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Upload v2/picotrust_v2.yaml with huggingface_hub

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+ experiment_name: picotrust_v2
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+
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+ model:
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+ type: picotrust
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+ encoder_size: 512
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+ decoder_size: 256 # Proven 256 decoder; trainer downsamples 512→256
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+
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+ data:
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+ source: folder
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+ path: ./data/train
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+ batch_size: 4
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+
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+ training:
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+ num_steps: 200000
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+ lr: 0.0001
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+ num_bits: 100
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+ image_size: 512
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+ warmup_steps: 500
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+ no_im_loss_steps: 10000 # 10k steps pure message — establish communication first
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+ generator_grad_clip: 0.25
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+ stn_lr_scale: 0.01
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+ borders: black
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+ rnd_trans: 0.10
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+ rnd_trans_ramp: 10000
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+ phase2_step: 60000
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+ phase2_decoder_lr_scale: 0.1
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+ residual_strength: 1.0 # Start unbounded — let encoder-decoder bootstrap
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+ residual_strength_anneal_target: 0.03 # Target ±0.03 for ~38+ dB PSNR
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+ residual_strength_anneal_start: 10000 # Start when image losses activate
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+ residual_strength_anneal_steps: 60000 # Slow squeeze 1.0→0.03 over 60k steps
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+
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+ loss:
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+ message:
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+ scale: 5.0
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+ ramp_steps: 1
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+ l2:
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+ scale: 1.5
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+ ramp_steps: 50000 # Slow squeeze — give encoder time to learn compact embedding
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+ lpips:
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+ scale: 1.0
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+ ramp_steps: 50000
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+ ffl:
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+ scale: 1.0
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+ ramp_steps: 50000
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+ delay_steps: 30000
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+ # chroma: not needed — grayscale residual (1-channel E_post) eliminates colour shifts by construction
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+ # mask_reg: not needed — bounded residual handles quality guarantee
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+ message_loss_type: mse
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+ l2_edge_gain: 0.0 # Disabled — was dominating loss, forcing content-agnostic residual
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+ use_border_falloff: true
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+ gan_config:
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+ enabled: true
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+ discriminator_lr: 0.00001
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+ g_loss_scale: 1.0
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+ g_loss_ramp_steps: 20000
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+
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+ distortion:
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+ strategy: curriculum
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+ perspective:
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+ strength: 0.1
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+ ramp_steps: 10000
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+ brightness:
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+ strength: 0.3
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+ ramp_steps: 1000
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+ saturation:
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+ strength: 1.0
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+ ramp_steps: 1000
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+ hue:
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+ strength: 0.1
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+ ramp_steps: 1000
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+ noise:
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+ strength: 0.02
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+ ramp_steps: 1000
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+ jpeg_quality:
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+ strength: 25
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+ ramp_steps: 1000
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+ enable_jpeg: true
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+ enable_blur: true
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+
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+ checkpoint:
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+ dir: checkpoints
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+ save_every_steps: 10000
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+ keep_last: 3
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+
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+ logging:
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+ backends: [console, tensorboard]
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+ log_every_steps: 100