.155940458440587e-08,
      "loss_reg": 0.04894978346419521
    },
    "validation": {
      "loss": 2.308025994721122,
      "loss_area": 0.4174362588724989,
      "loss_cls": 0.06357826597036365,
      "loss_density": 1.1210296078304881e-08,
      "loss_reg": 2.1818322725594044
    }
  },
  "experiment": "joint-root-area-poc",
  "history_file": "training_history.json",
  "limitations": [
    "Single selected source tile with a deterministic spatial patch holdout; not independent-image generalization evidence.",
    "SAM3 polygon arrays are pseudo segmentation used only by the auxiliary area branch.",
    "No matched point-only baseline was run, so this run makes no area-supervision improvement claim.",
    "Unseen output is qualitative because no manual roots are available for that image.",
    "The required optimizer batch size 2 is executed as two size-1 physical microbatches with gradient accumulation because an unrelated co-tenant occupied about 14.7 GiB on physical GPU 5; decoder activation checkpointing and AMP preserve the requested GPU and effective batch without changing source model code."
  ],
  "metrics": {
    "area_auxiliary": {
      "interpretation": "auxiliary diagnostic against SAM3 pseudo segmentation, not manual GT",
      "intersection_pixels": 144289,
      "iou_against_pseudo_area": 0.5008104515273627,
      "threshold": 0.5,
      "union_pixels": 288111
    },
    "evaluation_class": "architecture/behavior POC; not an area-supervision effect comparison",
    "held_out_manual_root_count": 142,
    "held_out_prediction_count": 99,
    "held_out_spatial_region": {
      "count_error": 43.0,
      "f1": 0.5477178423236515,
      "fn": 76,
      "fp": 33,
      "precision": 0.6666666666666666,
      "recall": 0.4647887323943662,
      "tp": 66
    },
    "same_tile_full_image": {
      "count_error": 43.0,
      "f1": 0.7093333333333333,
      "fn": 76,
      "fp": 33,
      "precision": 0.8012048192771084,
      "recall": 0.6363636363636364,
      "tp": 133
    }
  },
  "run_id": "0003-point18-joint-area-poc",
  "schema": "wheat-exp.recipe",
  "schema_version": "1.0.0",
  "smoke_command": "cd /mnt/hdb/chenyizi/wheat-detect && source /mnt/hdb/chenyizi/sam3-fewshot/venv/bin/activate && PYTHONPATH=/mnt/hdb/chenyizi/wheat-detect HF_HOME=/mnt/hdb/chenyizi/sam3-fewshot/hf-cache TRANSFORMERS_OFFLINE=1 CUDA_VISIBLE_DEVICES=5 python experiments/joint-root-area-poc/runs/0003-point18-joint-area-poc/run_joint_poc.py --mode smoke",
  "source_revision": {
    "git_commit": null,
    "git_commit_status": "unavailable: remote checkout has no commit object",
    "repository": "/mnt/hdb/chenyizi/wheat-detect",
    "source_file_sha256": {
      "wheat_detect/data/dataset.py": "0401cc3a0a39ab02a3322aa5c68d689c2df4ab79718c7191493b7f70bae8efe2",
      "wheat_detect/data/preprocessing.py": "fe57174183e5427517c5f3e68077cc226e6ebbf390b8522aedb2140b10c1ab6e",
      "wheat_detect/evaluation/metrics.py": "0762d8e0aff3d0f170d366b93814ab032a4392ce55b4cf9e8f555c3f3ab45467",
      "wheat_detect/evaluation/rendering.py": "c4d273e24332666bd1ac909141af4e18eb53ec4023d0fd41f7cb4694e0b6981f",
      "wheat_detect/losses/counting_loss.py": "3e112a1a861bd47150bfd8b9236c649aeb434f79a303354774b196a76403f4f3",
      "wheat_detect/models/counting_module.py": "8a33d7510d1f48ec4682dc7ae93ef47c072097a312174fa5bac6ccfdf0c98db6",
      "wheat_detect/models/decoder.py": "19e61542dc4cba0616435a9b5054db631840ef819bc10fee2aeb982b8f5e378b"
    },
    "source_model_code_modified_by_run": false
  },
  "spec_version": "1.0.0",
  "status": "complete",
  "supervision": {
    "area_target": "raster fills from 457 SAM3 pseudo polygon arrays",
    "area_valid": true,
    "classification_target": "manual root Gaussian heatmap",
    "density_target": "manual root density diagnostic/supporting loss",
    "polygon_geometry_used_for_point_targets_or_metrics": false,
    "primary_root_target": "209 manual CVAT wheat points",
    "regression_target": "manual root offset map"
  },
  "training_command": "cd /mnt/hdb/chenyizi/wheat-detect && source /mnt/hdb/chenyizi/sam3-fewshot/venv/bin/activate && PYTHONPATH=/mnt/hdb/chenyizi/wheat-detect HF_HOME=/mnt/hdb/chenyizi/sam3-fewshot/hf-cache TRANSFORMERS_OFFLINE=1 CUDA_VISIBLE_DEVICES=5 python experiments/joint-root-area-poc/runs/0003-point18-joint-area-poc/run_joint_poc.py --mode train",
  "unseen_status": "complete_qualitative_only"
}