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FlexICM / checkpoints /README.md
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Add task-metric eval path and Cascade Mask R-CNN + Swin-B for det/instance.
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Checkpoints Layout (Placeholders)

Put pretrained / trained weights here before running test / eval. Training still writes to logs/ by default; after training, copy (or symlink) best checkpoints into this tree so eval configs have a stable path.

Files named PLACEHOLDER are not real weights. Replace each with the matching .pth.tar checkpoint, then update or keep the path expected by eval scripts.

Directory map 

checkpoints/
β”œβ”€β”€ base_codec/                 # frozen TIC (TransTIC / AdaptiveICMH)
β”‚   β”œβ”€β”€ base_codec_1.pth.tar    # Ξ» = 0.0035
β”‚   β”œβ”€β”€ base_codec_2.pth.tar    # Ξ» = 0.0067
β”‚   β”œβ”€β”€ base_codec_3.pth.tar    # Ξ» = 0.0130
β”‚   └── base_codec_4.pth.tar    # Ξ» = 0.0250
β”‚
β”œβ”€β”€ taic/                       # five single-task TAIC codecs
β”‚   β”œβ”€β”€ detection/{1,2,3,4}/checkpoint_best_loss.pth.tar
β”‚   β”œβ”€β”€ semantic/{1,2,3,4}/checkpoint_best_loss.pth.tar
β”‚   β”œβ”€β”€ instance/{1,2,3,4}/checkpoint_best_loss.pth.tar
β”‚   β”œβ”€β”€ panoptic/{1,2,3,4}/checkpoint_best_loss.pth.tar
β”‚   └── pose/{1,2,3,4}/checkpoint_best_loss.pth.tar
β”‚
└── ctaic/                      # three multi-task scenarios
    β”œβ”€β”€ s1_det_instance/
    β”‚   β”œβ”€β”€ stage1/{1,2,3,4}/checkpoint_best_loss.pth.tar
    β”‚   └── stage2/{1,2,3,4}/checkpoint_best_loss.pth.tar
    β”œβ”€β”€ s2_sem_panoptic/
    β”‚   β”œβ”€β”€ stage1/{1,2,3,4}/checkpoint_best_loss.pth.tar
    β”‚   └── stage2/{1,2,3,4}/checkpoint_best_loss.pth.tar
    └── s3_det_pose/
        β”œβ”€β”€ stage1/{1,2,3,4}/checkpoint_best_loss.pth.tar
        └── stage2/{1,2,3,4}/checkpoint_best_loss.pth.tar

Quality folders {1,2,3,4} match paper Ξ» / TIC quality levels.

Download base TIC codecs 

bash scripts/download_base_codecs.sh
# downloads into checkpoints/base_codec/

After training: copy into placeholders 

# example: TAIC detection, quality 1
cp logs/taic_detection/1/checkpoint_best_loss.pth.tar \
   checkpoints/taic/detection/1/checkpoint_best_loss.pth.tar

# example: C-TAIC s1 stage2, quality 1
cp logs/ctaic_s1_stage2/1/checkpoint_best_loss.pth.tar \
   checkpoints/ctaic/s1_det_instance/stage2/1/checkpoint_best_loss.pth.tar

Eval configs

See configs/eval/ β€” they point to these placeholder paths.

Codec test (bpp / feature distortion):

python scripts/eval_taic.py -c configs/eval/taic_detection.yaml
python scripts/eval_ctaic.py -c configs/eval/ctaic_s1.yaml

Scripts refuse to run if a PLACEHOLDER file is still present or if the .pth.tar is missing. Task rate–accuracy metrics are not implemented in these scripts yet.

Task networks (for metric evaluation) 

checkpoints/task_networks/
β”œβ”€β”€ detection/model.pth
β”œβ”€β”€ instance/model.pth
β”œβ”€β”€ semantic/model.pth
β”œβ”€β”€ panoptic/model.pth
└── pose/model.pth

These are official pretrained task networks (not codec weights). Required when running:

python scripts/eval_taic.py -c configs/eval/taic_detection.yaml --with-metrics

See configs/task_networks/README.md for config/checkpoint pairing.