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challenge0123 (Isaac Sim + camera-facing v4 convention baseline).arch : DOPE (VGG-19 backbone + belief maps + affinity fields)
keypoints : 9 (8 cuboid corners + centroid, camera-facing v4 convention)
input : 448x448 RGB
output : belief maps (9ch) + affinity fields (16ch)
pallet dim : 1.10 x 1.30 x 0.11 m (KS T-11형)Weight : final_net_epoch_0080.pth
Init weight : challenge0123/final_net_epoch_0060.pth (scratch -> 60 ep on mixed_v8 + chal_v1 + chal_v2)
Epochs : 80 (60 -> 80, 20 ep fine-tune)
Batch size : 8
LR : 1e-4
Sigma : 4.0 (belief Gaussian)
Image size : 448
Workers : 4
Seed : 4139
Loss : belief MSE + affinity (no symmetric / no geo / no struct / no rel)capturepallet03_manual_gt
capturepallet04_manual_gt
capturepallet05_manual_gt
capturepallet07_manual_gt
capturepallet09_manual_gt
capturepalletcad_manual_gtfinal_net_epoch_0080.pth trained model weight
header.txt raw training Namespace + seed
README.md this filetwin_pnp_check.py (50/50 frame, reproj 2.89px, |dt|=0.085m):1PALLET_WIDTH_M = 1.0 # mixed_v8_train label dim
2PALLET_DEPTH_M = 1.2
3PALLET_HEIGHT_M = 0.15
4PALLET_PNP_CONTRACT_Z180 = True # Cuboid3d.vertices @ diag([-1,-1,+1])task.yaml's nominal (1.1, 1.3, 0.11) is the spec value and does not match the actual label dim used in training.ImageNet pretrained VGG-19
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challenge0123 scratch, 60 ep, mixed_v8_train + chal_v1 + chal_v2
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challenge0123_ft_manual <--- this model, 20 ep on 6 manual-GT sequencesproject_keypoint_convention_v4_conversion.mdproject_annotate_pnp_fix_v4_gravity.md