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>
57 lines
1.6 KiB
YAML
57 lines
1.6 KiB
YAML
# GridBased training -- polychromatic 5-30 keV, MOENCH014 @150 V
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#
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# Same architecture and the same losses as the 12 keV configuration; what
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# changes is that the energy head now carries information, so it gets a real
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# weight and a scale that is not tied to a beam energy.
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experiment:
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name: "poly5to30keV_Moench014"
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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/poly5to30keV_Moench014_150V
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train_shards: [0, 11]
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val_shards: [0, 1]
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max_train_frames: null
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max_val_frames: 8000
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input_scale: 15.0 # keV, mid-range; frames are divided by this
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energy_scale: 15.0 # keV, the energy target is E / energy_scale
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augment: true
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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, 128, 128] # still 4 x conv3x3, so R = 4 px; wider, because
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# the cloud size now has to be disentangled from
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# the energy rather than mapping straight to depth
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predict_energy: true
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norm: "none"
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prior_prob: 0.01
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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: 1.0 # 0.1 at a single energy, where the head learned a
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# constant; here it carries the spectrum
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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
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smooth_l1_beta_energy: 0.03 # ~ the 0.39 keV noise floor of a 3x3 sum, / 15 keV
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training:
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epochs: 300
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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: 6
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amp: true
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seed: 0
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checkpoint_epochs: [50, 100, 150, 200, 250, 300]
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eval:
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threshold: 0.3
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merge_distance: 0.1
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match_radius: 1.0
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pixel_size_um: 25.0
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