File size: 3,984 Bytes
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To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2026-06-03 16:31:52.095285: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
2026-06-03 16:31:59.966324: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 46732 MB memory: -> device: 0, name: NVIDIA RTX A6000, pci bus id: 0000:41:00.0, compute capability: 8.6
WARNING:absl:Skipping variable loading for optimizer 'RMSprop', because it has 1 variables whereas the saved optimizer has 301 variables.
2026-06-03 16:32:05.682868: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:432] Loaded cuDNN version 8907
2026-06-03 16:32:05.816842: I tensorflow/tsl/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory
2026-06-03 16:32:06.284320: I tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:606] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once.
Building model…
Weights loaded.
Loading 92 test images…
Predicting…
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12/12 [==============================] - 3s 70ms/step
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ROCKGLACIERNET — EVALUATION SUMMARY (baseline 128x128)
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Test images : 92
Best threshold by IoU : 0.65
GLOBAL (pixel-pooled) metrics @ best threshold:
precision : 0.6935
recall : 0.8028
f1 : 0.7442
iou : 0.5926
dice : 0.7442
PER-IMAGE IoU (@ t=0.5):
mean : 0.6622
median : 0.6957
Threshold sweep:
thresh precision recall f1 iou dice
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0.3 0.6169 0.8922 0.7294 0.5741 0.7294
0.35 0.6276 0.8812 0.7331 0.5786 0.7331
0.4 0.6382 0.8699 0.7363 0.5826 0.7363
0.45 0.6483 0.8583 0.7387 0.5856 0.7387
0.5 0.6582 0.8463 0.7405 0.5879 0.7405
0.55 0.6689 0.8332 0.7421 0.5899 0.7421
0.6 0.6807 0.8193 0.7436 0.5919 0.7436
0.65 0.6935 0.8028 0.7442 0.5926 0.7442 <- best IoU
0.7 0.7081 0.7822 0.7433 0.5915 0.7433
10 WORST images by IoU (@ t=0.5):
image_id orig_H orig_W glacier_frac_gt iou precision recall
0242 130 120 0.1247 0.2662 0.2758 0.8841
0286 289 177 0.3099 0.3416 0.3696 0.8186
0450 80 82 0.0709 0.3615 0.3849 0.8559
0288 250 115 0.2682 0.3783 0.5689 0.5304
0301 125 84 0.1251 0.3966 0.4103 0.9224
0491 141 74 0.1455 0.4199 0.4927 0.7395
0615 91 60 0.0974 0.4655 0.5520 0.7481
0234 98 110 0.2587 0.4680 0.8010 0.5296
0032 55 60 0.0436 0.4833 0.5472 0.8056
0268 146 135 0.2199 0.4892 0.6085 0.7138
Output dir: outputs/predictions/baseline_model_output
Files: per_image_metrics.csv, evaluation_metrics.csv, confusion_matrix.csv
Saved 20 comparison PNGs (10 worst + 10 best).
Evaluation complete.
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