r2s-cur-d025-9a606d4-30b17847-47646604
completed —
Hypothesis
With dropout 0.25 the model transcribes validation renders better than v9 (dose between 0.2 and 0.3). Pass requires the paired 95% bootstrap interval on the per-icon reduction in mean 72 px pixel error on the 339 validation renders to exclude zero against each normal v9 run (RTX 4080 original, H100 rerun, B300 seeds 2 and 3) and, for depth arms, a direction consistent across seeds against dropout 0.2 at 6 layers. Its greedy latency on the RTX 4080 is recorded.
Visual output
Written result
Hypothesis. With dropout 0.25 the model transcribes validation renders better than v9 (dose between 0.2 and 0.3). Pass requires the paired 95% bootstrap interval on the per-icon reduction in mean 72 px pixel error on the 339 validation renders to exclude zero against each normal v9 run (RTX 4080 original, H100 rerun, B300 seeds 2 and 3) and, for depth arms, a direction consistent across seeds against dropout 0.2 at 6 layers. Its greedy latency on the RTX 4080 is recorded.
Result
| measure | value |
|---|---|
| icons evaluated | 339 (primary/validation) |
| model pixel error, mean [95% CI] | 0.0697 [0.0648, 0.0747] |
| model pixel error, median | 0.0635 |
| exact program rate | 0.009 |
| rendered rate | 1.000 |
| blank canvas pixel error | 0.1720 |
| nearest training icon pixel error | 0.0897 [0.0848, 0.0947] |
| error reduction vs nearest icon | 0.0201 [0.0158, 0.0243] |
| icons where model beats nearest icon | 256 |
| CLIP top-1, model greedy (32 Gate N icons) | 0.656 |
| CLIP top-1, nearest training icon | 0.344 |
| CLIP top-1, OmniSVG 4B zero-shot (Gate N) | 0.609 |
Resources
| measure | value |
|---|---|
| device | NVIDIA B300 SXM6 AC |
| parameters | 9,025,826 |
| train seconds | 2627 |
| peak VRAM GiB | 4.388660430908203 |
| latency ms/icon, batch 1, median | 2154.8 |
| latency ms/icon, batch 1, p95 | 2158.9 |
| throughput ms/icon, batch 64 | 69.0 |
| model calls per icon (decoding) | 752.0 |
| torch / CUDA | 2.14.1+cu130 / 13.0 |
Latency covers encoding and decoding to a validated token program; it excludes rasterising the SVG. Samples: samples.png, rows are reference, model, nearest training icon.
State transitions
- running2026-10-30T18:15:02Z
- completed2026-10-30T19:01:22Z
Run record
Verbatim from runs/r2s-cur-d025-9a606d4-30b17847-47646604/run.yaml, the record committed before launch.
schema_version
2
run_id
r2s-cur-d025-9a606d4-30b17847-47646604
state
completed
planned_at
2026-10-30T18:15:02Z
hypothesis
With dropout 0.25 the model transcribes validation renders better than v9 (dose between 0.2 and 0.3). Pass requires the paired 95% bootstrap interval on the per-icon reduction in mean 72 px pixel error on the 339 validation renders to exclude zero against each normal v9 run (RTX 4080 original, H100 rerun, B300 seeds 2 and 3) and, for depth arms, a direction consistent across seeds against dropout 0.2 at 6 layers. Its greedy latency on the RTX 4080 is recorded.
parent_run
r2s-cur-dropout02
git_commit
9a606d47e8a674cddbe38159cae29eb99fd081b0
dirty_patch_sha256
config
configs/render2svg/cur-d025.yaml
config_sha256
30b178470e855bac…config_resolved
model
image_size
144
d_model
256
heads
8
encoder_layers
2
decoder_layers
6
feedforward
1024
dropout
0.25
metric
true
fourier
10
order
path
sources
0
training
steps
60000
batch_size
32
learning_rate
0.0005
weight_decay
0.01
warmup_steps
1000
seed
7001
eval_every
2000
eval_icons
64
train_icons
None
evaluate_on_train
false
augment_variants
0
augment_seed
9001
augment_mirror
0.5
augment_max_shift
48
augment_colour
0.1
augment_online
true
compose_probability
0.5
compose_parts
4
augment_original
0.1
loader_workers
16
extra_training
bf16
true
final_eval_icons
339
clip_icons
32
dataset
pilot_config
configs/learning/openmoji-g1-geometric-gate-v16.yaml
cache_sha256
476466042da98d72…train_icons
2681
augmented_variants
0
extra_training
None
augmented_sha256
None
evaluated_on
primary/validation
seed
7001
determinism
seeded; cuDNN and SDPA kernels not forced deterministic
model_parameters
9025826
command
.venv/bin/python -m mojidiff.learning.render2svg --config configs/render2svg/cur-d025.yaml
outputs
run_dir
runs/r2s-cur-d025-9a606d4-30b17847-47646604
checkpoints
/home/eai-jdkxz/eai-team11/mojidiff/cache/runs/r2s-cur-d025-9a606d4-30b17847-47646604
notes
completed_at
2026-10-30T19:01:22Z
resource
device
NVIDIA B300 SXM6 AC
torch_version
2.14.1+cu130
cuda_version
13.0
python_version
3.12.3
peak_vram_gib
4.388660430908203
train_seconds
2627.054362212948
inference_ms_per_icon
2154.8321500013117
inference_p95_ms_per_icon
2158.925377007108
icons_per_second
0.4640732690012033
batched_ms_per_icon
68.96088382836751
batched_icons_per_call
64
excludes
rendering the output SVG to pixels
baselines
blank_canvas_pixel_error
0.1720480552069557
nearest_training_icon_pixel_error
0.08973124639576185
gate_n_omnisvg_zero_shot_top1
0.609375
nearest_training_icon_clip_top1
0.34375
result
icons
339
model_pixel_error
0.06967784575030032, 0.06478462569784958, 0.07466130230999178
model_pixel_error_median
0.06348467618227005
exact_program_rate
0.008849557522123894
rendered_rate
1.0
blank_pixel_error
0.1720480552069557
selected_step
60000
nearest_training_icon_pixel_error
0.08973124639576185, 0.0848205749792233, 0.09466393787711656
model_minus_baseline_error_reduction
0.020053400645461527, 0.015784209976164438, 0.024345050805296743
icons_model_beats_baseline
256
clip_retrieval
model_greedy
top1_rate
0.65625
top5_rate
0.9375
mean_rank
2.03125
clip_to_reference_mean
0.9229987002909184
chance_top1
0.03125
icons
32
nearest_training_icon
top1_rate
0.34375
top5_rate
0.59375
mean_rank
7.40625
clip_to_reference_mean
0.8630617223680019
chance_top1
0.03125
icons
32
gate_n_omnisvg_zero_shot_top1
0.609375
gate_n_omnisvg_best_of_12_top1
0.828125
model_calls_per_icon
752