xie_x1andClaude Opus 5.5
0f9a3f496f
Code, configs, charge LUTs, best.pth and evaluation records for each training run, the knife-edge / Siemens-star / XRF notebooks with outputs, and the overnight run reports. Epoch checkpoints, inference results and HTML exports stay local (see .gitignore). Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
50 lines
1.5 KiB
YAML
50 lines
1.5 KiB
YAML
# GridBased training -- 12 keV, MOENCH040 @150 V (WP3, monochromatic gate G2)
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experiment:
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name: "12keV_Moench040"
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results_dir: "Results"
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data:
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folder: /mnt/sls_det_storage/moench_data/MLXID/Samples/GridBased/12keV_Moench040_150V
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train_shards: [0, 11] # inclusive
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val_shards: [0, 1]
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max_train_frames: null # null = all
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max_val_frames: 8000
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input_scale: 12.0 # keV; frames are divided by this
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energy_scale: 12.0 # keV; energy target is E / energy_scale
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augment: true # 8-fold D4, a true symmetry of the problem
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batch_size_train: 256
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batch_size_val: 512
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num_workers: 8
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model:
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channels: [32, 64, 64, 64] # 4 x conv3x3 -> receptive-field radius R = 4 px
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predict_energy: true
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norm: "none" # see models.py: 'group' breaks strict locality
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prior_prob: 0.01 # focal-loss bias init
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loss:
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w_obj: 1.0
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w_off: 1.0
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w_ene: 0.1
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focal_alpha: 0.25
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focal_gamma: 2.0
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smooth_l1_beta_offset: 0.01 # px = 0.25 um, as for the cluster-based models
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smooth_l1_beta_energy: 0.02
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training:
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epochs: 150
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lr: 1.0e-3
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weight_decay: 0.0
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scheduler_factor: 0.5
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scheduler_patience: 4
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amp: true
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seed: 0
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checkpoint_epochs: [25, 50, 75, 100, 125, 150]
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eval:
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threshold: 0.3 # from the ROC of run 260830_00: eff plateaus below 0.3
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merge_distance: 0.1 # px; position-space duplicate suppression
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match_radius: 1.0 # px; 25 um
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pixel_size_um: 25.0
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