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feat: archive best scoliosis checkpoints

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crosstyan
2026-03-11 10:23:38 +08:00
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artifact/**/*.pt filter=lfs diff=lfs merge=lfs -text
artifact/scoliosis_sconet_112_bodyonly_plaince_adamw_cosine/README.md
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# Scoliosis ScoNet-MT-ske Artifacts
This directory archives the two retained best checkpoints from the practical
`1:1:2` skeleton-map training path:
- best by macro-F1
- best by accuracy
Model recipe:
- split: `Scoliosis1K_112.json`
- representation: `body-only` skeleton heatmap
- losses: plain CE + triplet
- finetune optimizer: `AdamW`
- scheduler: cosine annealing
Files:
- `*_scalar_test_f1.pt`: retained best checkpoint by macro-F1
- `*_scalar_test_accuracy.pt`: retained best checkpoint by accuracy
- `train_config.yaml`: training config for the finetune run
- `eval_best_f1_27000.yaml`: standalone eval config for the retained best-F1 checkpoint
- `eval_best_accuracy_64000.yaml`: standalone eval config for the retained best-accuracy checkpoint
## Reproduce eval
Run from the repo root.
Best-F1 checkpoint:
```bash
CUDA_VISIBLE_DEVICES=GPU-9cc7b26e-90d4-0c49-4d4c-060e528ffba6 \
uv run python -m torch.distributed.run --nproc_per_node=1 \
opengait/main.py \
--cfgs artifact/scoliosis_sconet_112_bodyonly_plaince_adamw_cosine/eval_best_f1_27000.yaml \
--phase test
```
Best-accuracy checkpoint:
```bash
CUDA_VISIBLE_DEVICES=GPU-9cc7b26e-90d4-0c49-4d4c-060e528ffba6 \
uv run python -m torch.distributed.run --nproc_per_node=1 \
opengait/main.py \
--cfgs artifact/scoliosis_sconet_112_bodyonly_plaince_adamw_cosine/eval_best_accuracy_64000.yaml \
--phase test
```
Paths to change on another machine:
- `data_cfg.dataset_root`
- point this to your local body-only Scoliosis1K skeleton dataset root
- `data_cfg.dataset_partition`
- change this only if your repo or partition file lives elsewhere
- `evaluator_cfg.restore_hint`
- change this only if you move the `artifact/` folder or rename the checkpoint files
- `evaluator_cfg.output_root`
- change this if you want eval outputs somewhere else
## Verified results
These commands were rerun from the copied `artifact/` configs on `2026-03-11`.
Best-F1 checkpoint (`27000`):
- Accuracy: `92.38%`
- Macro Precision: `90.30%`
- Macro Recall: `87.39%`
- Macro F1: `88.70%`
Best-accuracy checkpoint (`64000`):
- Accuracy: `94.25%`
- Macro Precision: `83.24%`
- Macro Recall: `95.76%`
- Macro F1: `87.63%`
artifact/scoliosis_sconet_112_bodyonly_plaince_adamw_cosine/eval_best_accuracy_64000.yaml
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data_cfg:
dataset_name: Scoliosis1K
dataset_root: /mnt/public/data/Scoliosis1K/Scoliosis1K-drf-pkl-118-sigma15-joint8-bodyonly
dataset_partition: ./datasets/Scoliosis1K/Scoliosis1K_112.json
data_in_use:
- true
- false
num_workers: 1
remove_no_gallery: false
test_dataset_name: Scoliosis1K
evaluator_cfg:
enable_float16: true
restore_ckpt_strict: true
restore_hint: ./artifact/scoliosis_sconet_112_bodyonly_plaince_adamw_cosine/ScoNet_skeleton_112_sigma15_joint8_bodyonly_plaince_adamw_cosine_finetune_1gpu_80k-iter-64000-score-0.9425-scalar_test_accuracy.pt
save_name: ScoNet_skeleton_112_sigma15_joint8_bodyonly_plaince_adamw_cosine_eval_64000_1gpu
output_root: ./artifact/scoliosis_sconet_112_bodyonly_plaince_adamw_cosine/eval-output
eval_func: evaluate_scoliosis
sampler:
batch_shuffle: false
batch_size: 1
sample_type: all_ordered
type: InferenceSampler
frames_all_limit: 720
metric: euc
transform:
- type: BaseSilCuttingTransform
model_cfg:
model: ScoNet
backbone_cfg:
type: ResNet9
block: BasicBlock
in_channel: 2
channels:
- 64
- 128
- 256
- 512
layers:
- 1
- 1
- 1
- 1
strides:
- 1
- 2
- 2
- 1
maxpool: false
SeparateFCs:
in_channels: 512
out_channels: 256
parts_num: 16
SeparateBNNecks:
class_num: 3
in_channels: 256
parts_num: 16
bin_num:
- 16
artifact/scoliosis_sconet_112_bodyonly_plaince_adamw_cosine/eval_best_f1_27000.yaml
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data_cfg:
dataset_name: Scoliosis1K
dataset_root: /mnt/public/data/Scoliosis1K/Scoliosis1K-drf-pkl-118-sigma15-joint8-bodyonly
dataset_partition: ./datasets/Scoliosis1K/Scoliosis1K_112.json
data_in_use:
- true
- false
num_workers: 1
remove_no_gallery: false
test_dataset_name: Scoliosis1K
evaluator_cfg:
enable_float16: true
restore_ckpt_strict: true
restore_hint: ./artifact/scoliosis_sconet_112_bodyonly_plaince_adamw_cosine/ScoNet_skeleton_112_sigma15_joint8_bodyonly_plaince_adamw_cosine_finetune_1gpu_80k-iter-27000-score-0.8870-scalar_test_f1.pt
save_name: ScoNet_skeleton_112_sigma15_joint8_bodyonly_plaince_adamw_cosine_eval_27000_1gpu
output_root: ./artifact/scoliosis_sconet_112_bodyonly_plaince_adamw_cosine/eval-output
eval_func: evaluate_scoliosis
sampler:
batch_shuffle: false
batch_size: 1
sample_type: all_ordered
type: InferenceSampler
frames_all_limit: 720
metric: euc
transform:
- type: BaseSilCuttingTransform
model_cfg:
model: ScoNet
backbone_cfg:
type: ResNet9
block: BasicBlock
in_channel: 2
channels:
- 64
- 128
- 256
- 512
layers:
- 1
- 1
- 1
- 1
strides:
- 1
- 2
- 2
- 1
maxpool: false
SeparateFCs:
in_channels: 512
out_channels: 256
parts_num: 16
SeparateBNNecks:
class_num: 3
in_channels: 256
parts_num: 16
bin_num:
- 16
artifact/scoliosis_sconet_112_bodyonly_plaince_adamw_cosine/train_config.yaml
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data_cfg:
dataset_name: Scoliosis1K
dataset_root: /mnt/public/data/Scoliosis1K/Scoliosis1K-drf-pkl-118-sigma15-joint8-bodyonly
dataset_partition: ./datasets/Scoliosis1K/Scoliosis1K_112.json
data_in_use:
- true
- false
num_workers: 1
remove_no_gallery: false
test_dataset_name: Scoliosis1K
evaluator_cfg:
enable_float16: true
restore_ckpt_strict: true
restore_hint: 80000
save_name: ScoNet_skeleton_112_sigma15_joint8_bodyonly_plaince_adamw_cosine_finetune_1gpu_80k
output_root: /mnt/hddl/data/OpenGait-output
eval_func: evaluate_scoliosis
sampler:
batch_shuffle: false
batch_size: 1
sample_type: all_ordered
type: InferenceSampler
frames_all_limit: 720
metric: euc
transform:
- type: BaseSilCuttingTransform
loss_cfg:
- loss_term_weight: 1.0
margin: 0.2
type: TripletLoss
log_prefix: triplet
- loss_term_weight: 1.0
scale: 16
type: CrossEntropyLoss
log_prefix: softmax
log_accuracy: true
model_cfg:
model: ScoNet
backbone_cfg:
type: ResNet9
block: BasicBlock
in_channel: 2
channels:
- 64
- 128
- 256
- 512
layers:
- 1
- 1
- 1
- 1
strides:
- 1
- 2
- 2
- 1
maxpool: false
SeparateFCs:
in_channels: 512
out_channels: 256
parts_num: 16
SeparateBNNecks:
class_num: 3
in_channels: 256
parts_num: 16
bin_num:
- 16
optimizer_cfg:
lr: 0.0005
solver: AdamW
weight_decay: 0.0005
scheduler_cfg:
scheduler: CosineAnnealingLR
T_max: 60000
eta_min: 0.00001
trainer_cfg:
enable_float16: true
fix_BN: false
with_test: true
log_iter: 100
restore_ckpt_strict: true
optimizer_reset: false
scheduler_reset: false
restore_hint: /mnt/hddl/data/OpenGait-output/Scoliosis1K/ScoNet/ScoNet_skeleton_112_sigma15_joint8_bodyonly_plaince_adamw_cosine_finetune_1gpu_80k/checkpoints/latest.pt
output_root: /mnt/hddl/data/OpenGait-output
auto_resume_latest: true
resume_every_iter: 500
resume_keep: 6
best_ckpt_cfg:
keep_n: 3
metric_names:
- scalar/test_f1/
- scalar/test_accuracy/
eval_iter: 1000
save_iter: 500
save_name: ScoNet_skeleton_112_sigma15_joint8_bodyonly_plaince_adamw_cosine_finetune_1gpu_80k
sync_BN: false
total_iter: 80000
sampler:
batch_shuffle: true
batch_size:
- 8
- 8
frames_num_fixed: 30
sample_type: fixed_unordered
type: TripletSampler
transform:
- type: BaseSilCuttingTransform
configs/sconet/sconet_scoliosis1k_skeleton_112_sigma15_joint8_bodyonly_plaince_adamw_cosine_eval_27000_1gpu.yaml
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data_cfg:
dataset_name: Scoliosis1K
dataset_root: /mnt/public/data/Scoliosis1K/Scoliosis1K-drf-pkl-118-sigma15-joint8-bodyonly
dataset_partition: ./datasets/Scoliosis1K/Scoliosis1K_112.json
data_in_use:
- true
- false
num_workers: 1
remove_no_gallery: false
test_dataset_name: Scoliosis1K
evaluator_cfg:
enable_float16: true
restore_ckpt_strict: true
restore_hint: /mnt/hddl/data/OpenGait-output/Scoliosis1K/ScoNet/ScoNet_skeleton_112_sigma15_joint8_bodyonly_plaince_adamw_cosine_finetune_1gpu_80k/checkpoints/best/scalar_test_f1/ScoNet_skeleton_112_sigma15_joint8_bodyonly_plaince_adamw_cosine_finetune_1gpu_80k-iter-27000-score-0.8870-scalar_test_f1.pt
save_name: ScoNet_skeleton_112_sigma15_joint8_bodyonly_plaince_adamw_cosine_eval_27000_1gpu
output_root: /mnt/hddl/data/OpenGait-output
eval_func: evaluate_scoliosis
sampler:
batch_shuffle: false
batch_size: 1
sample_type: all_ordered
type: InferenceSampler
frames_all_limit: 720
metric: euc
transform:
- type: BaseSilCuttingTransform
model_cfg:
model: ScoNet
backbone_cfg:
type: ResNet9
block: BasicBlock
in_channel: 2
channels:
- 64
- 128
- 256
- 512
layers:
- 1
- 1
- 1
- 1
strides:
- 1
- 2
- 2
- 1
maxpool: false
SeparateFCs:
in_channels: 512
out_channels: 256
parts_num: 16
SeparateBNNecks:
class_num: 3
in_channels: 256
parts_num: 16
bin_num:
- 16
configs/sconet/sconet_scoliosis1k_skeleton_112_sigma15_joint8_bodyonly_plaince_adamw_cosine_eval_64000_1gpu.yaml
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@@ -0,0 +1,60 @@
data_cfg:
dataset_name: Scoliosis1K
dataset_root: /mnt/public/data/Scoliosis1K/Scoliosis1K-drf-pkl-118-sigma15-joint8-bodyonly
dataset_partition: ./datasets/Scoliosis1K/Scoliosis1K_112.json
data_in_use:
- true
- false
num_workers: 1
remove_no_gallery: false
test_dataset_name: Scoliosis1K
evaluator_cfg:
enable_float16: true
restore_ckpt_strict: true
restore_hint: /mnt/hddl/data/OpenGait-output/Scoliosis1K/ScoNet/ScoNet_skeleton_112_sigma15_joint8_bodyonly_plaince_adamw_cosine_finetune_1gpu_80k/checkpoints/best/scalar_test_accuracy/ScoNet_skeleton_112_sigma15_joint8_bodyonly_plaince_adamw_cosine_finetune_1gpu_80k-iter-64000-score-0.9425-scalar_test_accuracy.pt
save_name: ScoNet_skeleton_112_sigma15_joint8_bodyonly_plaince_adamw_cosine_eval_64000_1gpu
output_root: /mnt/hddl/data/OpenGait-output
eval_func: evaluate_scoliosis
sampler:
batch_shuffle: false
batch_size: 1
sample_type: all_ordered
type: InferenceSampler
frames_all_limit: 720
metric: euc
transform:
- type: BaseSilCuttingTransform
model_cfg:
model: ScoNet
backbone_cfg:
type: ResNet9
block: BasicBlock
in_channel: 2
channels:
- 64
- 128
- 256
- 512
layers:
- 1
- 1
- 1
- 1
strides:
- 1
- 2
- 2
- 1
maxpool: false
SeparateFCs:
in_channels: 512
out_channels: 256
parts_num: 16
SeparateBNNecks:
class_num: 3
in_channels: 256
parts_num: 16
bin_num:
- 16
docs/scoliosis_reproducibility_audit.md
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@@ -163,6 +163,9 @@ Conclusion:
- on `1:1:2`, `body-only + weighted CE` reached `81.82 Acc / 66.21 Prec / 88.50 Rec / 65.96 F1` on the full test set
- on the same split, `body-only + plain CE` improved that further to `83.16 Acc / 68.24 Prec / 80.02 Rec / 68.47 F1` at `7000`
- a later explicit rerun of the `body-only + plain CE` `7000` full-test eval reproduced that same `83.16 / 68.24 / 80.02 / 68.47` result
- a later `AdamW` cosine finetune from that same `10k` plain-CE checkpoint improved the practical result further:
- verified retained best checkpoint at `27000`: `92.38 Acc / 90.30 Prec / 87.39 Rec / 88.70 F1`
- final `80000` checkpoint still remained strong: `90.64 Acc / 72.87 Prec / 93.19 Rec / 75.74 F1`
- adding back limited head context via `head-lite` did not improve the full-test score; its `7000` checkpoint reached only `78.07 Acc / 65.42 Prec / 80.50 Rec / 62.08 F1`
- the first practical DRF bridge on the same `1:1:2` body-only recipe peaked early and still underperformed the plain skeleton baseline; its best retained `2000` checkpoint reached only `80.21 Acc / 58.92 Prec / 59.23 Rec / 57.84 F1` on the full test set
@@ -180,6 +183,7 @@ Conclusion:
- the `1:1:8` class ratio is not just a nuisance; it appears to be a major driver of the current skeleton/DRF failure mode
- on the easier `1:1:2` split, weighted CE is not currently the winning recipe; the best local full-test result so far came from plain CE
- `head-lite` may help the small fixed proxy subset, but that gain did not transfer to the full `TEST_SET`, so `body-only + plain CE` remains the best practical skeleton recipe
- once the practical `1:1:2` body-only plain-CE recipe was established, the branch still appeared underfit enough that optimizer/schedule mattered again. A later `AdamW` cosine finetune beat the earlier SGD bridge by a large margin at its retained best checkpoint, which means the earlier `83.16 / 68.47` result was a stable baseline but not the ceiling of this skeleton recipe
- DRF currently looks worse than the plain skeleton baseline not because the skeleton path is dead, but because the additional prior branch is not yet providing a selective or stable complement. The current local evidence points to three likely causes:
- the body-only skeleton baseline already captures most of the useful torso signal on `1:1:2`, so PAV may be largely redundant in this setting
- the current PGA/PAV path appears weakly selective in local diagnostics, so the prior is not clearly emphasizing a few clinically relevant parts
@@ -210,3 +214,17 @@ At the moment, this repo does not yet support:
- claiming a successful independent reproduction of the DRF paper’s quantitative results
- claiming that the paper’s skeleton-map preprocessing is fully specified
- treating the paper’s qualitative feature-response visualizations as reproduced
For practical model selection, the current conclusion is simpler:
- stop treating DRF as the default winner
- keep the practical mainline on `1:1:2`
- use the retained `body-only + plain CE` skeleton checkpoint family as the working solution
- the strongest verified practical checkpoint is the later `AdamW` cosine finetune checkpoint at `27000`, with:
- `92.38 Acc / 90.30 Prec / 87.39 Rec / 88.70 F1`
That means the remaining work is no longer broad reproduction debugging. It is mostly optional refinement:
- confirm whether `body-only` really beats `full-body` under the same successful training recipe
- optionally retry DRF only after the strong practical skeleton baseline is fixed
- package and use the retained best checkpoint rather than continuing to churn the whole search space
docs/scoliosis_training_change_log.md
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@@ -41,14 +41,15 @@ Use it for:
| 2026-03-10 | `ScoNet_skeleton_112_sigma15_joint8_bodyonly_plaince_main_1gpu_20k` | ScoNet-MT-ske mainline | `Scoliosis1K-drf-pkl-118-sigma15-joint8-bodyonly` + `Scoliosis1K_112.json` | Promoted the winning practical skeleton recipe to a longer `20k` run with full `TEST_SET` eval and checkpoint save every `1000`; no proxy subset, same plain CE + SGD setup | interrupted | superseded by the true-resume continuation below |
| 2026-03-10 | `ScoNet_skeleton_112_sigma15_joint8_bodyonly_plaince_resume_1gpu_20k` | ScoNet-MT-ske mainline | `Scoliosis1K-drf-pkl-118-sigma15-joint8-bodyonly` + `Scoliosis1K_112.json` | True continuation of the earlier plain-CE `1:1:2` `10k` bridge from its `latest.pt`, extended to `20k` with full `TEST_SET` eval and checkpoint save every `1000` | interrupted | superseded by the AdamW finetune branch below |
| 2026-03-10 | `ScoNet_skeleton_112_sigma15_joint8_bodyonly_plaince_adamw_finetune_1gpu_20k` | ScoNet-MT-ske finetune | `Scoliosis1K-drf-pkl-118-sigma15-joint8-bodyonly` + `Scoliosis1K_112.json` | AdamW finetune from the earlier plain-CE `1:1:2` `10k` checkpoint; restores model weights only, resets optimizer/scheduler state, keeps full `TEST_SET` eval and checkpoint save every `1000` | interrupted | superseded by the longer overnight 40k finetune below |
| 2026-03-10 | `ScoNet_skeleton_112_sigma15_joint8_bodyonly_plaince_adamw_finetune_1gpu_40k` | ScoNet-MT-ske finetune | `Scoliosis1K-drf-pkl-118-sigma15-joint8-bodyonly` + `Scoliosis1K_112.json` | Longer overnight AdamW finetune from the same `10k` plain-CE checkpoint; restores model weights only, resets optimizer/scheduler state, extends to `40000` total iterations with full `TEST_SET` eval every `1000` | running | pending |
| 2026-03-10 | `ScoNet_skeleton_112_sigma15_joint8_bodyonly_plaince_adamw_finetune_1gpu_40k` | ScoNet-MT-ske finetune | `Scoliosis1K-drf-pkl-118-sigma15-joint8-bodyonly` + `Scoliosis1K_112.json` | Longer overnight AdamW finetune from the same `10k` plain-CE checkpoint; restores model weights only, resets optimizer/scheduler state, extends to `40000` total iterations with full `TEST_SET` eval every `1000` | interrupted | superseded by the cosine `80k` finetune below |
| 2026-03-10 to 2026-03-11 | `ScoNet_skeleton_112_sigma15_joint8_bodyonly_plaince_adamw_cosine_finetune_1gpu_80k` | ScoNet-MT-ske finetune | `Scoliosis1K-drf-pkl-118-sigma15-joint8-bodyonly` + `Scoliosis1K_112.json` | Practical long-run finetune from the same `10k` plain-CE checkpoint, but switched to `AdamW` with cosine decay, HDD-backed `output_root`, `save_iter=500`, `eval_iter=1000`, and best-N checkpoint retention | complete | final `80000` eval: `90.64/72.87/93.19/75.74`; verified best retained full-test checkpoint at `27000`: `92.38/90.30/87.39/88.70` (Acc/Prec/Rec/F1) |
## Current best skeleton baseline
Current best `ScoNet-MT-ske`-style result:
- practical best on the easier `1:1:2` split:
- `ScoNet_skeleton_112_sigma15_joint8_bodyonly_plaince_bridge_1gpu_10k` at `7000`
- `83.16 Acc / 68.24 Prec / 80.02 Rec / 68.47 F1`
- `ScoNet_skeleton_112_sigma15_joint8_bodyonly_plaince_adamw_cosine_finetune_1gpu_80k` at retained best checkpoint `27000`
- verified standalone full-test eval: `92.38 Acc / 90.30 Prec / 87.39 Rec / 88.70 F1`
- best result retained on the harder `1:1:8` split:
- `ScoNet_skeleton_118_sigma15_joint8_sharedalign_2gpu_bs12x8`
- `50.47 Acc / 69.31 Prec / 54.58 Rec / 48.63 F1`
@@ -68,3 +69,4 @@ Current best `ScoNet-MT-ske`-style result:
- A later full-test rerun of the retained `body-only + plain CE` `7000` checkpoint reproduced the same `83.16 / 68.24 / 80.02 / 68.47` result exactly, so that number is now explicitly reconfirmed rather than just carried forward from the original run log.
- `Head-lite` looked stronger than `body-only` on the fixed 128-sequence proxy subset at `7000`, but it did not transfer to the full test set: `78.07 Acc / 65.42 Prec / 80.50 Rec / 62.08 F1`, which is clearly below the `body-only + plain CE` full-test result.
- The first practical DRF bridge on the winning `1:1:2` recipe did not beat the plain skeleton baseline. Its best retained checkpoint (`2000`) reached only `80.21 Acc / 58.92 Prec / 59.23 Rec / 57.84 F1` on the full test set, versus `83.16 / 68.24 / 80.02 / 68.47` for `body-only + plain CE` at `7000`. The working local interpretation is that the added PAV/PGA path is currently injecting a weak or noisy prior rather than a useful complementary signal.
- Extending the practical `1:1:2` body-only plain-CE baseline with an `AdamW` cosine finetune changed the picture substantially. The final `80000` checkpoint still evaluated well (`90.64 / 72.87 / 93.19 / 75.74`), but the real win was an earlier retained checkpoint: `27000` reproduced exactly at `92.38 Acc / 90.30 Prec / 87.39 Rec / 88.70 F1` on a standalone full-test rerun. So for this practical path, the best result is no longer the original SGD bridge checkpoint but the retained `AdamW` cosine finetune checkpoint.
docs/sconet-drf-status-and-training.md
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@@ -71,6 +71,7 @@ The main findings so far are:
- `body-only + weighted CE` reached `81.82%` accuracy and `65.96%` macro-F1 on the full test set at `7000`
- `body-only + plain CE` improved that to `83.16%` accuracy and `68.47%` macro-F1 at `7000`
- a later full-test rerun confirmed the `body-only + plain CE` `7000` result exactly
- an `AdamW` cosine finetune from that same plain-CE checkpoint improved the practical best further; the retained `27000` checkpoint reproduced at `92.38%` accuracy and `88.70%` macro-F1 on the full test set
- a `head-lite + plain CE` variant looked promising on the fixed proxy subset but underperformed on the full test set at `7000` (`78.07%` accuracy, `62.08%` macro-F1)
The current working conclusion is:
@@ -79,7 +80,7 @@ The current working conclusion is:
- the strong silhouette checkpoint is not evidence that the skeleton-map path works
- the main remaining suspect is the skeleton-map representation and preprocessing path
- for practical model development, `1:1:2` is currently the better working split than `1:1:8`
- for practical model development, the current best skeleton recipe is still `body-only + plain CE + SGD` on `1:1:2`
- for practical model development, the current best skeleton recipe is `body-only + plain CE`, and the current best retained checkpoint comes from a later `AdamW` cosine finetune on `1:1:2`
- the first practical DRF bridge on that same winning `1:1:2` recipe did not improve on the plain skeleton baseline:
- best retained DRF checkpoint (`2000`) on the full test set: `80.21 Acc / 58.92 Prec / 59.23 Rec / 57.84 F1`
- current best plain skeleton checkpoint (`7000`) on the full test set: `83.16 Acc / 68.24 Prec / 80.02 Rec / 68.47 F1`
@@ -119,3 +120,35 @@ The skeleton-map control used the same recipe, except for the modality-specific
1. Train a pure silhouette `1:1:8` baseline from the upstream ScoNet config as a clean sanity control.
2. Treat skeleton-map preprocessing as the primary debugging target until a `ScoNet-MT-ske`-style run gets close to the paper.
3. Only after the skeleton baseline is credible should DRF/PAV-specific conclusions be treated as decisive.
## Practical conclusion
For practical use in this repo, the current winning path is:
- split: `1:1:2`
- representation: `body-only` skeleton map
- losses: plain CE + triplet
- baseline training: `SGD`
- best retained finetune: `AdamW` + cosine decay
The strongest verified checkpoint so far is:
- `ScoNet_skeleton_112_sigma15_joint8_bodyonly_plaince_adamw_cosine_finetune_1gpu_80k`
- retained best checkpoint at `27000`
- verified full-test result: `92.38 Acc / 90.30 Prec / 87.39 Rec / 88.70 F1`
So the current local recommendation is:
- keep `body-only` as the default practical skeleton representation
- keep `1:1:2` as the main practical split
- treat DRF as an optional research branch, not the mainline model
## Remaining useful experiments
At this point, there are only a few experiments that still look high-value:
1. one clean `full-body` finetune under the same successful `1:1:2` recipe, just to confirm that `body-only` is really the best practical representation
2. one DRF rerun on top of the now-stronger practical baseline recipe, only if the goal is to test whether DRF can add value once the skeleton branch is already strong
3. a final packaging/evaluation pass around the retained best checkpoints, rather than more broad preprocessing churn
Everything else looks lower value than simply using the retained best `27000` checkpoint.