Update OrientedDet pretrained checkpoints

rotated_retinanet_r50_fpn_dota_le90_3x-42968545.json

@@ -0,0 +1,351 @@
+{
+  "model_type": "rotated_retinanet",
+  "experiment_timestamp": "20260913-031811",
+  "source_recipe": "configs/rotated_retinanet/dota_le90_3x.json",
+  "source_code_root": "/home/jeffaudi/projects/oriented-det",
+  "git_commit": "dfb6614eee87d20ba7aa39313db1b8683a9ab57f",
+  "git_describe": "v0.2.0-6-gdfb6614",
+  "git_dirty": false,
+  "git_branch": "main",
+  "git_commit_date": "2026-09-13T02:53:08+00:00",
+  "package_version": "0.1.0",
+  "enable_albumentation": false,
+  "enable_profiling": false,
+  "dataset": {
+    "data_root": "/path/to/data/DOTA-v1.0-tiled",
+    "format": "dota",
+    "train_tiles_dir": "/path/to/data/DOTA-v1.0-tiled/train",
+    "val_tiles_dir": "/path/to/data/DOTA-v1.0-tiled/val",
+    "train_tiles_dirs": [
+      "/path/to/data/DOTA-v1.0-tiled/train",
+      "/path/to/data/DOTA-v1.0-tiled/val"
+    ],
+    "val_tiles_dirs": [
+      "/path/to/data/DOTA-v1.0-tiled/val"
+    ],
+    "same_folder": false,
+    "overlap": 200,
+    "annotations_file": null,
+    "split_file": null,
+    "val_split_id": 0,
+    "train_includes_val": false,
+    "ignore_labels": [
+      "container-crane",
+      "airport",
+      "helipad"
+    ],
+    "lookalike_labels": null,
+    "map_labels": null,
+    "difficult_tags": null,
+    "difficult_strategy": "keep",
+    "filter_empty_gt": true,
+    "max_train_samples": null,
+    "max_val_samples": null,
+    "max_samples_shuffle_seed": null,
+    "allowed_classes": null,
+    "train_split": null,
+    "val_split": null,
+    "tile_metrics_csv": null,
+    "hard_tile_metric_column": "f1",
+    "hard_tile_threshold": 0.8,
+    "hard_tile_oversample_factor": 2.0,
+    "class_tile_oversample_classes": null,
+    "class_tile_oversample_factor": 1.0,
+    "class_tile_oversample_min_count": 1,
+    "drop_easy_empty_tiles": false
+  },
+  "augmentation": {
+    "brightness_limit": 0.2,
+    "contrast_limit": 0.2,
+    "gamma_limit": [
+      80,
+      120
+    ],
+    "gauss_noise_var_limit": [
+      10.0,
+      50.0
+    ],
+    "blur_limit": 3,
+    "clahe_clip_limit": 4.0,
+    "p_brightness_contrast": 0.5,
+    "p_gamma": 0.3,
+    "p_noise": 0.2,
+    "p_blur": 0.2,
+    "p_clahe": 0.3
+  },
+  "data_loader": {
+    "batch_size": 2,
+    "num_workers": 4,
+    "shuffle": true,
+    "pin_memory": true
+  },
+  "model": {
+    "backbone": "resnet50",
+    "pretrained_backbone": true,
+    "trainable_layers": 5,
+    "frozen_stages": 1,
+    "fpn_returned_layers": [
+      2,
+      3,
+      4
+    ],
+    "fpn_strides": [
+      8,
+      16,
+      32,
+      64,
+      128
+    ],
+    "fpn_extra_level": true,
+    "anchor_scales": [
+      8
+    ],
+    "anchor_ratios": [
+      0.5,
+      1.0,
+      2.0
+    ],
+    "anchor_angles": null,
+    "anchor_octave_base_scale": 4,
+    "anchor_scales_per_octave": 3,
+    "target_means": [
+      0.0,
+      0.0,
+      0.0,
+      0.0,
+      0.0
+    ],
+    "target_stds": [
+      1.0,
+      1.0,
+      1.0,
+      1.0,
+      1.0
+    ],
+    "roi_loss_type": "focal",
+    "roi_focal_alpha": 0.25,
+    "roi_focal_gamma": 2.0,
+    "roi_norm_factor": null,
+    "roi_edge_swap": true,
+    "roi_proj_xy": false,
+    "roi_box_reg_angle_weight": 1.0,
+    "roi_box_reg_angle_schedule_epochs": null,
+    "roi_box_reg_angle_schedule_values": null,
+    "roi_box_reg_aux_weight": 0.0,
+    "roi_box_reg_aux_schedule_epochs": null,
+    "roi_box_reg_aux_schedule_values": null,
+    "roi_batch_size_per_image": 512,
+    "roi_positive_iou_threshold": 0.5,
+    "roi_negative_iou_threshold": 0.5,
+    "roi_match_low_quality": false,
+    "roi_min_pos_iou": 0.5,
+    "roi_box_reg_aux_loss_type": null,
+    "roi_box_reg_kfiou_fun": null,
+    "roi_box_reg_probiou_mode": null,
+    "roi_box_reg_main_loss_type": "smooth_l1",
+    "roi_box_reg_norm": "sampled_all",
+    "retinanet_stacked_convs": 4,
+    "box_reg_loss_type": "l1",
+    "box_reg_weight": 1.0,
+    "fcos_stacked_convs": 4,
+    "fcos_center_sampling": true,
+    "fcos_center_sample_radius": 1.5,
+    "fcos_norm_on_bbox": true,
+    "fcos_centerness_on_reg": true,
+    "fcos_scale_angle": true,
+    "fcos_regress_ranges": null,
+    "fcos_angle_weight": 1.0,
+    "fcos_nms_pre": 2000,
+    "aux_loss_type": null,
+    "aux_loss_weight": 0.0,
+    "aux_angle_weight": 1.0,
+    "aux_angle_lambda": 1.0,
+    "rpn_min_size": 2,
+    "rpn_pre_nms_top_n": 6000,
+    "rpn_post_nms_top_n": 3000,
+    "rpn_nms_threshold": 0.7,
+    "rpn_batch_size_per_image": 256,
+    "rpn_positive_iou_threshold": 0.5,
+    "rpn_negative_iou_threshold": 0.4,
+    "rpn_min_pos_iou": 0.3,
+    "rpn_match_low_quality": true,
+    "use_hbb_for_matching": true,
+    "roi_use_hbb_for_matching": false,
+    "add_gt_as_proposals": true,
+    "final_nms_iou_threshold": 0.1,
+    "nms_class_agnostic": false,
+    "final_nms_use_cpu": false,
+    "final_nms_iou_schedule_epochs": null,
+    "final_nms_iou_schedule_values": null,
+    "max_detections_per_image": 2000,
+    "inference_pre_nms_score_threshold": 0.05,
+    "roi_inference_top_class_only": false
+  },
+  "training": {
+    "lr_scheduler_type": null,
+    "lr_scheduler_step_epochs": 8,
+    "lr_scheduler_milestones": [
+      24,
+      33
+    ],
+    "lr_scheduler_gamma": 0.1,
+    "lr_scheduler_plateau_metric": "total_loss",
+    "lr_scheduler_plateau_factor": 0.1,
+    "lr_scheduler_plateau_patience": 5,
+    "lr_scheduler_one_cycle_pct_start": 0.3,
+    "lr_scheduler_one_cycle_div_factor": 25.0,
+    "lr_scheduler_one_cycle_final_div_factor": 10000.0,
+    "lr_scheduler_cosine_eta_min": 1e-06,
+    "lr_scheduler_cosine_epochs": null,
+    "lr_scheduler_cosine_tail_epochs": 0,
+    "lr_scheduler_cosine_tail_lr": null,
+    "lr_warmup_steps": 500,
+    "lr_scaling_with_accumulation": "linear",
+    "lr_scale_with_world_size": false,
+    "use_lr_param_groups": false,
+    "lr_mult_backbone": 0.5,
+    "lr_mult_rpn": 0.5,
+    "lr_mult_roi": 2.0,
+    "lr_mult_other": 1.0,
+    "lr_mult_head": null,
+    "num_epochs": 36,
+    "learning_rate": 0.0025,
+    "momentum": 0.9,
+    "weight_decay": 0.0001,
+    "use_amp": false,
+    "gradient_accumulation_steps": 1,
+    "max_grad_norm": 35.0,
+    "loss_weights": null,
+    "freeze_backbone_epochs": 0,
+    "freeze_rpn_epochs": 0,
+    "early_stop_patience": null,
+    "early_stop_metric": "mAP",
+    "early_stop_min_delta": 0.0,
+    "early_stop_higher_is_better": null
+  },
+  "evaluation": {
+    "train_val_score_threshold": 0.3,
+    "per_class_score_threshold": null,
+    "iou_threshold": 0.5,
+    "preds_score_threshold": 0.05,
+    "final_nms_iou_threshold": 0.1,
+    "compute_map_final": false,
+    "compute_map_every_n_epochs": 4,
+    "extended_gt_metrics": true,
+    "use_exact_rotated_iou": false,
+    "use_exact_rotated_iou_for_final_map": null
+  },
+  "production": {
+    "score_threshold": 0.35,
+    "per_class_score_threshold": null,
+    "final_nms_iou_threshold": 0.1,
+    "final_nms_use_cpu": true,
+    "inference_pre_nms_score_threshold": 0.05,
+    "max_detections_per_image": 3000,
+    "nms_class_agnostic": null,
+    "roi_inference_top_class_only": null,
+    "rpn_pre_nms_top_n": null,
+    "rpn_post_nms_top_n": null,
+    "rpn_nms_threshold": null,
+    "overlap_pixels": 200,
+    "ignore_margin_pixels": 0,
+    "use_first_image_canvas": false,
+    "stick_to_model_canvas": true
+  },
+  "checkpoint": {
+    "load_from_checkpoint": null,
+    "load_from_experiment": null,
+    "discover_previous_run": false,
+    "resume_from_checkpoint_epoch": false,
+    "load_optimizer_state": false,
+    "load_scheduler_state": false,
+    "load_include_prefixes": null,
+    "load_exclude_prefixes": null,
+    "start_epoch": 0,
+    "best_metric": "mAP",
+    "higher_is_better": true
+  },
+  "loss": {
+    "loss_type": "focal",
+    "class_weight_method": "sqrt",
+    "class_weight_beta": 0.9999,
+    "class_weight_schedule_type": null,
+    "class_weight_schedule_start_epoch": 0,
+    "class_weight_schedule_end_epoch": 0,
+    "class_weight_schedule_power": 1.0,
+    "background_weight": null,
+    "focal_alpha": 0.25,
+    "focal_gamma": 2.0,
+    "class_weight_overrides": null,
+    "label_smoothing": 0.0,
+    "roi_grouped_ce_enabled": false,
+    "roi_grouped_ce_groups": null,
+    "roi_grouped_ce_schedule_type": null,
+    "roi_grouped_ce_schedule_start_epoch": 0,
+    "roi_grouped_ce_schedule_end_epoch": 8,
+    "roi_grouped_ce_schedule_power": 1.0
+  },
+  "tensorboard": {
+    "log_debug_anchors_proposals": true,
+    "vis_score_threshold": 0.5
+  },
+  "preprocessing": {
+    "resize_mode": "fixed",
+    "target_size": [
+      1024,
+      1024
+    ],
+    "normalize_mean": [
+      0.307941,
+      0.311986,
+      0.297761
+    ],
+    "normalize_std": [
+      0.192315,
+      0.187769,
+      0.181551
+    ],
+    "pad_size_divisor": 32,
+    "enable_flip_horizontal": true,
+    "enable_flip_vertical": true,
+    "enable_flip_diagonal": true,
+    "enable_random_rotate": false,
+    "random_rotate_prob": 0.5,
+    "random_rotate_angle_range": 180.0
+  },
+  "class_map": {
+    "baseball-diamond": 1,
+    "basketball-court": 2,
+    "bridge": 3,
+    "ground-track-field": 4,
+    "harbor": 5,
+    "helicopter": 6,
+    "large-vehicle": 7,
+    "plane": 8,
+    "roundabout": 9,
+    "ship": 10,
+    "small-vehicle": 11,
+    "soccer-ball-field": 12,
+    "storage-tank": 13,
+    "swimming-pool": 14,
+    "tennis-court": 15
+  },
+  "class_names": [
+    "baseball-diamond",
+    "basketball-court",
+    "bridge",
+    "ground-track-field",
+    "harbor",
+    "helicopter",
+    "large-vehicle",
+    "plane",
+    "roundabout",
+    "ship",
+    "small-vehicle",
+    "soccer-ball-field",
+    "storage-tank",
+    "swimming-pool",
+    "tennis-court"
+  ],
+  "num_classes": 15
+}

rotated_retinanet_r50_fpn_dota_le90_3x-42968545.log

@@ -0,0 +1,1709 @@
+Loading configuration from: configs/rotated_retinanet/dota_le90_3x.json
+Training log file: /home/jeffaudi/projects/oriented-det/runs/rotated_retinanet/20260913-031811/train.log
+================================================================================
+ROTATED_RETINANET Training
+================================================================================
+Source git: v0.2.0-6-gdfb6614
+Source branch: main
+Source commit date: 2026-09-13T02:53:08+00:00
+Package: oriented-det 0.1.0
+PyTorch Version: 2.3.0+cu121
+CUDA Available: True
+MPS (Apple Silicon) Available: False
+CUDA Device: NVIDIA GeForce RTX 3090 Ti
+Mixed Precision (AMP): False
+
+Checking dataset directories...
+Train tiles (2 root(s)):
+  /path/to/data/DOTA-v1.0-tiled/train
+    Images exist: True
+    Labels exist: True
+  /path/to/data/DOTA-v1.0-tiled/val
+    Images exist: True
+    Labels exist: True
+
+Val tiles (1 root(s)):
+  /path/to/data/DOTA-v1.0-tiled/val
+    Images exist: True
+    Labels exist: True
+
+
+Loading datasets...
+DOTA filter_empty_gt (MMRotate-style):
+  train: filter_empty_gt dropped 19464 / 33155 tiles (13691 kept)
+  val: filter_empty_gt dropped 4548 / 7669 tiles (3121 kept)
+
+Found 15 classes:
+  0: baseball-diamond
+  1: basketball-court
+  2: bridge
+  3: ground-track-field
+  4: harbor
+  5: helicopter
+  6: large-vehicle
+  7: plane
+  8: roundabout
+  9: ship
+  10: small-vehicle
+  11: soccer-ball-field
+  12: storage-tank
+  13: swimming-pool
+  14: tennis-court
+
+Number of classes (foreground): 15
+Class mapping: {'baseball-diamond': 1, 'basketball-court': 2, 'bridge': 3, 'ground-track-field': 4, 'harbor': 5, 'helicopter': 6, 'large-vehicle': 7, 'plane': 8, 'roundabout': 9, 'ship': 10, 'small-vehicle': 11, 'soccer-ball-field': 12, 'storage-tank': 13, 'swimming-pool': 14, 'tennis-court': 15}
+
+Analyzing class distribution in training set...
+
+================================================================================
+Class Distribution Analysis - Training Set
+================================================================================
+Total objects: 264,176
+Total classes: 15
+
+Class Name           Count        Percentage  
+--------------------------------------------------------------------------------
+ship                     82,131       31.09%
+small-vehicle            63,156       23.91%
+large-vehicle            45,972       17.40%
+plane                    20,023        7.58%
+harbor                   17,028        6.45%
+storage-tank             14,971        5.67%
+tennis-court              6,149        2.33%
+swimming-pool             4,273        1.62%
+bridge                    4,122        1.56%
+helicopter                1,286        0.49%
+basketball-court          1,269        0.48%
+baseball-diamond          1,120        0.42%
+roundabout                1,017        0.38%
+soccer-ball-field           874        0.33%
+ground-track-field          785        0.30%
+--------------------------------------------------------------------------------
+
+Imbalance Ratio (max/min): 104.63x
+Most frequent class: ship (82,131 instances)
+Least frequent class: ground-track-field (785 instances)
+
+Class weighting disabled (loss_type=focal)
+
+Loss configuration: focal
+  Focal Loss Alpha: 0.25
+  Focal Loss Gamma: 2.0
+
+
+Training configuration created:
+  Model type: rotated_retinanet
+  Experiment timestamp: 20260913-031811
+  Number of classes: 15
+  Batch size: 2
+  Learning rate: 0.0025
+  Epochs: 36
+
+Creating collate functions...
+  - Training: no Albumentations augmentation (flips: horizontal, vertical, diagonal; rotate off)
+  - Validation: no augmentation
+
+Creating data loaders...
+Training samples: 13691
+Validation samples: 3121
+Batches per epoch: 6846
+
+Creating model...
+Using device: cuda
+
+Complete ROTATED_RETINANET model created:
+  Backbone: resnet50
+  Number of classes: 15 (foreground)
+  Pretrained backbone: True
+  Trainable backbone layers: 3 (last 3 stages) (frozen_stages=1)
+  RPN anchor reference angles (fixed horizontal priors, not configurable): ['0.0°']
+  Anchor scales: [8]
+  Anchor ratios: [0.5, 1.0, 2.0]
+  FPN strides: [8, 16, 32, 64, 128]
+  FPN levels: 5 (P3, P4, P5, P6, P7)
+  ROI Loss Type: focal
+
+  Total parameters: 36,587,892
+  Trainable parameters: 36,365,492
+
+Configuration saved to: /home/jeffaudi/projects/oriented-det/runs/rotated_retinanet/20260913-031811/config.json
+
+Learning rate warmup enabled: 500 optimizer steps
+  Warmup: 0 → 0.0025 over 500 steps
+  After warmup: MultiStepLR at epochs [24, 33], gamma=0.1
+
+Experiment directory: /home/jeffaudi/projects/oriented-det/runs/rotated_retinanet/20260913-031811
+TensorBoard logging enabled. Logs saved to: /home/jeffaudi/projects/oriented-det/runs/rotated_retinanet/20260913-031811
+View logs with: tensorboard --logdir /home/jeffaudi/projects/oriented-det/runs/rotated_retinanet
+View this experiment with: tensorboard --logdir /home/jeffaudi/projects/oriented-det/runs/rotated_retinanet/20260913-031811
+
+Training configuration:
+  Epochs: 36
+  Learning rate: 0.002500
+  Batch size per GPU: 2
+  Gradient accumulation: 1
+  Best checkpoint metric: mAP (higher_is_better=True)
+  Effective batch size: 2
+  Mixed precision: False
+  Eval (mAP / val matching): score_threshold=0.3, iou_threshold=0.5 (evaluation.train_val_score_threshold; production does not override)
+  Eval IoU backend (mAP / GT cover): GPU sampling (approx)
+  Eval IoU backend (final mAP): GPU sampling (approx)
+
+Starting training from scratch (no checkpoint loaded)
+
+Starting epoch: 0
+
+Profiling disabled
+
+================================================================================
+Starting training...
+================================================================================
+TensorBoard: tensorboard --logdir /home/jeffaudi/projects/oriented-det/runs/rotated_retinanet
+================================================================================
+
+Training started at: 2026-09-13T03:19:04+00:00
+
+Epoch 1/36
+--------------------------------------------------
+/home/jeffaudi/.pyenv/versions/oriented-det/lib/python3.12/site-packages/torch/nn/modules/conv.py:456: UserWarning: Plan failed with a cudnnException: CUDNN_BACKEND_EXECUTION_PLAN_DESCRIPTOR: cudnnFinalize Descriptor Failed cudnn_status: CUDNN_STATUS_NOT_SUPPORTED (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:919.)
+  return F.conv2d(input, weight, bias, self.stride,
+Epoch 1/36 complete: 6846/6846 batches, loss: 1.5853
+  Effective LR: ref=2.500e-03 (0=2.500e-03)
+Validation complete: 1561 batches
+Validation Metrics:
+--------------------------------------------------
+  Eval thresholds (mAP / matching): score≥0.3000, IoU≥0.50
+  Avg Detections per Image (score≥0.3000): 8.97
+  Avg Detections per Image (score≥0.5): 1.87
+  Max detection score: 0.901
+  Mean detection score: 0.137
+  Time per Step: 0.4878
+  Accuracy / GT cover: (skipped — computed on mAP epochs)
+  Ground Truth objects: 57768
+  mAP: (skipped)
+  Ground Truth Classes: 15 classes
+  Predicted Classes: 15 classes
+  Timing: 30m 20s this epoch (avg 30m 20s) | ETA ~17h 41m for 35 epoch(s) left, mAP every 4 epoch(s).
+
+Epoch 2/36
+--------------------------------------------------
+Epoch 2/36 complete: 6846/6846 batches, loss: 1.2816
+  Effective LR: ref=2.500e-03 (0=2.500e-03)
+Validation complete: 1561 batches
+Validation Metrics:
+--------------------------------------------------
+  Eval thresholds (mAP / matching): score≥0.3000, IoU≥0.50
+  Avg Detections per Image (score≥0.3000): 10.30  ↑1.33
+  Avg Detections per Image (score≥0.5): 2.49  ↑0.62
+  Max detection score: 0.981
+  Mean detection score: 0.148
+  Time per Step: 0.5488  ↓0.0610
+  Accuracy / GT cover: (skipped — computed on mAP epochs)
+  Ground Truth objects: 57768
+  mAP: (skipped)
+  Ground Truth Classes: 15 classes
+  Predicted Classes: 15 classes
+  Timing: 33m 31s this epoch (avg 31m 56s) | ETA ~18h 5m for 34 epoch(s) left, mAP every 4 epoch(s).
+
+Epoch 3/36
+--------------------------------------------------
+Epoch 3/36 complete: 6846/6846 batches, loss: 1.1239
+  Effective LR: ref=2.500e-03 (0=2.500e-03)
+Validation complete: 1561 batches
+Validation Metrics:
+--------------------------------------------------
+  Eval thresholds (mAP / matching): score≥0.3000, IoU≥0.50
+  Avg Detections per Image (score≥0.3000): 12.46  ↑2.16
+  Avg Detections per Image (score≥0.5): 4.97  ↑2.48
+  Max detection score: 0.989
+  Mean detection score: 0.190
+  Time per Step: 0.5551  ↓0.0063
+  Accuracy / GT cover: (skipped — computed on mAP epochs)
+  Ground Truth objects: 57768
+  mAP: (skipped)
+  Ground Truth Classes: 15 classes
+  Predicted Classes: 15 classes
+  Timing: 32m 24s this epoch (avg 32m 5s) | ETA ~17h 38m for 33 epoch(s) left, mAP every 4 epoch(s).
+
+Epoch 4/36
+--------------------------------------------------
+Epoch 4/36 complete: 6846/6846 batches, loss: 1.0266
+  Effective LR: ref=2.500e-03 (0=2.500e-03)
+
+Computing mAP (periodic evaluation: every 4 epoch(s))...
+Validation complete: 1561 batches
+
+Computing mAP (this may take a while)...
+  Eval filters: score≥0.3000, IoU≥0.50
+  Images: 3,121
+  Total detections (post score filter): 52,180 (16.7 per image)
+  Total ground truths: 57,768 (18.5 per image)
+  Warning: Large number of detections may slow down mAP computation
+  Consider increasing evaluation.train_val_score_threshold (current: 0.3) for faster mAP matching
+  Computing AP for baseball-diamond: 599 dets, 364 GTs (218,036 IoU calculations)
+  Computing AP for bridge: 630 dets, 666 GTs (419,580 IoU calculations)
+  WARNING: harbor has 24,124,674 IoU calculations (5,613 dets × 4,298 GTs). This will be slow!
+  Computing AP for harbor: 5,613 dets, 4,298 GTs (24,124,674 IoU calculations)
+    Using chunked batch IoU computation (GPU-accelerated) for harbor (24,124,674 calculations)
+    Processing 5,613 detections in chunks of 1,163
+  WARNING: large-vehicle has 53,577,998 IoU calculations (5,701 dets × 9,398 GTs). This will be slow!
+  Computing AP for large-vehicle: 5,701 dets, 9,398 GTs (53,577,998 IoU calculations)
+    Using chunked batch IoU computation (GPU-accelerated) for large-vehicle (53,577,998 calculations)
+    Processing 5,701 detections in chunks of 532
+  WARNING: plane has 21,582,822 IoU calculations (4,562 dets × 4,731 GTs). This will be slow!
+  Computing AP for plane: 4,562 dets, 4,731 GTs (21,582,822 IoU calculations)
+    Using chunked batch IoU computation (GPU-accelerated) for plane (21,582,822 calculations)
+    Processing 4,562 detections in chunks of 1,056
+  WARNING: ship has 357,557,928 IoU calculations (19,292 dets × 18,534 GTs). This will be slow!
+  Computing AP for ship: 19,292 dets, 18,534 GTs (357,557,928 IoU calculations)
+    Using chunked batch IoU computation (GPU-accelerated) for ship (357,557,928 calculations)
+    Processing 19,292 detections in chunks of 269
+  WARNING: small-vehicle has 102,213,000 IoU calculations (9,000 dets × 11,357 GTs). This will be slow!
+  Computing AP for small-vehicle: 9,000 dets, 11,357 GTs (102,213,000 IoU calculations)
+    Using chunked batch IoU computation (GPU-accelerated) for small-vehicle (102,213,000 calculations)
+    Processing 9,000 detections in chunks of 440
+  WARNING: storage-tank has 13,981,149 IoU calculations (2,779 dets × 5,031 GTs). This will be slow!
+  Computing AP for storage-tank: 2,779 dets, 5,031 GTs (13,981,149 IoU calculations)
+    Using chunked batch IoU computation (GPU-accelerated) for storage-tank (13,981,149 calculations)
+    Processing 2,779 detections in chunks of 993
+  Computing AP for swimming-pool: 1,137 dets, 693 GTs (787,941 IoU calculations)
+  Computing AP for tennis-court: 1,504 dets, 1,529 GTs (2,299,616 IoU calculations)
+  mAP computation completed in 8m 41s. mAP: 0.5285
+Validation Metrics:
+--------------------------------------------------
+  Eval thresholds (mAP / matching): score≥0.3000, IoU≥0.50
+  Avg Detections per Image (score≥0.3000): 13.53  ↑1.06
+  Avg Detections per Image (score≥0.5): 6.46  ↑1.49
+  Max detection score: 0.995
+  Mean detection score: 0.206
+  Time per Step: 0.5642  ↓0.0091
+  Accuracy: 0.7153 (71.53%)
+  Correct Predictions: 37322/52180 (matched detections)
+  Ground Truth objects: 57768
+  GT covered pre-eval-threshold: 42504/57768
+  GT covered post-eval-threshold: 37360/57768
+  GT lost by eval-threshold filtering: 5144
+  GT cover rate pre-eval-threshold: 73.58%
+  GT cover rate post-eval-threshold: 64.67%
+  GT IoU vs raw dets (eval IoU threshold=0.50; per-GT max IoU over all dets / over same-class dets):
+    mean best IoU (any class): 0.6172, median: 0.7138
+    mean best IoU (correct class): 0.5907, median: 0.7021
+    per-class mean best IoU (raw detections):
+      | class              | gts | mean_any | mean_same | med_same |
+      |--------------------|-----:|----------:|-----------:|----------:|
+      | baseball-diamond   | 364 |   0.6668 |    0.6572 |   0.7096 |
+      | basketball-court   | 278 |   0.7252 |    0.6569 |   0.7811 |
+      | bridge             | 666 |   0.5187 |    0.5047 |   0.5422 |
+      | ground-track-field | 216 |   0.7156 |    0.6857 |   0.7417 |
+      | harbor             | 4298 |   0.5950 |    0.5841 |   0.6083 |
+      | helicopter         | 157 |   0.6578 |    0.4708 |   0.5589 |
+      | large-vehicle      | 9398 |   0.6229 |    0.5491 |   0.6464 |
+      | plane              | 4731 |   0.7470 |    0.7317 |   0.7898 |
+      | roundabout         | 256 |   0.6628 |    0.6332 |   0.7423 |
+      | ship               | 18534 |   0.6299 |    0.6227 |   0.7295 |
+      | small-vehicle      | 11357 |   0.5798 |    0.5539 |   0.6748 |
+      | soccer-ball-field  | 260 |   0.6756 |    0.6078 |   0.7133 |
+      | storage-tank       | 5031 |   0.4826 |    0.4509 |   0.5989 |
+      | swimming-pool      | 693 |   0.5655 |    0.5499 |   0.6255 |
+      | tennis-court       | 1529 |   0.8086 |    0.7925 |   0.8520 |
+      | global             | 57768 |   0.6172 |    0.5907 |   0.7021 |
+    GTs with IoU≥thresh but no same-class hit (wrong-class / misaligned): 1910
+    GTs with no detection above IoU thresh (missed / poor loc): 13323
+    GTs with 0% best IoU vs any detection (no spatial overlap): 3170 (5.49% of GTs)
+    histogram best IoU (any class) [0,0.25)/[0.25,0.5)/[0.5,0.75)/[0.75,1]: 7512/5811/21305/23140
+    histogram best IoU (correct class) [0,0.25)/[0.25,0.5)/[0.5,0.75)/[0.75,1]: 9437/5796/20742/21793
+  Class-agnostic duplicate rate (post score thresh; IoU≥0.50 vs a higher-score box): micro=2.04% (1066/52180 boxes), macro mean over images with detections=3.02% (2975 images)
+  mAP: 0.5285 (52.85%)
+  Per-Class AP:
+    baseball-diamond: 0.6677 (66.77%)
+    basketball-court: 0.6080 (60.80%)
+    bridge: 0.3460 (34.60%)
+    ground-track-field: 0.4616 (46.16%)
+    harbor: 0.5277 (52.77%)
+    helicopter: 0.2328 (23.28%)
+    large-vehicle: 0.3288 (32.88%)
+    plane: 0.8130 (81.30%)
+    roundabout: 0.5028 (50.28%)
+    ship: 0.7018 (70.18%)
+    small-vehicle: 0.4775 (47.75%)
+    soccer-ball-field: 0.4173 (41.73%)
+    storage-tank: 0.4458 (44.58%)
+    swimming-pool: 0.4965 (49.65%)
+    tennis-court: 0.9012 (90.12%)
+  Ground Truth Classes: 15 classes
+  Predicted Classes: 15 classes
+  Timing: 43m 46s this epoch (avg 35m 0s) | ETA ~18h 40m for 32 epoch(s) left, mAP every 4 epoch(s).
+
+Epoch 5/36
+--------------------------------------------------
+Epoch 5/36 complete: 6846/6846 batches, loss: 0.9569
+  Effective LR: ref=2.500e-03 (0=2.500e-03)
+Validation complete: 1561 batches
+Validation Metrics:
+--------------------------------------------------
+  Eval thresholds (mAP / matching): score≥0.3000, IoU≥0.50
+  Avg Detections per Image (score≥0.3000): 14.43  ↑0.90
+  Avg Detections per Image (score≥0.5): 7.71  ↑1.25
+  Max detection score: 0.993
+  Mean detection score: 0.220
+  Time per Step: 0.5566  ↑0.0075
+  Accuracy / GT cover: (skipped — computed on mAP epochs)
+  Ground Truth objects: 57768
+  mAP: (skipped)
+  Ground Truth Classes: 15 classes
+  Predicted Classes: 15 classes
+  Timing: 31m 19s this epoch (avg 34m 16s) | ETA ~17h 42m for 31 epoch(s) left, mAP every 4 epoch(s).
+
+Epoch 6/36
+--------------------------------------------------
+Epoch 6/36 complete: 6846/6846 batches, loss: 0.9022
+  Effective LR: ref=2.500e-03 (0=2.500e-03)
+Validation complete: 1561 batches
+Validation Metrics:
+--------------------------------------------------
+  Eval thresholds (mAP / matching): score≥0.3000, IoU≥0.50
+  Avg Detections per Image (score≥0.3000): 15.00  ↑0.57
+  Avg Detections per Image (score≥0.5): 8.57  ↑0.86
+  Max detection score: 0.998
+  Mean detection score: 0.202
+  Time per Step: 0.5526  ↑0.0040
+  Accuracy / GT cover: (skipped — computed on mAP epochs)
+  Ground Truth objects: 57768
+  mAP: (skipped)
+  Ground Truth Classes: 15 classes
+  Predicted Classes: 15 classes
+  Timing: 31m 34s this epoch (avg 33m 49s) | ETA ~16h 54m for 30 epoch(s) left, mAP every 4 epoch(s).
+
+Epoch 7/36
+--------------------------------------------------
+Epoch 7/36 complete: 6846/6846 batches, loss: 0.8603
+  Effective LR: ref=2.500e-03 (0=2.500e-03)
+Validation complete: 1561 batches
+Validation Metrics:
+--------------------------------------------------
+  Eval thresholds (mAP / matching): score≥0.3000, IoU≥0.50
+  Avg Detections per Image (score≥0.3000): 15.52  ↑0.51
+  Avg Detections per Image (score≥0.5): 9.16  ↑0.59
+  Max detection score: 0.998
+  Mean detection score: 0.203
+  Time per Step: 0.5507  ↑0.0019
+  Accuracy / GT cover: (skipped — computed on mAP epochs)
+  Ground Truth objects: 57768
+  mAP: (skipped)
+  Ground Truth Classes: 15 classes
+  Predicted Classes: 15 classes
+  Timing: 31m 42s this epoch (avg 33m 31s) | ETA ~16h 11m for 29 epoch(s) left, mAP every 4 epoch(s).
+
+Epoch 8/36
+--------------------------------------------------
+Epoch 8/36 complete: 6846/6846 batches, loss: 0.8262
+  Effective LR: ref=2.500e-03 (0=2.500e-03)
+
+Computing mAP (periodic evaluation: every 4 epoch(s))...
+Validation complete: 1561 batches
+
+Computing mAP (this may take a while)...
+  Eval filters: score≥0.3000, IoU≥0.50
+  Images: 3,121
+  Total detections (post score filter): 58,191 (18.6 per image)
+  Total ground truths: 57,768 (18.5 per image)
+  Warning: Large number of detections may slow down mAP computation
+  Consider increasing evaluation.train_val_score_threshold (current: 0.3) for faster mAP matching
+  Computing AP for baseball-diamond: 450 dets, 364 GTs (163,800 IoU calculations)
+  Computing AP for basketball-court: 448 dets, 278 GTs (124,544 IoU calculations)
+  Computing AP for bridge: 758 dets, 666 GTs (504,828 IoU calculations)
+  Computing AP for ground-track-field: 536 dets, 216 GTs (115,776 IoU calculations)
+  WARNING: harbor has 22,727,824 IoU calculations (5,288 dets × 4,298 GTs). This will be slow!
+  Computing AP for harbor: 5,288 dets, 4,298 GTs (22,727,824 IoU calculations)
+    Using chunked batch IoU computation (GPU-accelerated) for harbor (22,727,824 calculations)
+    Processing 5,288 detections in chunks of 1,163
+  WARNING: large-vehicle has 99,975,924 IoU calculations (10,638 dets × 9,398 GTs). This will be slow!
+  Computing AP for large-vehicle: 10,638 dets, 9,398 GTs (99,975,924 IoU calculations)
+    Using chunked batch IoU computation (GPU-accelerated) for large-vehicle (99,975,924 calculations)
+    Processing 10,638 detections in chunks of 532
+  WARNING: plane has 23,371,140 IoU calculations (4,940 dets × 4,731 GTs). This will be slow!
+  Computing AP for plane: 4,940 dets, 4,731 GTs (23,371,140 IoU calculations)
+    Using chunked batch IoU computation (GPU-accelerated) for plane (23,371,140 calculations)
+    Processing 4,940 detections in chunks of 1,056
+  Computing AP for roundabout: 428 dets, 256 GTs (109,568 IoU calculations)
+  WARNING: ship has 334,724,040 IoU calculations (18,060 dets × 18,534 GTs). This will be slow!
+  Computing AP for ship: 18,060 dets, 18,534 GTs (334,724,040 IoU calculations)
+    Using chunked batch IoU computation (GPU-accelerated) for ship (334,724,040 calculations)
+    Processing 18,060 detections in chunks of 269
+  WARNING: small-vehicle has 113,274,718 IoU calculations (9,974 dets × 11,357 GTs). This will be slow!
+  Computing AP for small-vehicle: 9,974 dets, 11,357 GTs (113,274,718 IoU calculations)
+    Using chunked batch IoU computation (GPU-accelerated) for small-vehicle (113,274,718 calculations)
+    Processing 9,974 detections in chunks of 440
+  WARNING: storage-tank has 18,659,979 IoU calculations (3,709 dets × 5,031 GTs). This will be slow!
+  Computing AP for storage-tank: 3,709 dets, 5,031 GTs (18,659,979 IoU calculations)
+    Using chunked batch IoU computation (GPU-accelerated) for storage-tank (18,659,979 calculations)
+    Processing 3,709 detections in chunks of 993
+  Computing AP for swimming-pool: 705 dets, 693 GTs (488,565 IoU calculations)
+  Computing AP for tennis-court: 1,623 dets, 1,529 GTs (2,481,567 IoU calculations)
+  mAP computation completed in 9m 16s. mAP: 0.6524
+Validation Metrics:
+--------------------------------------------------
+  Eval thresholds (mAP / matching): score≥0.3000, IoU≥0.50
+  Avg Detections per Image (score≥0.3000): 15.91  ↑0.39
+  Avg Detections per Image (score≥0.5): 9.49  ↑0.33
+  Max detection score: 0.997
+  Mean detection score: 0.220
+  Time per Step: 0.5416  ↑0.0091
+  Accuracy: 0.7309 (73.09%)
+  Correct Predictions: 42534/58191 (matched detections)
+  Ground Truth objects: 57768
+  GT covered pre-eval-threshold: 46006/57768
+  GT covered post-eval-threshold: 42565/57768
+  GT lost by eval-threshold filtering: 3441
+  GT cover rate pre-eval-threshold: 79.64%
+  GT cover rate post-eval-threshold: 73.68%
+  GT IoU vs raw dets (eval IoU threshold=0.50; per-GT max IoU over all dets / over same-class dets):
+    mean best IoU (any class): 0.6590, median: 0.7392
+    mean best IoU (correct class): 0.6467, median: 0.7360
+    per-class mean best IoU (raw detections):
+      | class              | gts | mean_any | mean_same | med_same |
+      |--------------------|-----:|----------:|-----------:|----------:|
+      | baseball-diamond   | 364 |   0.7134 |    0.7032 |   0.7395 |
+      | basketball-court   | 278 |   0.8134 |    0.8067 |   0.8442 |
+      | bridge             | 666 |   0.5621 |    0.5551 |   0.6008 |
+      | ground-track-field | 216 |   0.7765 |    0.7587 |   0.7960 |
+      | harbor             | 4298 |   0.6379 |    0.6262 |   0.6614 |
+      | helicopter         | 157 |   0.7063 |    0.6415 |   0.7114 |
+      | large-vehicle      | 9398 |   0.6635 |    0.6437 |   0.7153 |
+      | plane              | 4731 |   0.7914 |    0.7817 |   0.8298 |
+      | roundabout         | 256 |   0.6720 |    0.6660 |   0.7593 |
+      | ship               | 18534 |   0.6396 |    0.6331 |   0.7383 |
+      | small-vehicle      | 11357 |   0.6410 |    0.6241 |   0.7197 |
+      | soccer-ball-field  | 260 |   0.7061 |    0.6426 |   0.7830 |
+      | storage-tank       | 5031 |   0.6001 |    0.5925 |   0.7194 |
+      | swimming-pool      | 693 |   0.5915 |    0.5634 |   0.6375 |
+      | tennis-court       | 1529 |   0.8431 |    0.8352 |   0.8734 |
+      | global             | 57768 |   0.6590 |    0.6467 |   0.7360 |
+    GTs with IoU≥thresh but no same-class hit (wrong-class / misaligned): 857
+    GTs with no detection above IoU thresh (missed / poor loc): 10785
+    GTs with 0% best IoU vs any detection (no spatial overlap): 1039 (1.80% of GTs)
+    histogram best IoU (any class) [0,0.25)/[0.25,0.5)/[0.5,0.75)/[0.75,1]: 5065/5720/19888/27095
+    histogram best IoU (correct class) [0,0.25)/[0.25,0.5)/[0.5,0.75)/[0.75,1]: 5981/5661/19510/26616
+  Class-agnostic duplicate rate (post score thresh; IoU≥0.50 vs a higher-score box): micro=1.73% (1004/58191 boxes), macro mean over images with detections=2.34% (3069 images)
+  mAP: 0.6524  ↑0.1238 (65.24%)
+  Per-Class AP:
+    baseball-diamond: 0.6702  ↑0.0025 (67.02%)
+    basketball-court: 0.7896  ↑0.1816 (78.96%)
+    bridge: 0.4433  ↑0.0973 (44.33%)
+    ground-track-field: 0.7805  ↑0.3189 (78.05%)
+    harbor: 0.6578  ↑0.1301 (65.78%)
+    helicopter: 0.4221  ↑0.1893 (42.21%)
+    large-vehicle: 0.5740  ↑0.2453 (57.40%)
+    plane: 0.9008  ↑0.0878 (90.08%)
+    roundabout: 0.6536  ↑0.1509 (65.36%)
+    ship: 0.6726  ↓0.0291 (67.26%)
+    small-vehicle: 0.5764  ↑0.0989 (57.64%)
+    soccer-ball-field: 0.5705  ↑0.1532 (57.05%)
+    storage-tank: 0.6125  ↑0.1667 (61.25%)
+    swimming-pool: 0.5532  ↑0.0567 (55.32%)
+    tennis-court: 0.9086  ↑0.0074 (90.86%)
+  Ground Truth Classes: 15 classes
+  Predicted Classes: 15 classes
+  Timing: 41m 44s this epoch (avg 34m 33s) | ETA ~16h 7m for 28 epoch(s) left, mAP every 4 epoch(s).
+
+Epoch 9/36
+--------------------------------------------------
+Epoch 9/36 complete: 6846/6846 batches, loss: 0.7970
+  Effective LR: ref=2.500e-03 (0=2.500e-03)
+Validation complete: 1561 batches
+Validation Metrics:
+--------------------------------------------------
+  Eval thresholds (mAP / matching): score≥0.3000, IoU≥0.50
+  Avg Detections per Image (score≥0.3000): 16.15  ↑0.25
+  Avg Detections per Image (score≥0.5): 9.90  ↑0.42
+  Max detection score: 0.999
+  Mean detection score: 0.249
+  Time per Step: 0.5361  ↑0.0055
+  Accuracy / GT cover: (skipped — computed on mAP epochs)
+  Ground Truth objects: 57768
+  mAP: (skipped)
+  Ground Truth Classes: 15 classes
+  Predicted Classes: 15 classes
+  Timing: 30m 31s this epoch (avg 34m 6s) | ETA ~15h 20m for 27 epoch(s) left, mAP every 4 epoch(s).
+
+Epoch 10/36
+--------------------------------------------------
+Epoch 10/36 complete: 6846/6846 batches, loss: 0.7710
+  Effective LR: ref=2.500e-03 (0=2.500e-03)
+Validation complete: 1561 batches
+Validation Metrics:
+--------------------------------------------------
+  Eval thresholds (mAP / matching): score≥0.3000, IoU≥0.50
+  Avg Detections per Image (score≥0.3000): 16.37  ↑0.22
+  Avg Detections per Image (score≥0.5): 10.34  ↑0.44
+  Max detection score: 0.998
+  Mean detection score: 0.255
+  Time per Step: 0.5317  ↑0.0044
+  Accuracy / GT cover: (skipped — computed on mAP epochs)
+  Ground Truth objects: 57768
+  mAP: (skipped)
+  Ground Truth Classes: 15 classes
+  Predicted Classes: 15 classes
+  Timing: 30m 29s this epoch (avg 33m 44s) | ETA ~14h 37m for 26 epoch(s) left, mAP every 4 epoch(s).
+
+Epoch 11/36
+--------------------------------------------------
+Epoch 11/36 complete: 6846/6846 batches, loss: 0.7486
+  Effective LR: ref=2.500e-03 (0=2.500e-03)
+Validation complete: 1561 batches
+Validation Metrics:
+--------------------------------------------------
+  Eval thresholds (mAP / matching): score≥0.3000, IoU≥0.50
+  Avg Detections per Image (score≥0.3000): 16.40  ↑0.04
+  Avg Detections per Image (score≥0.5): 10.28  ↓0.06
+  Max detection score: 0.997
+  Mean detection score: 0.226
+  Time per Step: 0.5260  ↑0.0057
+  Accuracy / GT cover: (skipped — computed on mAP epochs)
+  Ground Truth objects: 57768
+  mAP: (skipped)
+  Ground Truth Classes: 15 classes
+  Predicted Classes: 15 classes
+  Timing: 29m 55s this epoch (avg 33m 23s) | ETA ~13h 54m for 25 epoch(s) left, mAP every 4 epoch(s).
+
+Epoch 12/36
+--------------------------------------------------
+Epoch 12/36 complete: 6846/6846 batches, loss: 0.7293
+  Effective LR: ref=2.500e-03 (0=2.500e-03)
+
+Computing mAP (periodic evaluation: every 4 epoch(s))...
+Validation complete: 1561 batches
+
+Computing mAP (this may take a while)...
+  Eval filters: score≥0.3000, IoU≥0.50
+  Images: 3,121
+  Total detections (post score filter): 45,341 (14.5 per image)
+  Total ground truths: 57,768 (18.5 per image)
+  Computing AP for baseball-diamond: 338 dets, 364 GTs (123,032 IoU calculations)
+  Computing AP for bridge: 648 dets, 666 GTs (431,568 IoU calculations)
+  WARNING: harbor has 20,067,362 IoU calculations (4,669 dets × 4,298 GTs). This will be slow!
+  Computing AP for harbor: 4,669 dets, 4,298 GTs (20,067,362 IoU calculations)
+    Using chunked batch IoU computation (GPU-accelerated) for harbor (20,067,362 calculations)
+    Processing 4,669 detections in chunks of 1,163
+  WARNING: large-vehicle has 77,448,918 IoU calculations (8,241 dets × 9,398 GTs). This will be slow!
+  Computing AP for large-vehicle: 8,241 dets, 9,398 GTs (77,448,918 IoU calculations)
+    Using chunked batch IoU computation (GPU-accelerated) for large-vehicle (77,448,918 calculations)
+    Processing 8,241 detections in chunks of 532
+  WARNING: plane has 21,204,342 IoU calculations (4,482 dets × 4,731 GTs). This will be slow!
+  Computing AP for plane: 4,482 dets, 4,731 GTs (21,204,342 IoU calculations)
+    Using chunked batch IoU computation (GPU-accelerated) for plane (21,204,342 calculations)
+    Processing 4,482 detections in chunks of 1,056
+  WARNING: ship has 226,114,800 IoU calculations (12,200 dets × 18,534 GTs). This will be slow!
+  Computing AP for ship: 12,200 dets, 18,534 GTs (226,114,800 IoU calculations)
+    Using chunked batch IoU computation (GPU-accelerated) for ship (226,114,800 calculations)
+    Processing 12,200 detections in chunks of 269
+  WARNING: small-vehicle has 99,964,314 IoU calculations (8,802 dets × 11,357 GTs). This will be slow!
+  Computing AP for small-vehicle: 8,802 dets, 11,357 GTs (99,964,314 IoU calculations)
+    Using chunked batch IoU computation (GPU-accelerated) for small-vehicle (99,964,314 calculations)
+    Processing 8,802 detections in chunks of 440
+  WARNING: storage-tank has 13,327,119 IoU calculations (2,649 dets × 5,031 GTs). This will be slow!
+  Computing AP for storage-tank: 2,649 dets, 5,031 GTs (13,327,119 IoU calculations)
+    Using chunked batch IoU computation (GPU-accelerated) for storage-tank (13,327,119 calculations)
+    Processing 2,649 detections in chunks of 993
+  Computing AP for swimming-pool: 734 dets, 693 GTs (508,662 IoU calculations)
+  Computing AP for tennis-court: 1,502 dets, 1,529 GTs (2,296,558 IoU calculations)
+  mAP computation completed in 7m 2s. mAP: 0.6306
+Validation Metrics:
+--------------------------------------------------
+  Eval thresholds (mAP / matching): score≥0.3000, IoU≥0.50
+  Avg Detections per Image (score≥0.3000): 16.25  ↓0.16
+  Avg Detections per Image (score≥0.5): 10.18  ↓0.10
+  Max detection score: 0.993
+  Mean detection score: 0.253
+  Time per Step: 0.5106  ↑0.0154
+  Accuracy: 0.8236 (82.36%)
+  Correct Predictions: 37345/45341 (matched detections)
+  Ground Truth objects: 57768
+  GT covered pre-eval-threshold: 46051/57768
+  GT covered post-eval-threshold: 37377/57768
+  GT lost by eval-threshold filtering: 8674
+  GT cover rate pre-eval-threshold: 79.72%
+  GT cover rate post-eval-threshold: 64.70%
+  GT IoU vs raw dets (eval IoU threshold=0.50; per-GT max IoU over all dets / over same-class dets):
+    mean best IoU (any class): 0.6591, median: 0.7456
+    mean best IoU (correct class): 0.6420, median: 0.7392
+    per-class mean best IoU (raw detections):
+      | class              | gts | mean_any | mean_same | med_same |
+      |--------------------|-----:|----------:|-----------:|----------:|
+      | baseball-diamond   | 364 |   0.7290 |    0.7224 |   0.7610 |
+      | basketball-court   | 278 |   0.7371 |    0.7101 |   0.8453 |
+      | bridge             | 666 |   0.5833 |    0.5799 |   0.6255 |
+      | ground-track-field | 216 |   0.7499 |    0.7433 |   0.7851 |
+      | harbor             | 4298 |   0.6189 |    0.6126 |   0.6509 |
+      | helicopter         | 157 |   0.6867 |    0.6243 |   0.6998 |
+      | large-vehicle      | 9398 |   0.6512 |    0.6260 |   0.7129 |
+      | plane              | 4731 |   0.7797 |    0.7783 |   0.8309 |
+      | roundabout         | 256 |   0.7189 |    0.7112 |   0.7845 |
+      | ship               | 18534 |   0.6516 |    0.6204 |   0.7409 |
+      | small-vehicle      | 11357 |   0.6497 |    0.6431 |   0.7300 |
+      | soccer-ball-field  | 260 |   0.7228 |    0.6846 |   0.7900 |
+      | storage-tank       | 5031 |   0.5871 |    0.5844 |   0.7265 |
+      | swimming-pool      | 693 |   0.6062 |    0.6029 |   0.6517 |
+      | tennis-court       | 1529 |   0.8356 |    0.8250 |   0.8640 |
+      | global             | 57768 |   0.6591 |    0.6420 |   0.7392 |
+    GTs with IoU≥thresh but no same-class hit (wrong-class / misaligned): 1097
+    GTs with no detection above IoU thresh (missed / poor loc): 10472
+    GTs with 0% best IoU vs any detection (no spatial overlap): 2374 (4.11% of GTs)
+    histogram best IoU (any class) [0,0.25)/[0.25,0.5)/[0.5,0.75)/[0.75,1]: 5751/4721/19107/28189
+    histogram best IoU (correct class) [0,0.25)/[0.25,0.5)/[0.5,0.75)/[0.75,1]: 6892/4677/19074/27125
+  Class-agnostic duplicate rate (post score thresh; IoU≥0.50 vs a higher-score box): micro=0.81% (365/45341 boxes), macro mean over images with detections=1.65% (2928 images)
+  mAP: 0.6306  ↓0.0218 (63.06%)
+  Per-Class AP:
+    baseball-diamond: 0.7960  ↑0.1258 (79.60%)
+    basketball-court: 0.7064  ↓0.0832 (70.64%)
+    bridge: 0.4677  ↑0.0244 (46.77%)
+    ground-track-field: 0.7507  ↓0.0299 (75.07%)
+    harbor: 0.5032  ↓0.1546 (50.32%)
+    helicopter: 0.4491  ↑0.0270 (44.91%)
+    large-vehicle: 0.5286  ↓0.0454 (52.86%)
+    plane: 0.8152  ↓0.0856 (81.52%)
+    roundabout: 0.7018  ↑0.0482 (70.18%)
+    ship: 0.5222  ↓0.1505 (52.22%)
+    small-vehicle: 0.5972  ↑0.0208 (59.72%)
+    soccer-ball-field: 0.6855  ↑0.1150 (68.55%)
+    storage-tank: 0.4530  ↓0.1595 (45.30%)
+    swimming-pool: 0.5729  ↑0.0197 (57.29%)
+    tennis-court: 0.9090  ↑0.0004 (90.90%)
+  Ground Truth Classes: 15 classes
+  Predicted Classes: 15 classes
+  Timing: 35m 25s this epoch (avg 33m 33s) | ETA ~13h 25m for 24 epoch(s) left, mAP every 4 epoch(s).
+
+Epoch 13/36
+--------------------------------------------------
+Epoch 13/36 complete: 6846/6846 batches, loss: 0.7119
+  Effective LR: ref=2.500e-03 (0=2.500e-03)
+Validation complete: 1561 batches
+Validation Metrics:
+--------------------------------------------------
+  Eval thresholds (mAP / matching): score≥0.3000, IoU≥0.50
+  Avg Detections per Image (score≥0.3000): 16.38  ↑0.13
+  Avg Detections per Image (score≥0.5): 10.49  ↑0.31
+  Max detection score: 0.998
+  Mean detection score: 0.251
+  Time per Step: 0.5013  ↑0.0093
+  Accuracy / GT cover: (skipped — computed on mAP epochs)
+  Ground Truth objects: 57768
+  mAP: (skipped)
+  Ground Truth Classes: 15 classes
+  Predicted Classes: 15 classes
+  Timing: 27m 43s this epoch (avg 33m 6s) | ETA ~12h 41m for 23 epoch(s) left, mAP every 4 epoch(s).
+
+Epoch 14/36
+--------------------------------------------------
+Epoch 14/36 complete: 6846/6846 batches, loss: 0.6956
+  Effective LR: ref=2.500e-03 (0=2.500e-03)
+Validation complete: 1561 batches
+Validation Metrics:
+--------------------------------------------------
+  Eval thresholds (mAP / matching): score≥0.3000, IoU≥0.50
+  Avg Detections per Image (score≥0.3000): 16.38  ↑0.01
+  Avg Detections per Image (score≥0.5): 10.58  ↑0.09
+  Max detection score: 0.999
+  Mean detection score: 0.217
+  Time per Step: 0.4901  ↑0.0112
+  Accuracy / GT cover: (skipped — computed on mAP epochs)
+  Ground Truth objects: 57768
+  mAP: (skipped)
+  Ground Truth Classes: 15 classes
+  Predicted Classes: 15 classes
+  Timing: 26m 33s this epoch (avg 32m 38s) | ETA ~11h 58m for 22 epoch(s) left, mAP every 4 epoch(s).
+
+Epoch 15/36
+--------------------------------------------------
+Epoch 15/36 complete: 6846/6846 batches, loss: 0.6809
+  Effective LR: ref=2.500e-03 (0=2.500e-03)
+Validation complete: 1561 batches
+Validation Metrics:
+--------------------------------------------------
+  Eval thresholds (mAP / matching): score≥0.3000, IoU≥0.50
+  Avg Detections per Image (score≥0.3000): 16.48  ↑0.09
+  Avg Detections per Image (score≥0.5): 10.77  ↑0.18
+  Max detection score: 0.997
+  Mean detection score: 0.285
+  Time per Step: 0.4823  ↑0.0079
+  Accuracy / GT cover: (skipped — computed on mAP epochs)
+  Ground Truth objects: 57768
+  mAP: (skipped)
+  Ground Truth Classes: 15 classes
+  Predicted Classes: 15 classes
+  Timing: 27m 13s this epoch (avg 32m 17s) | ETA ~11h 17m for 21 epoch(s) left, mAP every 4 epoch(s).
+
+Epoch 16/36
+--------------------------------------------------
+Epoch 16/36 complete: 6846/6846 batches, loss: 0.6676
+  Effective LR: ref=2.500e-03 (0=2.500e-03)
+
+Computing mAP (periodic evaluation: every 4 epoch(s))...
+Validation complete: 1561 batches
+
+Computing mAP (this may take a while)...
+  Eval filters: score≥0.3000, IoU≥0.50
+  Images: 3,121
+  Total detections (post score filter): 53,923 (17.3 per image)
+  Total ground truths: 57,768 (18.5 per image)
+  Warning: Large number of detections may slow down mAP computation
+  Consider increasing evaluation.train_val_score_threshold (current: 0.3) for faster mAP matching
+  Computing AP for baseball-diamond: 415 dets, 364 GTs (151,060 IoU calculations)
+  Computing AP for bridge: 845 dets, 666 GTs (562,770 IoU calculations)
+  WARNING: harbor has 20,359,626 IoU calculations (4,737 dets × 4,298 GTs). This will be slow!
+  Computing AP for harbor: 4,737 dets, 4,298 GTs (20,359,626 IoU calculations)
+    Using chunked batch IoU computation (GPU-accelerated) for harbor (20,359,626 calculations)
+    Processing 4,737 detections in chunks of 1,163
+  WARNING: large-vehicle has 86,377,018 IoU calculations (9,191 dets × 9,398 GTs). This will be slow!
+  Computing AP for large-vehicle: 9,191 dets, 9,398 GTs (86,377,018 IoU calculations)
+    Using chunked batch IoU computation (GPU-accelerated) for large-vehicle (86,377,018 calculations)
+    Processing 9,191 detections in chunks of 532
+  WARNING: plane has 21,809,910 IoU calculations (4,610 dets × 4,731 GTs). This will be slow!
+  Computing AP for plane: 4,610 dets, 4,731 GTs (21,809,910 IoU calculations)
+    Using chunked batch IoU computation (GPU-accelerated) for plane (21,809,910 calculations)
+    Processing 4,610 detections in chunks of 1,056
+  WARNING: ship has 317,691,294 IoU calculations (17,141 dets × 18,534 GTs). This will be slow!
+  Computing AP for ship: 17,141 dets, 18,534 GTs (317,691,294 IoU calculations)
+    Using chunked batch IoU computation (GPU-accelerated) for ship (317,691,294 calculations)
+    Processing 17,141 detections in chunks of 269
+  WARNING: small-vehicle has 101,645,150 IoU calculations (8,950 dets × 11,357 GTs). This will be slow!
+  Computing AP for small-vehicle: 8,950 dets, 11,357 GTs (101,645,150 IoU calculations)
+    Using chunked batch IoU computation (GPU-accelerated) for small-vehicle (101,645,150 calculations)
+    Processing 8,950 detections in chunks of 440
+  WARNING: storage-tank has 21,582,990 IoU calculations (4,290 dets × 5,031 GTs). This will be slow!
+  Computing AP for storage-tank: 4,290 dets, 5,031 GTs (21,582,990 IoU calculations)
+    Using chunked batch IoU computation (GPU-accelerated) for storage-tank (21,582,990 calculations)
+    Processing 4,290 detections in chunks of 993
+  Computing AP for swimming-pool: 917 dets, 693 GTs (635,481 IoU calculations)
+  Computing AP for tennis-court: 1,566 dets, 1,529 GTs (2,394,414 IoU calculations)
+  mAP computation completed in 8m 37s. mAP: 0.7263
+Validation Metrics:
+--------------------------------------------------
+  Eval thresholds (mAP / matching): score≥0.3000, IoU≥0.50
+  Avg Detections per Image (score≥0.3000): 16.53  ↑0.05
+  Avg Detections per Image (score≥0.5): 10.82  ↑0.06
+  Max detection score: 0.999
+  Mean detection score: 0.258
+  Time per Step: 0.4761  ↑0.0061
+  Accuracy: 0.8020 (80.20%)
+  Correct Predictions: 43248/53923 (matched detections)
+  Ground Truth objects: 57768
+  GT covered pre-eval-threshold: 47404/57768
+  GT covered post-eval-threshold: 43274/57768
+  GT lost by eval-threshold filtering: 4131
+  GT cover rate pre-eval-threshold: 82.06%
+  GT cover rate post-eval-threshold: 74.91%
+  GT IoU vs raw dets (eval IoU threshold=0.50; per-GT max IoU over all dets / over same-class dets):
+    mean best IoU (any class): 0.6738, median: 0.7543
+    mean best IoU (correct class): 0.6651, median: 0.7526
+    per-class mean best IoU (raw detections):
+      | class              | gts | mean_any | mean_same | med_same |
+      |--------------------|-----:|----------:|-----------:|----------:|
+      | baseball-diamond   | 364 |   0.7613 |    0.7561 |   0.7845 |
+      | basketball-court   | 278 |   0.8185 |    0.8170 |   0.8553 |
+      | bridge             | 666 |   0.6131 |    0.6093 |   0.6478 |
+      | ground-track-field | 216 |   0.7803 |    0.7703 |   0.8036 |
+      | harbor             | 4298 |   0.6634 |    0.6593 |   0.6920 |
+      | helicopter         | 157 |   0.7311 |    0.6770 |   0.7222 |
+      | large-vehicle      | 9398 |   0.6773 |    0.6616 |   0.7362 |
+      | plane              | 4731 |   0.7841 |    0.7822 |   0.8339 |
+      | roundabout         | 256 |   0.7359 |    0.7197 |   0.8229 |
+      | ship               | 18534 |   0.6581 |    0.6507 |   0.7551 |
+      | small-vehicle      | 11357 |   0.6484 |    0.6360 |   0.7310 |
+      | soccer-ball-field  | 260 |   0.7245 |    0.6829 |   0.8131 |
+      | storage-tank       | 5031 |   0.6228 |    0.6212 |   0.7466 |
+      | swimming-pool      | 693 |   0.5943 |    0.5933 |   0.6669 |
+      | tennis-court       | 1529 |   0.8599 |    0.8531 |   0.8866 |
+      | global             | 57768 |   0.6738 |    0.6651 |   0.7526 |
+    GTs with IoU≥thresh but no same-class hit (wrong-class / misaligned): 510
+    GTs with no detection above IoU thresh (missed / poor loc): 9751
+    GTs with 0% best IoU vs any detection (no spatial overlap): 1291 (2.23% of GTs)
+    histogram best IoU (any class) [0,0.25)/[0.25,0.5)/[0.5,0.75)/[0.75,1]: 5049/4702/18344/29673
+    histogram best IoU (correct class) [0,0.25)/[0.25,0.5)/[0.5,0.75)/[0.75,1]: 5709/4552/18176/29331
+  Class-agnostic duplicate rate (post score thresh; IoU≥0.50 vs a higher-score box): micro=0.86% (462/53923 boxes), macro mean over images with detections=1.49% (3040 images)
+  mAP: 0.7263  ↑0.0957 (72.63%)
+  Per-Class AP:
+    baseball-diamond: 0.8854  ↑0.0894 (88.54%)
+    basketball-court: 0.8999  ↑0.1935 (89.99%)
+    bridge: 0.5432  ↑0.0755 (54.32%)
+    ground-track-field: 0.8694  ↑0.1187 (86.94%)
+    harbor: 0.6588  ↑0.1556 (65.88%)
+    helicopter: 0.7064  ↑0.2573 (70.64%)
+    large-vehicle: 0.5805  ↑0.0519 (58.05%)
+    plane: 0.9030  ↑0.0878 (90.30%)
+    roundabout: 0.6983  ↓0.0035 (69.83%)
+    ship: 0.6790  ↑0.1568 (67.90%)
+    small-vehicle: 0.5884  ↓0.0089 (58.84%)
+    soccer-ball-field: 0.7010  ↑0.0155 (70.10%)
+    storage-tank: 0.6238  ↑0.1708 (62.38%)
+    swimming-pool: 0.6489  ↑0.0760 (64.89%)
+    tennis-court: 0.9088  ↓0.0002 (90.88%)
+  Ground Truth Classes: 15 classes
+  Predicted Classes: 15 classes
+  Timing: 38m 21s this epoch (avg 32m 39s) | ETA ~10h 53m for 20 epoch(s) left, mAP every 4 epoch(s).
+
+Epoch 17/36
+--------------------------------------------------
+Epoch 17/36 complete: 6846/6846 batches, loss: 0.6549
+  Effective LR: ref=2.500e-03 (0=2.500e-03)
+Validation complete: 1561 batches
+Validation Metrics:
+--------------------------------------------------
+  Eval thresholds (mAP / matching): score≥0.3000, IoU≥0.50
+  Avg Detections per Image (score≥0.3000): 16.59  ↑0.06
+  Avg Detections per Image (score≥0.5): 11.00  ↑0.17
+  Max detection score: 1.000
+  Mean detection score: 0.277
+  Time per Step: 0.4680  ↑0.0081
+  Accuracy / GT cover: (skipped — computed on mAP epochs)
+  Ground Truth objects: 57768
+  mAP: (skipped)
+  Ground Truth Classes: 15 classes
+  Predicted Classes: 15 classes
+  Timing: 26m 19s this epoch (avg 32m 17s) | ETA ~10h 13m for 19 epoch(s) left, mAP every 4 epoch(s).
+
+Epoch 18/36
+--------------------------------------------------
+Epoch 18/36 complete: 6846/6846 batches, loss: 0.6435
+  Effective LR: ref=2.500e-03 (0=2.500e-03)
+Validation complete: 1561 batches
+Validation Metrics:
+--------------------------------------------------
+  Eval thresholds (mAP / matching): score≥0.3000, IoU≥0.50
+  Avg Detections per Image (score≥0.3000): 16.68  ↑0.09
+  Avg Detections per Image (score≥0.5): 11.20  ↑0.21
+  Max detection score: 1.000
+  Mean detection score: 0.332
+  Time per Step: 0.4632  ↑0.0048
+  Accuracy / GT cover: (skipped — computed on mAP epochs)
+  Ground Truth objects: 57768
+  mAP: (skipped)
+  Ground Truth Classes: 15 classes
+  Predicted Classes: 15 classes
+  Timing: 27m 33s this epoch (avg 32m 1s) | ETA ~9h 36m for 18 epoch(s) left, mAP every 4 epoch(s).
+
+Epoch 19/36
+--------------------------------------------------
+Epoch 19/36 complete: 6846/6846 batches, loss: 0.6329
+  Effective LR: ref=2.500e-03 (0=2.500e-03)
+Validation complete: 1561 batches
+Validation Metrics:
+--------------------------------------------------
+  Eval thresholds (mAP / matching): score≥0.3000, IoU≥0.50
+  Avg Detections per Image (score≥0.3000): 16.61  ↓0.07
+  Avg Detections per Image (score≥0.5): 11.21  ↑0.01
+  Max detection score: 0.997
+  Mean detection score: 0.267
+  Time per Step: 0.4577  ↑0.0056
+  Accuracy / GT cover: (skipped — computed on mAP epochs)
+  Ground Truth objects: 57768
+  mAP: (skipped)
+  Ground Truth Classes: 15 classes
+  Predicted Classes: 15 classes
+  Timing: 26m 49s this epoch (avg 31m 45s) | ETA ~8h 59m for 17 epoch(s) left, mAP every 4 epoch(s).
+
+Epoch 20/36
+--------------------------------------------------
+Epoch 20/36 complete: 6846/6846 batches, loss: 0.6235
+  Effective LR: ref=2.500e-03 (0=2.500e-03)
+
+Computing mAP (periodic evaluation: every 4 epoch(s))...
+Validation complete: 1561 batches
+
+Computing mAP (this may take a while)...
+  Eval filters: score≥0.3000, IoU≥0.50
+  Images: 3,121
+  Total detections (post score filter): 51,817 (16.6 per image)
+  Total ground truths: 57,768 (18.5 per image)
+  Warning: Large number of detections may slow down mAP computation
+  Consider increasing evaluation.train_val_score_threshold (current: 0.3) for faster mAP matching
+  Computing AP for baseball-diamond: 447 dets, 364 GTs (162,708 IoU calculations)
+  Computing AP for bridge: 870 dets, 666 GTs (579,420 IoU calculations)
+  WARNING: harbor has 20,351,030 IoU calculations (4,735 dets × 4,298 GTs). This will be slow!
+  Computing AP for harbor: 4,735 dets, 4,298 GTs (20,351,030 IoU calculations)
+    Using chunked batch IoU computation (GPU-accelerated) for harbor (20,351,030 calculations)
+    Processing 4,735 detections in chunks of 1,163
+  WARNING: large-vehicle has 88,247,220 IoU calculations (9,390 dets × 9,398 GTs). This will be slow!
+  Computing AP for large-vehicle: 9,390 dets, 9,398 GTs (88,247,220 IoU calculations)
+    Using chunked batch IoU computation (GPU-accelerated) for large-vehicle (88,247,220 calculations)
+    Processing 9,390 detections in chunks of 532
+  WARNING: plane has 20,319,645 IoU calculations (4,295 dets × 4,731 GTs). This will be slow!
+  Computing AP for plane: 4,295 dets, 4,731 GTs (20,319,645 IoU calculations)
+    Using chunked batch IoU computation (GPU-accelerated) for plane (20,319,645 calculations)
+    Processing 4,295 detections in chunks of 1,056
+  WARNING: ship has 304,680,426 IoU calculations (16,439 dets × 18,534 GTs). This will be slow!
+  Computing AP for ship: 16,439 dets, 18,534 GTs (304,680,426 IoU calculations)
+    Using chunked batch IoU computation (GPU-accelerated) for ship (304,680,426 calculations)
+    Processing 16,439 detections in chunks of 269
+  WARNING: small-vehicle has 105,063,607 IoU calculations (9,251 dets × 11,357 GTs). This will be slow!
+  Computing AP for small-vehicle: 9,251 dets, 11,357 GTs (105,063,607 IoU calculations)
+    Using chunked batch IoU computation (GPU-accelerated) for small-vehicle (105,063,607 calculations)
+    Processing 9,251 detections in chunks of 440
+  WARNING: storage-tank has 15,877,836 IoU calculations (3,156 dets × 5,031 GTs). This will be slow!
+  Computing AP for storage-tank: 3,156 dets, 5,031 GTs (15,877,836 IoU calculations)
+    Using chunked batch IoU computation (GPU-accelerated) for storage-tank (15,877,836 calculations)
+    Processing 3,156 detections in chunks of 993
+  Computing AP for swimming-pool: 547 dets, 693 GTs (379,071 IoU calculations)
+  Computing AP for tennis-court: 1,510 dets, 1,529 GTs (2,308,790 IoU calculations)
+  mAP computation completed in 8m 27s. mAP: 0.6674
+Validation Metrics:
+--------------------------------------------------
+  Eval thresholds (mAP / matching): score≥0.3000, IoU≥0.50
+  Avg Detections per Image (score≥0.3000): 16.61  ↓0.00
+  Avg Detections per Image (score≥0.5): 11.28  ↑0.06
+  Max detection score: 0.998
+  Mean detection score: 0.256
+  Time per Step: 0.4545  ↑0.0032
+  Accuracy: 0.8109 (81.09%)
+  Correct Predictions: 42016/51817 (matched detections)
+  Ground Truth objects: 57768
+  GT covered pre-eval-threshold: 46670/57768
+  GT covered post-eval-threshold: 42045/57768
+  GT lost by eval-threshold filtering: 4625
+  GT cover rate pre-eval-threshold: 80.79%
+  GT cover rate post-eval-threshold: 72.78%
+  GT IoU vs raw dets (eval IoU threshold=0.50; per-GT max IoU over all dets / over same-class dets):
+    mean best IoU (any class): 0.6654, median: 0.7506
+    mean best IoU (correct class): 0.6550, median: 0.7485
+    per-class mean best IoU (raw detections):
+      | class              | gts | mean_any | mean_same | med_same |
+      |--------------------|-----:|----------:|-----------:|----------:|
+      | baseball-diamond   | 364 |   0.7556 |    0.7529 |   0.7783 |
+      | basketball-court   | 278 |   0.8334 |    0.8319 |   0.8624 |
+      | bridge             | 666 |   0.5289 |    0.5068 |   0.5977 |
+      | ground-track-field | 216 |   0.8095 |    0.7872 |   0.8090 |
+      | harbor             | 4298 |   0.6522 |    0.6407 |   0.6907 |
+      | helicopter         | 157 |   0.6986 |    0.6794 |   0.7240 |
+      | large-vehicle      | 9398 |   0.6786 |    0.6601 |   0.7349 |
+      | plane              | 4731 |   0.7683 |    0.7631 |   0.8391 |
+      | roundabout         | 256 |   0.6999 |    0.6201 |   0.8027 |
+      | ship               | 18533 |   0.6548 |    0.6440 |   0.7538 |
+      | small-vehicle      | 11357 |   0.6413 |    0.6357 |   0.7239 |
+      | soccer-ball-field  | 260 |   0.7080 |    0.6843 |   0.7765 |
+      | storage-tank       | 5031 |   0.5956 |    0.5908 |   0.7047 |
+      | swimming-pool      | 693 |   0.5811 |    0.5723 |   0.6699 |
+      | tennis-court       | 1529 |   0.8500 |    0.8433 |   0.8757 |
+      | global             | 57767 |   0.6654 |    0.6550 |   0.7485 |
+    GTs with IoU≥thresh but no same-class hit (wrong-class / misaligned): 605
+    GTs with no detection above IoU thresh (missed / poor loc): 10360
+    GTs with 0% best IoU vs any detection (no spatial overlap): 1714 (2.97% of GTs)
+    histogram best IoU (any class) [0,0.25)/[0.25,0.5)/[0.5,0.75)/[0.75,1]: 5329/5031/18423/28984
+    histogram best IoU (correct class) [0,0.25)/[0.25,0.5)/[0.5,0.75)/[0.75,1]: 6147/4818/18178/28624
+  Class-agnostic duplicate rate (post score thresh; IoU≥0.50 vs a higher-score box): micro=1.11% (576/51817 boxes), macro mean over images with detections=1.97% (2916 images)
+  mAP: 0.6674  ↓0.0589 (66.74%)
+  Per-Class AP:
+    baseball-diamond: 0.8849  ↓0.0005 (88.49%)
+    basketball-court: 0.8986  ↓0.0013 (89.86%)
+    bridge: 0.2941  ↓0.2491 (29.41%)
+    ground-track-field: 0.7915  ↓0.0779 (79.15%)
+    harbor: 0.6158  ↓0.0430 (61.58%)
+    helicopter: 0.5835  ↓0.1230 (58.35%)
+    large-vehicle: 0.6077  ↑0.0272 (60.77%)
+    plane: 0.8141  ↓0.0889 (81.41%)
+    roundabout: 0.5325  ↓0.1659 (53.25%)
+    ship: 0.6923  ↑0.0133 (69.23%)
+    small-vehicle: 0.5834  ↓0.0049 (58.34%)
+    soccer-ball-field: 0.6798  ↓0.0213 (67.98%)
+    storage-tank: 0.5400  ↓0.0839 (54.00%)
+    swimming-pool: 0.5846  ↓0.0643 (58.46%)
+    tennis-court: 0.9090  ↑0.0001 (90.90%)
+  Ground Truth Classes: 15 classes
+  Predicted Classes: 15 classes
+  Timing: 38m 20s this epoch (avg 32m 5s) | ETA ~8h 33m for 16 epoch(s) left, mAP every 4 epoch(s).
+
+Epoch 21/36
+--------------------------------------------------
+Epoch 21/36 complete: 6846/6846 batches, loss: 0.6151
+  Effective LR: ref=2.500e-03 (0=2.500e-03)
+Validation complete: 1561 batches
+Validation Metrics:
+--------------------------------------------------
+  Eval thresholds (mAP / matching): score≥0.3000, IoU≥0.50
+  Avg Detections per Image (score≥0.3000): 16.66  ↑0.05
+  Avg Detections per Image (score≥0.5): 11.37  ↑0.09
+  Max detection score: 0.999
+  Mean detection score: 0.281
+  Time per Step: 0.4495  ↑0.0050
+  Accuracy / GT cover: (skipped — computed on mAP epochs)
+  Ground Truth objects: 57768
+  mAP: (skipped)
+  Ground Truth Classes: 15 classes
+  Predicted Classes: 15 classes
+  Timing: 26m 35s this epoch (avg 31m 49s) | ETA ~7h 57m for 15 epoch(s) left, mAP every 4 epoch(s).
+
+Epoch 22/36
+--------------------------------------------------
+Epoch 22/36 complete: 6846/6846 batches, loss: 0.6067
+  Effective LR: ref=2.500e-03 (0=2.500e-03)
+Validation complete: 1561 batches
+Validation Metrics:
+--------------------------------------------------
+  Eval thresholds (mAP / matching): score≥0.3000, IoU≥0.50
+  Avg Detections per Image (score≥0.3000): 16.72  ↑0.07
+  Avg Detections per Image (score≥0.5): 11.48  ↑0.11
+  Max detection score: 0.999
+  Mean detection score: 0.293
+  Time per Step: 0.4458  ↑0.0037
+  Accuracy / GT cover: (skipped — computed on mAP epochs)
+  Ground Truth objects: 57768
+  mAP: (skipped)
+  Ground Truth Classes: 15 classes
+  Predicted Classes: 15 classes
+  Timing: 27m 2s this epoch (avg 31m 36s) | ETA ~7h 22m for 14 epoch(s) left, mAP every 4 epoch(s).
+
+Epoch 23/36
+--------------------------------------------------
+Epoch 23/36 complete: 6846/6846 batches, loss: 0.5986
+  Effective LR: ref=2.500e-03 (0=2.500e-03)
+Validation complete: 1561 batches
+Validation Metrics:
+--------------------------------------------------
+  Eval thresholds (mAP / matching): score≥0.3000, IoU≥0.50
+  Avg Detections per Image (score≥0.3000): 16.77  ↑0.05
+  Avg Detections per Image (score≥0.5): 11.62  ↑0.15
+  Max detection score: 1.000
+  Mean detection score: 0.304
+  Time per Step: 0.4403  ↑0.0055
+  Accuracy / GT cover: (skipped — computed on mAP epochs)
+  Ground Truth objects: 57768
+  mAP: (skipped)
+  Ground Truth Classes: 15 classes
+  Predicted Classes: 15 classes
+  Timing: 25m 48s this epoch (avg 31m 21s) | ETA ~6h 47m for 13 epoch(s) left, mAP every 4 epoch(s).
+
+Epoch 24/36
+--------------------------------------------------
+Epoch 24/36 complete: 6846/6846 batches, loss: 0.5907
+  Effective LR: ref=2.500e-03 (0=2.500e-03)
+
+Computing mAP (periodic evaluation: every 4 epoch(s))...
+Validation complete: 1561 batches
+
+Computing mAP (this may take a while)...
+  Eval filters: score≥0.3000, IoU≥0.50
+  Images: 3,121
+  Total detections (post score filter): 58,962 (18.9 per image)
+  Total ground truths: 57,768 (18.5 per image)
+  Warning: Large number of detections may slow down mAP computation
+  Consider increasing evaluation.train_val_score_threshold (current: 0.3) for faster mAP matching
+  Computing AP for baseball-diamond: 293 dets, 364 GTs (106,652 IoU calculations)
+  Computing AP for bridge: 560 dets, 666 GTs (372,960 IoU calculations)
+  WARNING: harbor has 20,939,856 IoU calculations (4,872 dets × 4,298 GTs). This will be slow!
+  Computing AP for harbor: 4,872 dets, 4,298 GTs (20,939,856 IoU calculations)
+    Using chunked batch IoU computation (GPU-accelerated) for harbor (20,939,856 calculations)
+    Processing 4,872 detections in chunks of 1,163
+  WARNING: large-vehicle has 102,926,896 IoU calculations (10,952 dets × 9,398 GTs). This will be slow!
+  Computing AP for large-vehicle: 10,952 dets, 9,398 GTs (102,926,896 IoU calculations)
+    Using chunked batch IoU computation (GPU-accelerated) for large-vehicle (102,926,896 calculations)
+    Processing 10,952 detections in chunks of 532
+  WARNING: plane has 22,112,694 IoU calculations (4,674 dets × 4,731 GTs). This will be slow!
+  Computing AP for plane: 4,674 dets, 4,731 GTs (22,112,694 IoU calculations)
+    Using chunked batch IoU computation (GPU-accelerated) for plane (22,112,694 calculations)
+    Processing 4,674 detections in chunks of 1,056
+  WARNING: ship has 340,098,900 IoU calculations (18,350 dets × 18,534 GTs). This will be slow!
+  Computing AP for ship: 18,350 dets, 18,534 GTs (340,098,900 IoU calculations)
+    Using chunked batch IoU computation (GPU-accelerated) for ship (340,098,900 calculations)
+    Processing 18,350 detections in chunks of 269
+  WARNING: small-vehicle has 129,004,163 IoU calculations (11,359 dets × 11,357 GTs). This will be slow!
+  Computing AP for small-vehicle: 11,359 dets, 11,357 GTs (129,004,163 IoU calculations)
+    Using chunked batch IoU computation (GPU-accelerated) for small-vehicle (129,004,163 calculations)
+    Processing 11,359 detections in chunks of 440
+  WARNING: storage-tank has 22,025,718 IoU calculations (4,378 dets × 5,031 GTs). This will be slow!
+  Computing AP for storage-tank: 4,378 dets, 5,031 GTs (22,025,718 IoU calculations)
+    Using chunked batch IoU computation (GPU-accelerated) for storage-tank (22,025,718 calculations)
+    Processing 4,378 detections in chunks of 993
+  Computing AP for swimming-pool: 900 dets, 693 GTs (623,700 IoU calculations)
+  Computing AP for tennis-court: 1,504 dets, 1,529 GTs (2,299,616 IoU calculations)
+  mAP computation completed in 9m 37s. mAP: 0.7218
+Validation Metrics:
+--------------------------------------------------
+  Eval thresholds (mAP / matching): score≥0.3000, IoU≥0.50
+  Avg Detections per Image (score≥0.3000): 16.86  ↑0.09
+  Avg Detections per Image (score≥0.5): 11.76  ↑0.14
+  Max detection score: 1.000
+  Mean detection score: 0.284
+  Time per Step: 0.4357  ↑0.0046
+  Accuracy: 0.7697 (76.97%)
+  Correct Predictions: 45382/58962 (matched detections)
+  Ground Truth objects: 57768
+  GT covered pre-eval-threshold: 48500/57768
+  GT covered post-eval-threshold: 45401/57768
+  GT lost by eval-threshold filtering: 3099
+  GT cover rate pre-eval-threshold: 83.96%
+  GT cover rate post-eval-threshold: 78.59%
+  GT IoU vs raw dets (eval IoU threshold=0.50; per-GT max IoU over all dets / over same-class dets):
+    mean best IoU (any class): 0.6946, median: 0.7696
+    mean best IoU (correct class): 0.6859, median: 0.7683
+    per-class mean best IoU (raw detections):
+      | class              | gts | mean_any | mean_same | med_same |
+      |--------------------|-----:|----------:|-----------:|----------:|
+      | baseball-diamond   | 364 |   0.7450 |    0.7396 |   0.7755 |
+      | basketball-court   | 278 |   0.8595 |    0.8409 |   0.8732 |
+      | bridge             | 666 |   0.5730 |    0.5707 |   0.6179 |
+      | ground-track-field | 216 |   0.7983 |    0.7940 |   0.8232 |
+      | harbor             | 4298 |   0.6846 |    0.6792 |   0.7174 |
+      | helicopter         | 157 |   0.7399 |    0.6963 |   0.7418 |
+      | large-vehicle      | 9398 |   0.7006 |    0.6809 |   0.7532 |
+      | plane              | 4731 |   0.8014 |    0.7987 |   0.8470 |
+      | roundabout         | 256 |   0.7558 |    0.7436 |   0.8217 |
+      | ship               | 18534 |   0.6720 |    0.6638 |   0.7709 |
+      | small-vehicle      | 11357 |   0.6737 |    0.6666 |   0.7454 |
+      | soccer-ball-field  | 260 |   0.7331 |    0.7035 |   0.8339 |
+      | storage-tank       | 5031 |   0.6718 |    0.6692 |   0.7701 |
+      | swimming-pool      | 693 |   0.6323 |    0.6286 |   0.6765 |
+      | tennis-court       | 1529 |   0.8633 |    0.8571 |   0.8923 |
+      | global             | 57768 |   0.6946 |    0.6859 |   0.7683 |
+    GTs with IoU≥thresh but no same-class hit (wrong-class / misaligned): 530
+    GTs with no detection above IoU thresh (missed / poor loc): 8697
+    GTs with 0% best IoU vs any detection (no spatial overlap): 1033 (1.79% of GTs)
+    histogram best IoU (any class) [0,0.25)/[0.25,0.5)/[0.5,0.75)/[0.75,1]: 4027/4670/16775/32296
+    histogram best IoU (correct class) [0,0.25)/[0.25,0.5)/[0.5,0.75)/[0.75,1]: 4737/4490/16524/32017
+  Class-agnostic duplicate rate (post score thresh; IoU≥0.50 vs a higher-score box): micro=1.00% (590/58962 boxes), macro mean over images with detections=1.51% (3007 images)
+  mAP: 0.7218  ↑0.0544 (72.18%)
+  Per-Class AP:
+    baseball-diamond: 0.7220  ↓0.1629 (72.20%)
+    basketball-court: 0.8159  ↓0.0827 (81.59%)
+    bridge: 0.4669  ↑0.1729 (46.69%)
+    ground-track-field: 0.7964  ↑0.0049 (79.64%)
+    harbor: 0.7594  ↑0.1436 (75.94%)
+    helicopter: 0.6986  ↑0.1151 (69.86%)
+    large-vehicle: 0.6869  ↑0.0792 (68.69%)
+    plane: 0.9017  ↑0.0876 (90.17%)
+    roundabout: 0.7151  ↑0.1827 (71.51%)
+    ship: 0.7563  ↑0.0640 (75.63%)
+    small-vehicle: 0.6039  ↑0.0205 (60.39%)
+    soccer-ball-field: 0.6245  ↓0.0552 (62.45%)
+    storage-tank: 0.7084  ↑0.1685 (70.84%)
+    swimming-pool: 0.6626  ↑0.0779 (66.26%)
+    tennis-court: 0.9089  ↓0.0001 (90.89%)
+  Ground Truth Classes: 15 classes
+  Predicted Classes: 15 classes
+  Timing: 38m 13s this epoch (avg 31m 38s) | ETA ~6h 19m for 12 epoch(s) left, mAP every 4 epoch(s).
+
+Epoch 25/36
+--------------------------------------------------
+Epoch 25/36 complete: 6846/6846 batches, loss: 0.5808
+  Effective LR: ref=2.500e-04 (0=2.500e-04)
+Validation complete: 1561 batches
+Validation Metrics:
+--------------------------------------------------
+  Eval thresholds (mAP / matching): score≥0.3000, IoU≥0.50
+  Avg Detections per Image (score≥0.3000): 16.88  ↑0.02
+  Avg Detections per Image (score≥0.5): 11.87  ↑0.11
+  Max detection score: 1.000
+  Mean detection score: 0.305
+  Time per Step: 0.4302  ↑0.0055
+  Accuracy / GT cover: (skipped — computed on mAP epochs)
+  Ground Truth objects: 57768
+  mAP: (skipped)
+  Ground Truth Classes: 15 classes
+  Predicted Classes: 15 classes
+  Timing: 25m 24s this epoch (avg 31m 23s) | ETA ~5h 45m for 11 epoch(s) left, mAP every 4 epoch(s).
+
+Epoch 26/36
+--------------------------------------------------
+Epoch 26/36 complete: 6846/6846 batches, loss: 0.5708
+  Effective LR: ref=2.500e-04 (0=2.500e-04)
+Validation complete: 1561 batches
+Validation Metrics:
+--------------------------------------------------
+  Eval thresholds (mAP / matching): score≥0.3000, IoU≥0.50
+  Avg Detections per Image (score≥0.3000): 16.90  ↑0.01
+  Avg Detections per Image (score≥0.5): 11.97  ↑0.09
+  Max detection score: 1.000
+  Mean detection score: 0.322
+  Time per Step: 0.4242  ↑0.0060
+  Accuracy / GT cover: (skipped — computed on mAP epochs)
+  Ground Truth objects: 57768
+  mAP: (skipped)
+  Ground Truth Classes: 15 classes
+  Predicted Classes: 15 classes
+  Timing: 24m 43s this epoch (avg 31m 8s) | ETA ~5h 11m for 10 epoch(s) left, mAP every 4 epoch(s).
+
+Epoch 27/36
+--------------------------------------------------
+Epoch 27/36 complete: 6846/6846 batches, loss: 0.5613
+  Effective LR: ref=2.500e-04 (0=2.500e-04)
+Validation complete: 1561 batches
+Validation Metrics:
+--------------------------------------------------
+  Eval thresholds (mAP / matching): score≥0.3000, IoU≥0.50
+  Avg Detections per Image (score≥0.3000): 16.93  ↑0.03
+  Avg Detections per Image (score≥0.5): 12.06  ↑0.10
+  Max detection score: 1.000
+  Mean detection score: 0.330
+  Time per Step: 0.4189  ↑0.0053
+  Accuracy / GT cover: (skipped — computed on mAP epochs)
+  Ground Truth objects: 57768
+  mAP: (skipped)
+  Ground Truth Classes: 15 classes
+  Predicted Classes: 15 classes
+  Timing: 24m 50s this epoch (avg 30m 54s) | ETA ~4h 38m for 9 epoch(s) left, mAP every 4 epoch(s).
+
+Epoch 28/36
+--------------------------------------------------
+Epoch 28/36 complete: 6846/6846 batches, loss: 0.5522
+  Effective LR: ref=2.500e-04 (0=2.500e-04)
+
+Computing mAP (periodic evaluation: every 4 epoch(s))...
+Validation complete: 1561 batches
+
+Computing mAP (this may take a while)...
+  Eval filters: score≥0.3000, IoU≥0.50
+  Images: 3,121
+  Total detections (post score filter): 54,602 (17.5 per image)
+  Total ground truths: 57,768 (18.5 per image)
+  Warning: Large number of detections may slow down mAP computation
+  Consider increasing evaluation.train_val_score_threshold (current: 0.3) for faster mAP matching
+  Computing AP for baseball-diamond: 379 dets, 364 GTs (137,956 IoU calculations)
+  Computing AP for bridge: 801 dets, 666 GTs (533,466 IoU calculations)
+  WARNING: harbor has 21,734,986 IoU calculations (5,057 dets × 4,298 GTs). This will be slow!
+  Computing AP for harbor: 5,057 dets, 4,298 GTs (21,734,986 IoU calculations)
+    Using chunked batch IoU computation (GPU-accelerated) for harbor (21,734,986 calculations)
+    Processing 5,057 detections in chunks of 1,163
+  WARNING: large-vehicle has 94,807,024 IoU calculations (10,088 dets × 9,398 GTs). This will be slow!
+  Computing AP for large-vehicle: 10,088 dets, 9,398 GTs (94,807,024 IoU calculations)
+    Using chunked batch IoU computation (GPU-accelerated) for large-vehicle (94,807,024 calculations)
+    Processing 10,088 detections in chunks of 532
+  WARNING: plane has 21,975,495 IoU calculations (4,645 dets × 4,731 GTs). This will be slow!
+  Computing AP for plane: 4,645 dets, 4,731 GTs (21,975,495 IoU calculations)
+    Using chunked batch IoU computation (GPU-accelerated) for plane (21,975,495 calculations)
+    Processing 4,645 detections in chunks of 1,056
+  WARNING: ship has 305,366,184 IoU calculations (16,476 dets × 18,534 GTs). This will be slow!
+  Computing AP for ship: 16,476 dets, 18,534 GTs (305,366,184 IoU calculations)
+    Using chunked batch IoU computation (GPU-accelerated) for ship (305,366,184 calculations)
+    Processing 16,476 detections in chunks of 269
+  WARNING: small-vehicle has 109,095,342 IoU calculations (9,606 dets × 11,357 GTs). This will be slow!
+  Computing AP for small-vehicle: 9,606 dets, 11,357 GTs (109,095,342 IoU calculations)
+    Using chunked batch IoU computation (GPU-accelerated) for small-vehicle (109,095,342 calculations)
+    Processing 9,606 detections in chunks of 440
+  WARNING: storage-tank has 20,405,736 IoU calculations (4,056 dets × 5,031 GTs). This will be slow!
+  Computing AP for storage-tank: 4,056 dets, 5,031 GTs (20,405,736 IoU calculations)
+    Using chunked batch IoU computation (GPU-accelerated) for storage-tank (20,405,736 calculations)
+    Processing 4,056 detections in chunks of 993
+  Computing AP for swimming-pool: 709 dets, 693 GTs (491,337 IoU calculations)
+  Computing AP for tennis-court: 1,542 dets, 1,529 GTs (2,357,718 IoU calculations)
+  mAP computation completed in 8m 39s. mAP: 0.7896
+Validation Metrics:
+--------------------------------------------------
+  Eval thresholds (mAP / matching): score≥0.3000, IoU≥0.50
+  Avg Detections per Image (score≥0.3000): 16.95  ↑0.02
+  Avg Detections per Image (score≥0.5): 12.16  ↑0.09
+  Max detection score: 1.000
+  Mean detection score: 0.329
+  Time per Step: 0.4136  ↑0.0053
+  Accuracy: 0.8303 (83.03%)
+  Correct Predictions: 45338/54602 (matched detections)
+  Ground Truth objects: 57768
+  GT covered pre-eval-threshold: 48650/57768
+  GT covered post-eval-threshold: 45358/57768
+  GT lost by eval-threshold filtering: 3292
+  GT cover rate pre-eval-threshold: 84.22%
+  GT cover rate post-eval-threshold: 78.52%
+  GT IoU vs raw dets (eval IoU threshold=0.50; per-GT max IoU over all dets / over same-class dets):
+    mean best IoU (any class): 0.7064, median: 0.7884
+    mean best IoU (correct class): 0.6976, median: 0.7875
+    per-class mean best IoU (raw detections):
+      | class              | gts | mean_any | mean_same | med_same |
+      |--------------------|-----:|----------:|-----------:|----------:|
+      | baseball-diamond   | 364 |   0.8156 |    0.8154 |   0.8309 |
+      | basketball-court   | 278 |   0.8774 |    0.8763 |   0.8993 |
+      | bridge             | 666 |   0.6789 |    0.6744 |   0.7253 |
+      | ground-track-field | 216 |   0.8405 |    0.8377 |   0.8590 |
+      | harbor             | 4298 |   0.7211 |    0.7188 |   0.7605 |
+      | helicopter         | 157 |   0.7683 |    0.7467 |   0.7619 |
+      | large-vehicle      | 9398 |   0.7216 |    0.7114 |   0.7822 |
+      | plane              | 4731 |   0.8213 |    0.8212 |   0.8642 |
+      | roundabout         | 256 |   0.7872 |    0.7733 |   0.8599 |
+      | ship               | 18534 |   0.6640 |    0.6499 |   0.7831 |
+      | small-vehicle      | 11357 |   0.6844 |    0.6749 |   0.7581 |
+      | soccer-ball-field  | 260 |   0.7747 |    0.7523 |   0.8720 |
+      | storage-tank       | 5030 |   0.6841 |    0.6829 |   0.7828 |
+      | swimming-pool      | 693 |   0.6601 |    0.6586 |   0.7048 |
+      | tennis-court       | 1529 |   0.8905 |    0.8843 |   0.9102 |
+      | global             | 57767 |   0.7064 |    0.6976 |   0.7875 |
+    GTs with IoU≥thresh but no same-class hit (wrong-class / misaligned): 487
+    GTs with no detection above IoU thresh (missed / poor loc): 8617
+    GTs with 0% best IoU vs any detection (no spatial overlap): 1050 (1.82% of GTs)
+    histogram best IoU (any class) [0,0.25)/[0.25,0.5)/[0.5,0.75)/[0.75,1]: 4462/4155/13830/35320
+    histogram best IoU (correct class) [0,0.25)/[0.25,0.5)/[0.5,0.75)/[0.75,1]: 5186/3918/13568/35095
+  Class-agnostic duplicate rate (post score thresh; IoU≥0.50 vs a higher-score box): micro=0.75% (408/54602 boxes), macro mean over images with detections=1.27% (3066 images)
+  mAP: 0.7896  ↑0.0677 (78.96%)
+  Per-Class AP:
+    baseball-diamond: 0.9072  ↑0.1852 (90.72%)
+    basketball-court: 0.9030  ↑0.0871 (90.30%)
+    bridge: 0.7292  ↑0.2622 (72.92%)
+    ground-track-field: 0.9058  ↑0.1094 (90.58%)
+    harbor: 0.7657  ↑0.0063 (76.57%)
+    helicopter: 0.8950  ↑0.1964 (89.50%)
+    large-vehicle: 0.7031  ↑0.0162 (70.31%)
+    plane: 0.9052  ↑0.0035 (90.52%)
+    roundabout: 0.8104  ↑0.0953 (81.04%)
+    ship: 0.6748  ↓0.0815 (67.48%)
+    small-vehicle: 0.6107  ↑0.0068 (61.07%)
+    soccer-ball-field: 0.7998  ↑0.1752 (79.98%)
+    storage-tank: 0.6298  ↓0.0786 (62.98%)
+    swimming-pool: 0.6946  ↑0.0320 (69.46%)
+    tennis-court: 0.9091  ↑0.0002 (90.91%)
+  Ground Truth Classes: 15 classes
+  Predicted Classes: 15 classes
+  Timing: 35m 26s this epoch (avg 31m 3s) | ETA ~4h 8m for 8 epoch(s) left, mAP every 4 epoch(s).
+
+Epoch 29/36
+--------------------------------------------------
+Epoch 29/36 complete: 6846/6846 batches, loss: 0.5437
+  Effective LR: ref=2.500e-04 (0=2.500e-04)
+Validation complete: 1561 batches
+Validation Metrics:
+--------------------------------------------------
+  Eval thresholds (mAP / matching): score≥0.3000, IoU≥0.50
+  Avg Detections per Image (score≥0.3000): 16.97  ↑0.02
+  Avg Detections per Image (score≥0.5): 12.25  ↑0.09
+  Max detection score: 1.000
+  Mean detection score: 0.318
+  Time per Step: 0.4087  ↑0.0049
+  Accuracy / GT cover: (skipped — computed on mAP epochs)
+  Ground Truth objects: 57768
+  mAP: (skipped)
+  Ground Truth Classes: 15 classes
+  Predicted Classes: 15 classes
+  Timing: 24m 37s this epoch (avg 30m 50s) | ETA ~3h 35m for 7 epoch(s) left, mAP every 4 epoch(s).
+
+Epoch 30/36
+--------------------------------------------------
+Epoch 30/36 complete: 6846/6846 batches, loss: 0.5355
+  Effective LR: ref=2.500e-04 (0=2.500e-04)
+Validation complete: 1561 batches
+Validation Metrics:
+--------------------------------------------------
+  Eval thresholds (mAP / matching): score≥0.3000, IoU≥0.50
+  Avg Detections per Image (score≥0.3000): 16.99  ↑0.02
+  Avg Detections per Image (score≥0.5): 12.33  ↑0.08
+  Max detection score: 1.000
+  Mean detection score: 0.320
+  Time per Step: 0.4042  ↑0.0045
+  Accuracy / GT cover: (skipped — computed on mAP epochs)
+  Ground Truth objects: 57768
+  mAP: (skipped)
+  Ground Truth Classes: 15 classes
+  Predicted Classes: 15 classes
+  Timing: 24m 43s this epoch (avg 30m 38s) | ETA ~3h 3m for 6 epoch(s) left, mAP every 4 epoch(s).
+
+Epoch 31/36
+--------------------------------------------------
+Epoch 31/36 complete: 6846/6846 batches, loss: 0.5279
+  Effective LR: ref=2.500e-04 (0=2.500e-04)
+Validation complete: 1561 batches
+Validation Metrics:
+--------------------------------------------------
+  Eval thresholds (mAP / matching): score≥0.3000, IoU≥0.50
+  Avg Detections per Image (score≥0.3000): 17.01  ↑0.02
+  Avg Detections per Image (score≥0.5): 12.41  ↑0.07
+  Max detection score: 1.000
+  Mean detection score: 0.332
+  Time per Step: 0.3998  ↑0.0044
+  Accuracy / GT cover: (skipped — computed on mAP epochs)
+  Ground Truth objects: 57768
+  mAP: (skipped)
+  Ground Truth Classes: 15 classes
+  Predicted Classes: 15 classes
+  Timing: 24m 34s this epoch (avg 30m 26s) | ETA ~2h 32m for 5 epoch(s) left, mAP every 4 epoch(s).
+
+Epoch 32/36
+--------------------------------------------------
+Epoch 32/36 complete: 6846/6846 batches, loss: 0.5205
+  Effective LR: ref=2.500e-04 (0=2.500e-04)
+
+Computing mAP (periodic evaluation: every 4 epoch(s))...
+Validation complete: 1561 batches
+
+Computing mAP (this may take a while)...
+  Eval filters: score≥0.3000, IoU≥0.50
+  Images: 3,121
+  Total detections (post score filter): 54,703 (17.5 per image)
+  Total ground truths: 57,768 (18.5 per image)
+  Warning: Large number of detections may slow down mAP computation
+  Consider increasing evaluation.train_val_score_threshold (current: 0.3) for faster mAP matching
+  Computing AP for baseball-diamond: 381 dets, 364 GTs (138,684 IoU calculations)
+  Computing AP for bridge: 812 dets, 666 GTs (540,792 IoU calculations)
+  WARNING: harbor has 21,382,550 IoU calculations (4,975 dets × 4,298 GTs). This will be slow!
+  Computing AP for harbor: 4,975 dets, 4,298 GTs (21,382,550 IoU calculations)
+    Using chunked batch IoU computation (GPU-accelerated) for harbor (21,382,550 calculations)
+    Processing 4,975 detections in chunks of 1,163
+  WARNING: large-vehicle has 90,728,292 IoU calculations (9,654 dets × 9,398 GTs). This will be slow!
+  Computing AP for large-vehicle: 9,654 dets, 9,398 GTs (90,728,292 IoU calculations)
+    Using chunked batch IoU computation (GPU-accelerated) for large-vehicle (90,728,292 calculations)
+    Processing 9,654 detections in chunks of 532
+  WARNING: plane has 21,757,869 IoU calculations (4,599 dets × 4,731 GTs). This will be slow!
+  Computing AP for plane: 4,599 dets, 4,731 GTs (21,757,869 IoU calculations)
+    Using chunked batch IoU computation (GPU-accelerated) for plane (21,757,869 calculations)
+    Processing 4,599 detections in chunks of 1,056
+  WARNING: ship has 309,425,130 IoU calculations (16,695 dets × 18,534 GTs). This will be slow!
+  Computing AP for ship: 16,695 dets, 18,534 GTs (309,425,130 IoU calculations)
+    Using chunked batch IoU computation (GPU-accelerated) for ship (309,425,130 calculations)
+    Processing 16,695 detections in chunks of 269
+  WARNING: small-vehicle has 113,763,069 IoU calculations (10,017 dets × 11,357 GTs). This will be slow!
+  Computing AP for small-vehicle: 10,017 dets, 11,357 GTs (113,763,069 IoU calculations)
+    Using chunked batch IoU computation (GPU-accelerated) for small-vehicle (113,763,069 calculations)
+    Processing 10,017 detections in chunks of 440
+  WARNING: storage-tank has 20,264,868 IoU calculations (4,028 dets × 5,031 GTs). This will be slow!
+  Computing AP for storage-tank: 4,028 dets, 5,031 GTs (20,264,868 IoU calculations)
+    Using chunked batch IoU computation (GPU-accelerated) for storage-tank (20,264,868 calculations)
+    Processing 4,028 detections in chunks of 993
+  Computing AP for swimming-pool: 740 dets, 693 GTs (512,820 IoU calculations)
+  Computing AP for tennis-court: 1,530 dets, 1,529 GTs (2,339,370 IoU calculations)
+  mAP computation completed in 8m 43s. mAP: 0.7848
+Validation Metrics:
+--------------------------------------------------
+  Eval thresholds (mAP / matching): score≥0.3000, IoU≥0.50
+  Avg Detections per Image (score≥0.3000): 17.02  ↑0.02
+  Avg Detections per Image (score≥0.5): 12.48  ↑0.07
+  Max detection score: 1.000
+  Mean detection score: 0.325
+  Time per Step: 0.3956  ↑0.0042
+  Accuracy: 0.8301 (83.01%)
+  Correct Predictions: 45410/54703 (matched detections)
+  Ground Truth objects: 57768
+  GT covered pre-eval-threshold: 48631/57768
+  GT covered post-eval-threshold: 45431/57768
+  GT lost by eval-threshold filtering: 3200
+  GT cover rate pre-eval-threshold: 84.18%
+  GT cover rate post-eval-threshold: 78.64%
+  GT IoU vs raw dets (eval IoU threshold=0.50; per-GT max IoU over all dets / over same-class dets):
+    mean best IoU (any class): 0.7093, median: 0.7916
+    mean best IoU (correct class): 0.7002, median: 0.7907
+    per-class mean best IoU (raw detections):
+      | class              | gts | mean_any | mean_same | med_same |
+      |--------------------|-----:|----------:|-----------:|----------:|
+      | baseball-diamond   | 364 |   0.8216 |    0.8215 |   0.8400 |
+      | basketball-court   | 278 |   0.8768 |    0.8761 |   0.9045 |
+      | bridge             | 666 |   0.6908 |    0.6886 |   0.7393 |
+      | ground-track-field | 216 |   0.8463 |    0.8452 |   0.8670 |
+      | harbor             | 4298 |   0.7281 |    0.7255 |   0.7681 |
+      | helicopter         | 157 |   0.7699 |    0.7449 |   0.7687 |
+      | large-vehicle      | 9398 |   0.7241 |    0.7073 |   0.7856 |
+      | plane              | 4731 |   0.8241 |    0.8239 |   0.8674 |
+      | roundabout         | 256 |   0.7926 |    0.7898 |   0.8649 |
+      | ship               | 18534 |   0.6648 |    0.6501 |   0.7844 |
+      | small-vehicle      | 11357 |   0.6890 |    0.6843 |   0.7606 |
+      | soccer-ball-field  | 260 |   0.7779 |    0.7589 |   0.8759 |
+      | storage-tank       | 5030 |   0.6856 |    0.6841 |   0.7879 |
+      | swimming-pool      | 693 |   0.6659 |    0.6659 |   0.7094 |
+      | tennis-court       | 1529 |   0.8973 |    0.8911 |   0.9148 |
+      | global             | 57767 |   0.7093 |    0.7002 |   0.7907 |
+    GTs with IoU≥thresh but no same-class hit (wrong-class / misaligned): 482
+    GTs with no detection above IoU thresh (missed / poor loc): 8642
+    GTs with 0% best IoU vs any detection (no spatial overlap): 1000 (1.73% of GTs)
+    histogram best IoU (any class) [0,0.25)/[0.25,0.5)/[0.5,0.75)/[0.75,1]: 4365/4277/13354/35771
+    histogram best IoU (correct class) [0,0.25)/[0.25,0.5)/[0.5,0.75)/[0.75,1]: 5130/3994/13094/35549
+  Class-agnostic duplicate rate (post score thresh; IoU≥0.50 vs a higher-score box): micro=0.69% (380/54703 boxes), macro mean over images with detections=1.29% (3063 images)
+  mAP: 0.7848  ↓0.0048 (78.48%)
+  Per-Class AP:
+    baseball-diamond: 0.9083  ↑0.0011 (90.83%)
+    basketball-court: 0.9014  ↓0.0015 (90.14%)
+    bridge: 0.7403  ↑0.0112 (74.03%)
+    ground-track-field: 0.9068  ↑0.0009 (90.68%)
+    harbor: 0.7680  ↑0.0022 (76.80%)
+    helicopter: 0.8175  ↓0.0775 (81.75%)
+    large-vehicle: 0.6877  ↓0.0155 (68.77%)
+    plane: 0.9049  ↓0.0003 (90.49%)
+    roundabout: 0.8103  ↓0.0001 (81.03%)
+    ship: 0.6721  ↓0.0027 (67.21%)
+    small-vehicle: 0.6081  ↓0.0026 (60.81%)
+    soccer-ball-field: 0.8072  ↑0.0075 (80.72%)
+    storage-tank: 0.6295  ↓0.0004 (62.95%)
+    swimming-pool: 0.7008  ↑0.0062 (70.08%)
+    tennis-court: 0.9086  ↓0.0005 (90.86%)
+  Ground Truth Classes: 15 classes
+  Predicted Classes: 15 classes
+  Timing: 35m 19s this epoch (avg 30m 35s) | ETA ~2h 2m for 4 epoch(s) left, mAP every 4 epoch(s).
+
+Epoch 33/36
+--------------------------------------------------
+Epoch 33/36 complete: 6846/6846 batches, loss: 0.5135
+  Effective LR: ref=2.500e-04 (0=2.500e-04)
+Validation complete: 1561 batches
+Validation Metrics:
+--------------------------------------------------
+  Eval thresholds (mAP / matching): score≥0.3000, IoU≥0.50
+  Avg Detections per Image (score≥0.3000): 17.04  ↑0.01
+  Avg Detections per Image (score≥0.5): 12.55  ↑0.07
+  Max detection score: 1.000
+  Mean detection score: 0.334
+  Time per Step: 0.3917  ↑0.0039
+  Accuracy / GT cover: (skipped — computed on mAP epochs)
+  Ground Truth objects: 57768
+  mAP: (skipped)
+  Ground Truth Classes: 15 classes
+  Predicted Classes: 15 classes
+  Timing: 24m 33s this epoch (avg 30m 24s) | ETA ~1h 31m for 3 epoch(s) left, mAP every 4 epoch(s).
+
+Epoch 34/36
+--------------------------------------------------
+Epoch 34/36 complete: 6846/6846 batches, loss: 0.5068
+  Effective LR: ref=2.500e-05 (0=2.500e-05)
+Validation complete: 1561 batches
+Validation Metrics:
+--------------------------------------------------
+  Eval thresholds (mAP / matching): score≥0.3000, IoU≥0.50
+  Avg Detections per Image (score≥0.3000): 17.05  ↑0.01
+  Avg Detections per Image (score≥0.5): 12.61  ↑0.06
+  Max detection score: 1.000
+  Mean detection score: 0.336
+  Time per Step: 0.3879  ↑0.0038
+  Accuracy / GT cover: (skipped — computed on mAP epochs)
+  Ground Truth objects: 57768
+  mAP: (skipped)
+  Ground Truth Classes: 15 classes
+  Predicted Classes: 15 classes
+  Timing: 24m 23s this epoch (avg 30m 14s) | ETA ~1h 0m for 2 epoch(s) left, mAP every 4 epoch(s).
+
+Epoch 35/36
+--------------------------------------------------
+Epoch 35/36 complete: 6846/6846 batches, loss: 0.5004
+  Effective LR: ref=2.500e-05 (0=2.500e-05)
+Validation complete: 1561 batches
+Validation Metrics:
+--------------------------------------------------
+  Eval thresholds (mAP / matching): score≥0.3000, IoU≥0.50
+  Avg Detections per Image (score≥0.3000): 17.06  ↑0.01
+  Avg Detections per Image (score≥0.5): 12.67  ↑0.06
+  Max detection score: 1.000
+  Mean detection score: 0.334
+  Time per Step: 0.3843  ↑0.0036
+  Accuracy / GT cover: (skipped — computed on mAP epochs)
+  Ground Truth objects: 57768
+  mAP: (skipped)
+  Ground Truth Classes: 15 classes
+  Predicted Classes: 15 classes
+  Timing: 24m 21s this epoch (avg 30m 4s) | ETA ~30m 4s for 1 epoch(s) left, mAP every 4 epoch(s).
+
+Epoch 36/36
+--------------------------------------------------
+Epoch 36/36 complete: 6846/6846 batches, loss: 0.4943
+  Effective LR: ref=2.500e-05 (0=2.500e-05)
+
+Computing mAP (periodic evaluation: every 4 epoch(s))...
+Validation complete: 1561 batches
+
+Computing mAP (this may take a while)...
+  Eval filters: score≥0.3000, IoU≥0.50
+  Images: 3,121
+  Total detections (post score filter): 54,778 (17.6 per image)
+  Total ground truths: 57,768 (18.5 per image)
+  Warning: Large number of detections may slow down mAP computation
+  Consider increasing evaluation.train_val_score_threshold (current: 0.3) for faster mAP matching
+  Computing AP for baseball-diamond: 382 dets, 364 GTs (139,048 IoU calculations)
+  Computing AP for bridge: 762 dets, 666 GTs (507,492 IoU calculations)
+  WARNING: harbor has 21,421,232 IoU calculations (4,984 dets × 4,298 GTs). This will be slow!
+  Computing AP for harbor: 4,984 dets, 4,298 GTs (21,421,232 IoU calculations)
+    Using chunked batch IoU computation (GPU-accelerated) for harbor (21,421,232 calculations)
+    Processing 4,984 detections in chunks of 1,163
+  WARNING: large-vehicle has 92,401,136 IoU calculations (9,832 dets × 9,398 GTs). This will be slow!
+  Computing AP for large-vehicle: 9,832 dets, 9,398 GTs (92,401,136 IoU calculations)
+    Using chunked batch IoU computation (GPU-accelerated) for large-vehicle (92,401,136 calculations)
+    Processing 9,832 detections in chunks of 532
+  WARNING: plane has 21,876,144 IoU calculations (4,624 dets × 4,731 GTs). This will be slow!
+  Computing AP for plane: 4,624 dets, 4,731 GTs (21,876,144 IoU calculations)
+    Using chunked batch IoU computation (GPU-accelerated) for plane (21,876,144 calculations)
+    Processing 4,624 detections in chunks of 1,056
+  WARNING: ship has 308,424,294 IoU calculations (16,641 dets × 18,534 GTs). This will be slow!
+  Computing AP for ship: 16,641 dets, 18,534 GTs (308,424,294 IoU calculations)
+    Using chunked batch IoU computation (GPU-accelerated) for ship (308,424,294 calculations)
+    Processing 16,641 detections in chunks of 269
+  WARNING: small-vehicle has 111,298,600 IoU calculations (9,800 dets × 11,357 GTs). This will be slow!
+  Computing AP for small-vehicle: 9,800 dets, 11,357 GTs (111,298,600 IoU calculations)
+    Using chunked batch IoU computation (GPU-accelerated) for small-vehicle (111,298,600 calculations)
+    Processing 9,800 detections in chunks of 440
+  WARNING: storage-tank has 21,331,440 IoU calculations (4,240 dets × 5,031 GTs). This will be slow!
+  Computing AP for storage-tank: 4,240 dets, 5,031 GTs (21,331,440 IoU calculations)
+    Using chunked batch IoU computation (GPU-accelerated) for storage-tank (21,331,440 calculations)
+    Processing 4,240 detections in chunks of 993
+  Computing AP for swimming-pool: 726 dets, 693 GTs (503,118 IoU calculations)
+  Computing AP for tennis-court: 1,535 dets, 1,529 GTs (2,347,015 IoU calculations)
+  mAP computation completed in 8m 41s. mAP: 0.7962
+Validation Metrics:
+--------------------------------------------------
+  Eval thresholds (mAP / matching): score≥0.3000, IoU≥0.50
+  Avg Detections per Image (score≥0.3000): 17.08  ↑0.01
+  Avg Detections per Image (score≥0.5): 12.73  ↑0.06
+  Max detection score: 1.000
+  Mean detection score: 0.343
+  Time per Step: 0.3808  ↑0.0034
+  Accuracy: 0.8312 (83.12%)
+  Correct Predictions: 45531/54778 (matched detections)
+  Ground Truth objects: 57768
+  GT covered pre-eval-threshold: 48655/57768
+  GT covered post-eval-threshold: 45552/57768
+  GT lost by eval-threshold filtering: 3103
+  GT cover rate pre-eval-threshold: 84.22%
+  GT cover rate post-eval-threshold: 78.85%
+  GT IoU vs raw dets (eval IoU threshold=0.50; per-GT max IoU over all dets / over same-class dets):
+    mean best IoU (any class): 0.7092, median: 0.7916
+    mean best IoU (correct class): 0.7011, median: 0.7907
+    per-class mean best IoU (raw detections):
+      | class              | gts | mean_any | mean_same | med_same |
+      |--------------------|-----:|----------:|-----------:|----------:|
+      | baseball-diamond   | 364 |   0.8254 |    0.8252 |   0.8392 |
+      | basketball-court   | 278 |   0.8805 |    0.8792 |   0.9049 |
+      | bridge             | 666 |   0.6935 |    0.6905 |   0.7424 |
+      | ground-track-field | 216 |   0.8487 |    0.8480 |   0.8664 |
+      | harbor             | 4298 |   0.7315 |    0.7291 |   0.7701 |
+      | helicopter         | 157 |   0.7775 |    0.7543 |   0.7801 |
+      | large-vehicle      | 9398 |   0.7237 |    0.7103 |   0.7856 |
+      | plane              | 4731 |   0.8259 |    0.8258 |   0.8692 |
+      | roundabout         | 256 |   0.7985 |    0.7876 |   0.8646 |
+      | ship               | 18534 |   0.6641 |    0.6513 |   0.7852 |
+      | small-vehicle      | 11357 |   0.6860 |    0.6801 |   0.7591 |
+      | soccer-ball-field  | 260 |   0.7829 |    0.7630 |   0.8750 |
+      | storage-tank       | 5030 |   0.6877 |    0.6867 |   0.7857 |
+      | swimming-pool      | 693 |   0.6667 |    0.6667 |   0.7145 |
+      | tennis-court       | 1529 |   0.8996 |    0.8934 |   0.9165 |
+      | global             | 57767 |   0.7092 |    0.7011 |   0.7907 |
+    GTs with IoU≥thresh but no same-class hit (wrong-class / misaligned): 417
+    GTs with no detection above IoU thresh (missed / poor loc): 8687
+    GTs with 0% best IoU vs any detection (no spatial overlap): 1043 (1.81% of GTs)
+    histogram best IoU (any class) [0,0.25)/[0.25,0.5)/[0.5,0.75)/[0.75,1]: 4445/4242/13222/35858
+    histogram best IoU (correct class) [0,0.25)/[0.25,0.5)/[0.5,0.75)/[0.75,1]: 5114/3990/13007/35656
+  Class-agnostic duplicate rate (post score thresh; IoU≥0.50 vs a higher-score box): micro=0.67% (365/54778 boxes), macro mean over images with detections=1.29% (3064 images)
+  mAP: 0.7962  ↑0.0115 (79.62%)
+  Per-Class AP:
+    baseball-diamond: 0.9085  ↑0.0003 (90.85%)
+    basketball-court: 0.9017  ↑0.0003 (90.17%)
+    bridge: 0.7439  ↑0.0035 (74.39%)
+    ground-track-field: 0.9078  ↑0.0010 (90.78%)
+    harbor: 0.7684  ↑0.0005 (76.84%)
+    helicopter: 0.8967  ↑0.0792 (89.67%)
+    large-vehicle: 0.6889  ↑0.0012 (68.89%)
+    plane: 0.9050  ↑0.0000 (90.50%)
+    roundabout: 0.8114  ↑0.0012 (81.14%)
+    ship: 0.6725  ↑0.0004 (67.25%)
+    small-vehicle: 0.6087  ↑0.0006 (60.87%)
+    soccer-ball-field: 0.8104  ↑0.0032 (81.04%)
+    storage-tank: 0.7086  ↑0.0791 (70.86%)
+    swimming-pool: 0.7018  ↑0.0010 (70.18%)
+    tennis-court: 0.9091  ↑0.0005 (90.91%)
+  Ground Truth Classes: 15 classes
+  Predicted Classes: 15 classes
+  Timing: 35m 15s this epoch (avg 30m 12s) | Training loop finished (no final mAP).
+
+================================================================================
+Training timing summary
+================================================================================
+  Started:   2026-09-13T03:19:04+00:00
+  Finished:  2026-09-13T21:26:33+00:00
+  Total wall: 18h 7m (65249.7 s)
+  Epochs:    36 completed (mean 30m 12s, min 24m 21s, max 43m 46s)
+================================================================================
+
+================================================================================
+Training completed successfully!
+================================================================================
+Experiment directory: /home/jeffaudi/projects/oriented-det/runs/rotated_retinanet/20260913-031811
+  Checkpoints saved to: /home/jeffaudi/projects/oriented-det/runs/rotated_retinanet/20260913-031811/checkpoints
+  TensorBoard logs saved to: /home/jeffaudi/projects/oriented-det/runs/rotated_retinanet/20260913-031811
+  Config saved to: /home/jeffaudi/projects/oriented-det/runs/rotated_retinanet/20260913-031811/config.json
+
+View TensorBoard with:
+  tensorboard --logdir /home/jeffaudi/projects/oriented-det/runs/rotated_retinanet
+  tensorboard --logdir /home/jeffaudi/projects/oriented-det/runs/rotated_retinanet/20260913-031811

rotated_retinanet_r50_fpn_dota_le90_3x-42968545.pth

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