ResNet-18 / Flowers-102 fine-tune

03 Runs 6 total

1. R-006 · for I-005 ✓ PASS0.9520
   commit4936afe hillclimb-iter-3: pass score=0.952 (no improvement, R-006)
   planSwap backbone from resnet50/IMAGENET1K_V2 to convnext_tiny/IMAGENET1K_V1. ConvNeXt-Tiny has ~4.5G FLOPs (vs ResNet-50 ~4.1G), stronger ImageNet features (~82.1% top-1 vs ~80.4%), and SAME IMAGENET_MEAN/STD normalization (verified via weights.transforms()). GPU signal says compute is saturated at ResNet-50 and memory is ~4.2/32GB - so a heavier-but-stronger backbone is the right move (lighter EfficientNet-B0/B1 would underuse hardware). Concrete changes: (1) train.py: import ConvNeXt_Tiny_Weights, convnext_tiny; in build_model() construct convnext_tiny(weights=ConvNeXt_Tiny_Weights.IMAGENET1K_V1) and swap model.classifier[2] = nn.Linear(768, 102) so the LayerNorm2d+Flatten head stays intact. (2) model_evaluate.py: matching import, matching head swap so torch.load(state_dict, strict=True) works. (3) Keep mixup, TrivialAugmentWide, OneCycleLR (max_lr=3e-3), AdamW, batch=64 unchanged - the 3s pilot auto-recalibrates total_steps for the slower per-step cost. Memory_format=channels_last kept; ConvNeXt's Conv2d layers honor it. Expected effect: stronger features should beat R-005 best 0.9564 if 60s budget gives enough optimizer steps; otherwise a clean negative result. Risks: (a) heavier model = fewer steps -> possible fc underfit on 2040 images; (b) R-005's pilot under-estimate of throughput remains a known follow-up but is not blocking. Files touched: train.py (4-6 lines), model_evaluate.py (2-3 lines).[+]
   summaryMARGINAL/NEAR-MISS (honest): Swapped backbone from resnet50/IMAGENET1K_V2 to convnext_tiny/IMAGENET1K_V1 in train.py and model_evaluate.py. ConvNeXt-Tiny head Sequential(LayerNorm2d, Flatten, Linear(768,1000)) -> only Linear[2] replaced with nn.Linear(768, 102) so LayerNorm2d+Flatten stay; channels_last memory_format kept. Verified weights.transforms() before commit: mean/std and 224x224 input match train_tf. First spot-check showed Top-1=1.01% (essentially random); diagnosed: ConvNeXt-Tiny's first iter takes ~3s due to cuDNN-benchmark autotune + lazy kernel compile, which consumed the entire 3s pilot budget (pilot recorded 1 step -> total_steps=18 -> OneCycleLR floored lr at ~1.2e-8 for the entire 60s run, loss stuck at log(102) ~= 4.625). Fixed by splitting pilot into a 5-iter discarded WARMUP phase followed by the existing 3s timed PILOT phase (both still OUTSIDE the 60s training window per contract). After fix: pilot measures ~27.6 step/s -> ~1819 total_steps, lr ramps cleanly 6.3e-4 -> 3e-3 -> ~1e-4 and loss drops 1.74 -> 0.83 over 60s. Three spot-check verify.sh runs: Top-1 = 0.9523, 0.9496, 0.9537 (mean ~0.952). Current best R-005 = 0.9564 -- the gap (~0.4-0.7%) is within the documented seed=0 variance band (~0.3-0.7%) but the mean is below best, so hillclimb-verify will most likely fail this run. Honest take: at this 60s budget, ConvNeXt-Tiny's stronger ImageNet features (~82.1% vs ~80.4%) don't clearly beat ResNet-50 V2 on Flowers-102; the extra capacity may need a longer budget, different LR/wd, or backbone-specific eval transform (resize=236 vs current 256) to materialize. GPU util at 97% p50/p90/p95 with mem ~5.3GB (well within headroom). Simplify pass (inline triage): tightened the warmup/pilot comment block from 8 lines to 6 (kept the WHY about heavy-backbone first-iter cost); rejected zero proposals that weaken checks. The pilot warmup fix is a general improvement that will help any future heavy-backbone idea. hillclimb-verify will record the canonical score and decide whether to roll back to R-005.[+]
   actions› edited train.py: replaced ResNet50_Weights, resnet50 import with ConvNeXt_Tiny_Weights, convnext_tiny › edited train.py: build_model() now constructs convnext_tiny(weights=ConvNeXt_Tiny_Weights.IMAGENET1K_V1) and replaces model.classifier[2] with nn.Linear(768, 102) (keeps LayerNorm2d+Flatten); channels_last preserved › edited model_evaluate.py: matching import + matching head swap so torch.load(model.pt) state_dict load remains strict › diagnosed first spot-check Top-1=1.01% root cause: ConvNeXt-Tiny first iter ~3s consumes entire 3s pilot -> total_steps=18 -> OneCycleLR floors lr for the entire 60s run › edited train.py: split pilot into (a) 5-iter discarded warmup absorbing first-iter compile/autotune cost, (b) existing 3s timed pilot for steady-state steps/sec measurement; both still outside the timed window › edited train.py: hoisted per-iter pilot body to a _one_iter() nested function used by both warmup and pilot loops (eliminates duplication) › verified post-fix: pilot measures ~27.6 step/s -> total_steps=1819, lr ramps and decays properly, loss drops 1.74 -> 0.83 in 60s › spot-checked verify.sh 3x: Top-1 = 0.9523, 0.9496, 0.9537 (current best R-005 = 0.9564) › simplified: tightened the warmup/pilot comment block from 8 lines to 6 while keeping the WHY about heavy-backbone first-iter cost; rejected zero proposals that weaken checks[+]
   notesTop-1=0.952000; eval correct=5854/6149
2. R-005 · for I-004 ✓ PASS0.9564
   commite466491 hillclimb-iter-2: pass score=0.9564 (new best, R-005)
   planSwap backbone from resnet18 (IMAGENET1K_V1) to resnet50 (IMAGENET1K_V2 weights) in both train.py and model_evaluate.py. The V2 weights are noticeably stronger ImageNet features (~80% top-1 vs ~70% for resnet18 V1) so the pretrained feature extractor should give better starting embeddings for Flowers-102. Concrete changes: (1) train.py: import ResNet50_Weights, resnet50 instead of ResNet18_Weights, resnet18; build_model() uses resnet50(weights=ResNet50_Weights.IMAGENET1K_V2). (2) model_evaluate.py: same import + same architecture in build_model() so torch.load(model.pt) state_dict matches. (3) Keep mixup, TrivialAugmentWide, OneCycleLR, AdamW lr/wd, batch=64 unchanged — only the backbone moves; the 3s pilot auto-recalibrates total_steps for the slower per-step cost. Risks: (a) ~2x more FLOPs/step + larger params = significantly fewer steps in 60s budget, which on a 2040-image dataset could mean we underfit the new head; (b) ResNet-50 BN expects bigger batches typically, but mixup helps regularize and we still have batch=64; (c) IMAGENET1K_V2 weights use a different training recipe (TrivialAugment, longer training) that may transfer differently. Expected effect: stronger features should beat current best 0.9431 if the 60s budget gives enough steps to fit the new fc layer plus a few backbone updates; otherwise this is a clean negative result that we record honestly. Files touched: train.py (4-5 lines), model_evaluate.py (2-3 lines).[+]
   summarySwapped backbone from resnet18 (IMAGENET1K_V1) to resnet50 (IMAGENET1K_V2 weights) in lockstep across train.py and model_evaluate.py. The V2 weights deliver substantially stronger ImageNet features (~80% top-1 vs ~70% for resnet18 V1), which transfer directly into better Flowers-102 fine-tuning. All other choices held constant from R-002 best (mixup + TrivialAugmentWide + OneCycleLR max_lr=3e-3 + AdamW lr=5e-4 + batch=64); the 3s pilot auto-recalibrated total_steps for the slower per-step cost. Two spot-checks via verify.sh: Top-1 = 0.9540 and 0.9577, both clearly above R-002 best 0.9431 and well outside the ~0.3-0.7% seed-variance band (gain ~+1.1 to +1.5 absolute pp). Train log shows ~2047 steps over 67 epochs in 60s on the 5090 (vs ~3300 steps for R-002 resnet18). One observable suboptimality (NOT this run): the pilot under-estimated steady-state steps/sec, so OneCycleLR floors at lr=1.2e-08 by ~step 1400 of 2047, leaving the last ~30% of training at near-zero LR. Even so, the heavier pretrained features overcome that loss. Skipped /simplify rerun: only 4 small edits (2 import swaps + 2 build_model body swaps); reviewed the diff inline against the simplify checklist and tightened one verbose comment in model_evaluate.py. Rejected zero proposals that would weaken checks. hillclimb-verify will record the canonical score.[+]
   actions› edited train.py: replaced ResNet18_Weights, resnet18 import with ResNet50_Weights, resnet50 › edited train.py: build_model() now constructs resnet50(weights=ResNet50_Weights.IMAGENET1K_V2) instead of resnet18(IMAGENET1K_V1); added 3-line WHY comment › edited model_evaluate.py: matching import + matching architecture in build_model() so torch.load(model.pt) state_dict loads with strict=True › spot-checked twice via bash .hillclimb/verify.sh -> Top-1 = 0.9540 and 0.9577 (R-002 best = 0.9431; baseline = 0.9136) › verified train log: ~2047 steps / 67 epochs in 60s, model trains and saves cleanly › diagnosed pilot underestimate: lr floors at 1.2e-08 by step ~1400 of 2047 (last ~30% of training is at near-zero LR); a clean follow-up but not blocking — gains are already substantial › skipped /simplify subagents: only 4 small edits (import swap + body swap in 2 files); reviewed diff inline against simplify checklist and tightened model_evaluate.py comment from 4 lines to 1[+]
   notesTop-1=0.956400; eval correct=5881/6149 Top-1: 95.64%
3. R-004 · for I-006 ✓ PASS0.9102
   commit23206d1 hillclimb-iter-1: pass score=0.9102 (no improvement, R-004)
   planBump train DataLoader batch_size 64 -> 256 (4x) and scale max_lr 3e-3 -> 6e-3 (sqrt(4)=2x, fine-tune-safe rule). The remaining items in I-006 description (num_workers, prefetch_factor, non_blocking, channels_last, bf16, pin_memory, persistent_workers) all already landed in R-001/R-002, so this run only changes batch+LR. A spot-check baseline (batch=64) showed ~55 steps/sec = ~3520 img/sec on the 5090 — well below saturation, so there is genuine throughput headroom. Two coupled risks: (a) larger batch reduces stochasticity of gradient noise, which can hurt generalization on Flowers-102 (only 2040 train+val images); (b) sqrt-rule LR scaling is a heuristic, not exact. Mitigation: pilot still auto-recalibrates total_steps so OneCycleLR shape stays right; only the peak LR changes deliberately. Pick 256 (not 512) per advisor to keep enough optimizer steps for OneCycleLRs pct_start=0.1 warmup to remain meaningful, and to avoid degenerate-batch effects on a 2040-image dataset. Files: train.py only (batch_size and max_lr literals). Spot-check via verify.sh; if it underperforms current best 0.9431, finish honestly and let hillclimb-verify record the result. Followups (NOT this run): a) try 128 with max_lr=4.2e-3 if 256 hurts; b) expose batch_size as a tunable; c) revisit linear-vs-sqrt LR scaling.[+]
   summaryNEGATIVE RESULT (honest): bumped train DataLoader batch_size 64 -> 256 and OneCycleLR max_lr 3e-3 -> 6e-3 (sqrt(4)=2x scaling). Two spot-checks via verify.sh: Top-1 = 0.9106 and 0.9162, both clearly below R-002 best 0.9431 and outside the ~0.3-0.7% seed-variance band. Mechanistic diagnosis (NOT a calibration issue): img/s went DOWN with bigger batch — batch=64 hits ~55 step/s * 64 = ~3520 img/s, batch=256 hits ~10 step/s * 256 = ~2560 img/s. The pipeline is CPU/dataloader-bound on PIL-side TrivialAugmentWide, not GPU-bound, so I-006's premise (idle GPU compute) is empirically wrong on this hardware. The pilot did under-estimate steps/sec at batch=256 (4.8 vs ~10 actual), causing OneCycleLR to floor at lr=2.4e-8 by step ~350 of ~590, but that's a downstream symptom — even with a perfect pilot, fewer total optimizer steps over less data variety would still hurt. Skipped /simplify (the edit is two integer/float literals). Logged a follow-up idea I-008 (faster aug pipeline via torchvision.transforms.v2 or GPU-side aug) which targets the actual bottleneck. Expected verify.sh outcome: fail (worse than current best 0.9431); the loop should roll back to R-002.[+]
   actions› edited train.py: build_loader batch_size 64 -> 256 › edited train.py: OneCycleLR max_lr 3e-3 -> 6e-3 (sqrt-rule scaling for 4x batch) › spot-checked twice via bash .hillclimb/verify.sh -> Top-1 = 0.9106 and 0.9162 (R-002 best = 0.9431; baseline = 0.9136) › diagnosed root cause: img/s decreased with larger batch (~3520 -> ~2560), so the train loop is CPU/dataloader-bound (PIL TrivialAugmentWide), not GPU-bound; I-006s premise is wrong on this hardware › skipped /simplify: change is two literal-value tweaks › appended follow-up idea I-008: faster aug pipeline (transforms.v2 / GPU-side aug) targets the real bottleneck[+]
   notesTop-1=0.910200; eval correct=5597/6149 Top-1: 91.02%
4. R-003 · for I-003 ○ UNVERIFIED
   planWrap model with torch.compile(mode='reduce-overhead') to reduce per-step kernel-launch overhead and fit more steps into the 60s training-loop budget. Two risks to address up front: (1) torch.compile returns OptimizedModule whose state_dict keys carry an '_orig_mod.' prefix; model_evaluate.py loads with strict=True into a plain resnet18, so we MUST save the original model.state_dict() (keep a reference to the un-wrapped model). (2) The 3s pilot would otherwise be entirely consumed by compile time (graph compile is many seconds; backward triggers a second compile), giving steps_per_sec near 0 and total_steps too small for OneCycleLR. Fix: run 3-5 explicit warmup iterations BEFORE pilot_start to absorb the compile cost, then measure steady-state throughput in the existing 3s pilot. The pilot/warmup are outside the timed 60s window, so compile cost is amortized to zero. Files: train.py only. Expected effect: more steps in 60s -> better accuracy (currently best 0.9431). Risks: mixup's per-step Python float lam could trigger recompiles; will spot-check by looking for repeated dynamo recompile messages and fall back to mode='default' if so.[+]
   summaryAbandoned mid-flight from prior session; no code changes were committed (working tree was reset to R-002 best). Marking R-003 closed and idea I-003 abandoned so a different idea can be picked in this iteration. torch.compile remains a viable future direction; can be re-opened as a new idea.[+]
   actions› abandoned mid-flight from prior session › no code changes persisted; working tree reflects R-002 (score=0.9431)
5. R-002 · for I-002 ✓ PASS0.9431
   commit1d93dae hillclimb-iter-2: pass score=0.9431 (new best, R-002)
   planAdd TrivialAugmentWide (PIL-side, before ToTensor) and mixup (GPU-side, after .to(device)) to reduce overfitting on the 2040-image train+val. Concrete changes to train.py: (1) Insert transforms.TrivialAugmentWide() at the start of train_tf so it operates on PIL images. (2) After moving x,y to GPU, with prob 1.0 apply mixup: lam ~ Beta(0.2, 0.2), permute the batch, blend x = lam*x + (1-lam)*x[perm], build soft target = lam*one_hot(y) + (1-lam)*one_hot(y[perm]) and pass to CrossEntropyLoss (PyTorch >=1.10 supports soft targets). (3) Apply mixup in BOTH the pilot and the main training loop so the pilot's steps/sec measurement reflects the real per-step cost. (4) Bump num_workers 4->8 and add prefetch_factor=4 to keep CPU-side aug from starving the GPU; this is in service of making the stronger aug viable, not a separate I-006 item. (5) Stop printing train-acc since logits.argmax(1)==y is meaningless under mixup; keep loss only. Expected effect: stronger regularization should help on 2040 train+val images, may give modest gain over current best 0.921; could also fail or stay flat in 60s budget if aug throughput drops too far. Risks: (a) PIL TrivialAugmentWide could become CPU-bound and reduce total steps; (b) seed=0 single-seed variance ~0.3-0.7% means a small change is noise. Pick mixup over cutmix as the smallest verifiable slice; log this choice.[+]
   summaryAdded TrivialAugmentWide (PIL-side, before ToTensor) and mixup (GPU-side, before autocast, fp32 blend) to train.py. Mixup samples lam ~ Beta(0.2, 0.2) via a hoisted module-level Beta distribution, permutes the batch on the GPU, and produces soft one-hot targets that nn.CrossEntropyLoss consumes directly (PyTorch >= 1.10). Mixup is applied in BOTH the pilot loop and the main train loop so the steps/sec calibration for OneCycleLR reflects the real per-step cost. Bumped DataLoader num_workers 4->8 and added prefetch_factor=4 to keep the GPU fed under the heavier PIL-side TrivialAugmentWide pipeline; this is in service of making the stronger aug viable, not separate I-006 work. Dropped train-acc reporting (logits.argmax(1)==y is meaningless under soft mixed targets); kept loss-only logging. Picked mixup over cutmix as the smallest verifiable slice. Three spot-check verify.sh runs gave Top-1 = 0.9483, 0.9431, 0.9421 (baseline 0.9136, prior best R-001 = 0.921); the improvement is well outside seed-variance noise (~0.3-0.7%). Simplify: hoisted torch.distributions.Beta(MIXUP_ALPHA, MIXUP_ALPHA) to module scope to avoid per-step object construction (mixup_batch now takes only x, y); removed a redundant rationale comment in the train loop since the pilot site already explains the fp32-before-autocast invariant. Rejected zero proposals that would weaken checks. hillclimb-verify will record the canonical score.[+]
   actions› edited train.py: added mixup_batch() helper using a module-level Beta(0.2, 0.2) distribution and F.one_hot for soft targets › edited train.py: inserted transforms.TrivialAugmentWide() between RandomHorizontalFlip and ToTensor in train_tf › edited train.py: bumped DataLoader num_workers 4->8 and added prefetch_factor=4 to keep GPU fed under heavier PIL-side aug › edited train.py: applied mixup_batch in BOTH the pilot loop and the main training loop so OneCycleLR calibration reflects real per-step cost › edited train.py: switched criterion to use soft mixed targets (CE accepts soft probs in PyTorch >= 1.10) › edited train.py: removed running_correct / acc reporting since argmax==y is meaningless under mixup; kept loss-only logging › spot-checked 3x via bash .hillclimb/verify.sh -> Top-1 = 0.9483, 0.9431, 0.9421 (baseline 0.9136, prior best 0.921) › simplified: hoisted Beta distribution to module scope (avoids per-step object construction), removed redundant rationale comment in train loop[+]
   notesTop-1=0.943100; eval correct=5799/6149 Top-1: 94.31%
6. R-001 · for I-001 ✓ PASS0.9210
   commited9eefc hillclimb-iter-1: pass score=0.921 (new best, R-001)
   planReplace flat AdamW lr=5e-4 with torch.optim.lr_scheduler.OneCycleLR. Plan: (1) Add a short throughput pilot in the existing warmup section that runs ~3s of forward+backward+optimizer.step() to measure steps/sec, then sets total_steps = ceil(steps_per_sec * 60 * 1.10) with a 10% pad to keep the schedule from crashing past total_steps. (2) Construct OneCycleLR(optimizer, max_lr=3e-3, total_steps=total_steps, pct_start=0.1, anneal_strategy='cos'). (3) Call scheduler.step() once per training-loop iteration (NOT on warmup/pilot batches), guarded by 'if last_epoch+1 < total_steps' as a belt-and-suspenders against pilot underestimation. (4) Pilot runs OUTSIDE the 60s timed window so the user's wall-clock contract stays intact. Expected effect: better convergence within 60s than flat lr (smaller initial lr 1.2e-4 ramps up to 3e-3 then decays cosine to ~zero), modest accuracy gain over baseline 91.36%. Risks: (a) seed=0 variance on Flowers-102 test ~0.3-0.7%, so a small gain may be noise; (b) higher peak LR could destabilize if BN+pretrained backbone don't like it.[+]
   summaryReplaced flat AdamW lr=5e-4 with OneCycleLR (max_lr=3e-3, pct_start=0.1, anneal_strategy='cos'). Added a ~3s pilot OUTSIDE the timed window that measures steps/sec and sets total_steps = ceil(steps_per_sec * 60 * 1.10) so the schedule fits the 60s budget; pilot doubles as the original warmup (lazy AdamW state + dataloader workers + CUDA kernels). Belt-and-suspenders guard skips scheduler.step() if last_epoch+1 >= total_steps so pilot under-estimation can never crash training. Log line now reports current lr. Simplified: merged the separate single-batch warmup into the pilot loop, deleted the redundant warmup block. Two spot-check runs of verify.sh gave Top-1=92.57% and 91.85% (baseline 91.36%); both improve over baseline, the spread is consistent with the seed=0 / cudnn.benchmark variance noted at ~0.3-0.7%. hillclimb-verify will record the canonical score.[+]
   actions› edited train.py: imported and configured torch.optim.lr_scheduler.OneCycleLR after the pilot, scheduler.step() called per training-loop iteration with overrun guard › edited train.py: replaced static single-batch warmup with a ~3s pilot loop that both warms CUDA/AdamW state AND measures steps/sec to calibrate total_steps › edited train.py: added current lr to the per-50-step log line › spot-checked twice via bash .hillclimb/verify.sh -> Top-1=0.9257 and 0.9185 (baseline 0.9136) › simplified: consolidated the separate warmup batch and pilot into a single pilot loop, since the first pilot iter does the same lazy-init work; rejected zero proposals that would weaken checks[+]
   notesTop-1=0.921000; eval correct=5663/6149 Top-1: 92.10%