Replace repo with official FlexICM checkpoints only

Remove previous code/weights and publish the mentor-selected TAIC/C-TAIC checkpoint_best_loss set under checkpoints/.

.gitignore

@@ -1,32 +0,0 @@
-__pycache__/
-*.py[cod]
-*.egg-info/
-.eggs/
-dist/
-build/
-.DS_Store
-logs/
-*.log
-coco_log/
-.ipynb_checkpoints/
-_figs/
-_paper_extract.txt
-
-# paper PDF — do not upload
-FlexICM.pdf
-*.pdf
-
-# Heavy weights to keep local-only.
-# TAIC / C-TAIC under checkpoints/{taic,ctaic} are intentionally uploadable
-# (avoid broad checkpoints/** + !negation — Hub upload handles ! poorly).
-checkpoints/base_codec/**/*.pth.tar
-checkpoints/base_codec/**/*.tar
-checkpoints/base_codec/**/*.pkl
-checkpoints/base_codec/**/*.pth
-checkpoints/task_networks/**/*.pth.tar
-checkpoints/task_networks/**/*.tar
-checkpoints/task_networks/**/*.pkl
-checkpoints/task_networks/**/*.pth
-!checkpoints/**/PLACEHOLDER*
-!checkpoints/**/*.txt
-!checkpoints/README.md

README.md

@@ -8,439 +8,5 @@ tags:
 
 # FlexICM: A Flexible Image Coding for Machines Framework
 
-Official codebase for the paper **FlexICM: A Flexible Image Coding for Machines Framework** (Tianma Shen, Ying Liu).
-
-Built on the **TIC (Transformer-based Image Compression)** base codec, this repository implements:
-
-- **TAIC (Base Layer)**: five single-task codecs that decode task intermediate features `h` **without** full image reconstruction
-- **C-TAIC (Extension Layer)**: three multi-task scenarios that condition on the base-layer latent \hat{y}_b via cross-attention
-
-## Five Tasks and Three Scenarios
-
-### TAIC (five task codecs)
-
-
-| Task                  | Teacher / Task Network          | Feature Alignment       | Metric   |
-| --------------------- | ------------------------------- | ----------------------- | -------- |
-| Object Detection      | Faster R-CNN + **Swin-B**       | FPN `P2..P6` (Eq. 2)    | mAP-bbox |
-| Semantic Segmentation | UPerNet + **Swin-B**            | FPN `P2..P6`            | mIoU     |
-| Instance Segmentation | Cascade Mask R-CNN + **Swin-B** | FPN `P2..P6`            | mAP-mask |
-| Panoptic Segmentation | MaskFormer + **Swin-B**         | Stages `F1..F4` (Eq. 3) | PQ       |
-| Pose Estimation       | **HigherHRNet**                 | Stages `F1..F4`         | mAP-OKS  |
-
-
-
-
-### C-TAIC (three scenarios)
-
-
-| Scenario | Base Layer            | Extension Layer       |
-| -------- | --------------------- | --------------------- |
-| **s1**   | Object Detection      | Instance Segmentation |
-| **s2**   | Semantic Segmentation | Panoptic Segmentation |
-| **s3**   | Object Detection      | Pose Estimation       |
-
-
----
-
-
-
-## Environment Setup
-
-> **Important:** Codec training **requires** task networks (teachers) to be available.
-> The loss D is computed from frozen teacher features, so you cannot train TAIC / C-TAIC
-> with only the codec packages. Install the teacher stack in **Task networks (teachers)** before the first training run.
-
-
-
-### Recommended environment
-
-- Ubuntu / RHEL, **CUDA 11.7+**, single **NVIDIA A100** (paper setting)
-- Python **3.8–3.10**
-- PyTorch **≥ 1.12** (2.0+ recommended)
-
-```bash
-conda create -n flexicm python=3.9 -y
-conda activate flexicm
-pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118
-pip install -r requirements.txt
-```
-
-
-
-### Core codec dependencies
-
-
-| Package      | Role                                                       |
-| ------------ | ---------------------------------------------------------- |
-| `compressai` | EntropyBottleneck / GaussianConditional / conv-deconv      |
-| `timm`       | **Required** Swin-B teacher backbone for feature alignment |
-| `PyYAML`     | Training configs                                           |
-
-
-
-
-### Task networks (teachers) — **required before training**
-
-Teachers are already implemented in `flexicm/tasks/` and are constructed automatically by
-`scripts/train_taic.py` / `scripts/train_ctaic.py` via `build_teacher(...)`.
-You still must install their runtime dependencies and allow pretrained weights to download.
-
-
-| Task                 | Teacher used in training                                                     | What you need installed                              |
-| -------------------- | ---------------------------------------------------------------------------- | ---------------------------------------------------- |
-| Detection / Instance | Swin-B (`timm`) for FPN alignment; metric head = Cascade Mask R-CNN + Swin-B | `timm`; ImageNet Swin-B on first run                 |
-| Semantic / Panoptic  | Swin-B (`timm`)                                                              | `timm`; ImageNet Swin-B on first run                 |
-| Pose                 | HigherHRNet-style HRNet stem (original HRNet, not Swin)                      | Implemented in-repo; no extra package beyond PyTorch |
-
-
-Checklist before training:
-
-1. `pip install -r requirements.txt` (includes `timm`)
-2. Machine can reach the internet **or** you have cached `timm` Swin weights
-3. Verify teachers import cleanly:
-
-```bash
-python -c "from flexicm.tasks import build_teacher; build_teacher('detection'); print('teachers ok')"
-```
-
-Without a working teacher, training will fail when computing the feature-alignment term D.
-
-### Task heads for metric evaluation
-
-To evaluate paper metrics (mAP / mIoU / PQ / OKS) with full task heads, also install:
-
-```bash
-pip install -U openmim
-mim install mmengine mmcv
-mim install mmdet mmsegmentation mmpose
-```
-
-Official detection / instance weights from
-[Swin-Transformer-Object-Detection](https://github.com/SwinTransformer/Swin-Transformer-Object-Detection)
-(see `configs/task_networks/README.md`):
-
-- **Cascade Mask R-CNN + Swin-B** (detection mAP-bbox **and** instance mAP-mask; same weights)
-- **UPerNet + Swin-B**: MMSegmentation Model Zoo
-- **MaskFormer + Swin-B**: MMDetection / Mask2Former
-- **HigherHRNet**: MMPose Model Zoo (**HRNet backbone**)
-
-These full heads are **not** required to start codec training; they are for final rate–accuracy evaluation.
-
----
-
-
-
-## Repository Layout
-
-```
-FlexICM/
-├── FlexICM.pdf                 # paper
-├── requirements.txt
-├── README.md
-├── configs/
-│   ├── taic/                   # five single-task configs
-│   │   ├── detection.yaml
-│   │   ├── semantic.yaml
-│   │   ├── instance.yaml
-│   │   ├── panoptic.yaml
-│   │   └── pose.yaml
-│   └── ctaic/                  # three multi-task scenarios
-│       ├── s1_det_instance.yaml
-│       ├── s2_sem_panoptic.yaml
-│       └── s3_det_pose.yaml
-├── scripts/
-│   ├── download_base_codecs.sh
-│   ├── train_taic.py
-│   ├── train_ctaic.py
-│   ├── eval_taic.py            # codec test (bpp / feature D)
-│   └── eval_ctaic.py           # codec test for C-TAIC
-├── flexicm/
-│   ├── models/                 # TAIC / C-TAIC / SFMA / TaskConnector / Conditional
-│   ├── layers/                 # RSTB / WindowAttention (same lineage as AdaptiveICMH)
-│   ├── tasks/                  # teachers + feature-alignment losses
-│   ├── data/                   # COCO / COCO-WholeBody image loading
-│   └── utils/
-└── checkpoints/                # placeholder tree for base / TAIC / C-TAIC weights
-                                # see checkpoints/README.md
-```
-
-Eval / codec-test configs: `configs/eval/`.
-
----
-
-
-
-## Dataset Preparation
-
-
-
-### COCO-2017 (detection / instance / semantic / panoptic)
-
-```text
-/data/coco2017/
-├── train2017/
-├── val2017/
-└── annotations/
-    ├── instances_train2017.json
-    ├── instances_val2017.json
-    ├── panoptic_train2017.json
-    ├── panoptic_val2017.json
-    ├── panoptic_train2017/          # PNG
-    ├── panoptic_val2017/
-    ├── stuff_train2017.json         # semantic / stuff (if used)
-    └── stuff_val2017.json
-```
-
-Download:
-
-```bash
-# images
-wget http://images.cocodataset.org/zips/train2017.zip
-wget http://images.cocodataset.org/zips/val2017.zip
-# annotations
-wget http://images.cocodataset.org/annotations/annotations_trainval2017.zip
-wget http://images.cocodataset.org/annotations/panoptic_annotations_trainval2017.zip
-```
-
-Set in the corresponding YAML:
-
-```yaml
-dataset_path: "/data/coco2017"
-```
-
-
-
-### COCO-WholeBody (pose estimation)
-
-Pose uses the same COCO `train2017/val2017` images plus WholeBody keypoint annotations:
-
-- Project page: [COCO-WholeBody](https://github.com/jin-s13/COCO-WholeBody)
-- Place JSON files under `annotations/`; evaluate with MMPose HigherHRNet + WholeBody configs
-
-Codec **training** only needs images for feature alignment, so `train2017` images are sufficient for that stage.
-
-### Data processing (aligned with task networks)
-
-The paper requires **codec training preprocessing to match task-network preprocessing**. Defaults in this repo:
-
-1. **Codec input**: RGB, `ToTensor()` → `[0,1]`; training uses `Resize → RandomCrop(256) → RandomHorizontalFlip`
-2. **Inside the teacher**: ImageNet mean/std normalization (consistent with Swin / HRNet pretraining)
-3. **Spatial alignment**: TIC requires spatial size divisible by **256** (256 crop for training; pad at inference)
-
-If you use official MMDet/MMSeg pipelines (short-side resize, normalization, etc.), ensure:
-
-- Teacher feature extraction uses the **same normalize / resize logic** as that task network
-- Codec and teacher see geometrically consistent tensors (same crop / same pad)
-
-Edit points: `flexicm/data/datasets.py`, `flexicm/tasks/swin_teacher.py`, `flexicm/tasks/__init__.py` (HigherHRNet).
-
----
-
-
-
-## Base Codec (TIC) Checkpoints
-
-The paper uses the same TIC pretrained weights as AdaptiveICMH / TransTIC:
-
-
-| Quality | λ (paper) | Checkpoint                                                                                        |
-| ------- | --------- | ------------------------------------------------------------------------------------------------- |
-| 1       | 0.0035    | [base_codec_1](https://github.com/NYCU-MAPL/TransTIC/releases/download/v1.0/base_codec_1.pth.tar) |
-| 2       | 0.0067    | [base_codec_2](https://github.com/NYCU-MAPL/TransTIC/releases/download/v1.0/base_codec_2.pth.tar) |
-| 3       | 0.0130    | [base_codec_3](https://github.com/NYCU-MAPL/TransTIC/releases/download/v1.0/base_codec_3.pth.tar) |
-| 4       | 0.0250    | [base_codec_4](https://github.com/NYCU-MAPL/TransTIC/releases/download/v1.0/base_codec_4.pth.tar) |
-
-
-```bash
-bash scripts/download_base_codecs.sh
-# downloads into checkpoints/base_codec/base_codec_{1,2,3,4}.pth.tar
-```
-
-Config example:
-
-```yaml
-base_codec: "./checkpoints/base_codec/base_codec_1.pth.tar"
-quality_level: 1
-lmbda: 0.0035
-```
-
-For each bitrate point, switch the matching `base_codec_k` and `lmbda`.
-
-Trained TAIC / C-TAIC weights for eval should be placed under `checkpoints/taic/` and
-`checkpoints/ctaic/` (see `checkpoints/README.md`). Until then, each quality folder
-contains a `PLACEHOLDER` file.
-
----
-
-
-
-## Training
-
-Paper settings:
-
-- Optimizer: **AdamW**, `lr=1e-4`
-- TAIC: `batch_size=80`, `epochs=35`
-- C-TAIC: `batch_size=40`, `epochs=40`
-- `λ ∈ {0.0035, 0.0067, 0.0130, 0.0250}`
-
-> If GPU memory is insufficient, reduce `batch_size` (optionally use gradient accumulation to approximate the paper effective batch).
-
-
-
-### Train five TAIC models
-
-```bash
-# edit dataset_path / base_codec / lmbda / gpu_id in configs/taic/*.yaml as needed
-python scripts/train_taic.py -c configs/taic/detection.yaml
-python scripts/train_taic.py -c configs/taic/semantic.yaml
-python scripts/train_taic.py -c configs/taic/instance.yaml
-python scripts/train_taic.py -c configs/taic/panoptic.yaml
-python scripts/train_taic.py -c configs/taic/pose.yaml
-```
-
-Trainable modules: **encoder SFMA + Task Connector**; TIC trunk is frozen.
-
-### Train three C-TAIC scenarios
-
-Requires a trained **base TAIC** checkpoint (to provide \hat{y}_b) and Stage-1 weights for the extension task.
-
-```bash
-# ---- s1: det → instance ----
-python scripts/train_ctaic.py -c configs/ctaic/s1_det_instance.yaml --stage 1
-python scripts/train_ctaic.py -c configs/ctaic/s1_det_instance.yaml --stage 2
-
-# ---- s2: semantic → panoptic ----
-python scripts/train_ctaic.py -c configs/ctaic/s2_sem_panoptic.yaml --stage 1
-python scripts/train_ctaic.py -c configs/ctaic/s2_sem_panoptic.yaml --stage 2
-
-# ---- s3: det → pose ----
-python scripts/train_ctaic.py -c configs/ctaic/s3_det_pose.yaml --stage 1
-python scripts/train_ctaic.py -c configs/ctaic/s3_det_pose.yaml --stage 2
-```
-
-Stage meanings:
-
-
-| Stage | Mode        | Trainable modules                      | `ŷ_b`                           |
-| ----- | ----------- | -------------------------------------- | ------------------------------- |
-| 1     | TAIC mode   | SFMA + Task Connector                  | not used                        |
-| 2     | C-TAIC mode | Prompt Generator + Condition Generator | from frozen base TAIC AD output |
-
-
-Check these config fields:
-
-```yaml
-base_taic_checkpoint:  # trained TAIC for the base task
-taic_init:             # extension-task TAIC (optional Stage-1 init)
-stage1_checkpoint:     # Stage-1 result loaded in Stage 2
-```
-
----
-
-
-
-## Codec Test
-
-Codec test measures **compression statistics**:
-
-
-| Metric       | Meaning                                                           |
-| ------------ | ----------------------------------------------------------------- |
-| `bpp`        | Likelihood bitrate R                                              |
-| `distortion` | Feature alignment D (Eq. 2 or Eq. 3)                              |
-| `loss`       | R + \lambda D                                                     |
-| `actual_bpp` | Optional: real bitstream size after `compress()` / `decompress()` |
-
-
-For C-TAIC, reported `bpp` is **extension-layer only** (base-layer rate is excluded), matching the paper.
-
-### Prepare codec checkpoints
-
-1. Copy trained weights into `checkpoints/taic/` or `checkpoints/ctaic/` (see `checkpoints/README.md`)
-2. Remove the local `PLACEHOLDER` once `checkpoint_best_loss.pth.tar` is present
-3. Edit `dataset_path` / `gpu_id` in `configs/eval/*.yaml`
-
-```bash
-python scripts/eval_taic.py -c configs/eval/taic_detection.yaml
-python scripts/eval_ctaic.py -c configs/eval/ctaic_s1.yaml
-```
-
----
-
-
-
-## Task-network metric evaluation
-
-To reproduce paper rate–accuracy numbers you must **also** load the official pretrained
-**task networks** and run metrics on COCO val:
-
-
-| Task      | Task network                 | Metric   |
-| --------- | ---------------------------- | -------- |
-| Detection | Cascade Mask R-CNN + Swin-B  | mAP-bbox |
-| Instance  | Cascade Mask R-CNN + Swin-B  | mAP-mask |
-| Semantic  | UPerNet + Swin-B             | mIoU     |
-| Panoptic  | MaskFormer + Swin-B          | PQ       |
-| Pose      | HigherHRNet (HRNet backbone) | mAP-OKS  |
-
-
-Pipeline: `image → codec → h → truncated task net (from Stage2 / FPN) → metric`.
-
-### Install metric dependencies
-
-```bash
-pip install pycocotools
-pip install -U openmim
-mim install mmengine mmcv mmdet mmsegmentation mmpose
-# optional for PQ:
-# pip install git+https://github.com/cocodataset/panopticapi.git
-```
-
-
-
-### Prepare task-network configs & checkpoints
-
-1. Put / symlink real OpenMMLab configs under `configs/task_networks/`
-  (see `configs/task_networks/README.md`; current `*.py` files are stubs)
-2. Download official weights to:
-
-```text
-checkpoints/task_networks/
-├── detection/model.pth
-├── instance/model.pth
-├── semantic/model.pth
-├── panoptic/model.pth
-└── pose/model.pth
-```
-
-1. Set in each `configs/eval/*.yaml`:
-
-```yaml
-task_config: "./configs/task_networks/<real_config>.py"
-task_checkpoint: "./checkpoints/task_networks/<task>/model.pth"
-ann_file: "annotations/instances_val2017.json"
-```
-
-
-
-### Run codec + metrics
-
-```bash
-python scripts/eval_taic.py -c configs/eval/taic_detection.yaml 
-python scripts/eval_taic.py -c configs/eval/taic_instance.yaml
-python scripts/eval_taic.py -c configs/eval/taic_semantic.yaml
-python scripts/eval_taic.py -c configs/eval/taic_panoptic.yaml 
-python scripts/eval_taic.py -c configs/eval/taic_pose.yaml 
-
-python scripts/eval_ctaic.py -c configs/eval/ctaic_s1.yaml 
-python scripts/eval_ctaic.py -c configs/eval/ctaic_s2.yaml
-python scripts/eval_ctaic.py -c configs/eval/ctaic_s3.yaml 
-```
-
-JSON results (codec + task metrics) are written under `logs/eval_taic/` or `logs/eval_ctaic/`.
-
-> Detection / instance metric paths are the most complete (COCO bbox via pycocotools).
-> Semantic mIoU needs a GT label loader; panoptic PQ needs `panopticapi` + GT folders;
-> pose-from-`h` may need a HigherHRNet stem hook for your exact MMPose version.
+Official checkpoints for the paper **FlexICM: A Flexible Image Coding for Machines Framework** (Tianma Shen, Ying Liu).
 

checkpoints/README.md

@@ -1,92 +0,0 @@
-# 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
-
-```text
-checkpoints/
-├── base_codec/                 # frozen TIC (TransTIC / AdaptiveICMH)
-│   ├── base_codec_1.pth.tar    # λ = 0.0035
-│   ├── base_codec_2.pth.tar    # λ = 0.0067
-│   ├── base_codec_3.pth.tar    # λ = 0.0130
-│   └── base_codec_4.pth.tar    # λ = 0.0250
-│
-├── taic/                       # five single-task TAIC codecs
-│   ├── detection/{1,2,3,4}/checkpoint_best_loss.pth.tar
-│   ├── semantic/{1,2,3,4}/checkpoint_best_loss.pth.tar
-│   ├── instance/{1,2,3,4}/checkpoint_best_loss.pth.tar
-│   ├── panoptic/{1,2,3,4}/checkpoint_best_loss.pth.tar
-│   └── pose/{1,2,3,4}/checkpoint_best_loss.pth.tar
-│
-└── ctaic/                      # three multi-task scenarios
-    ├── s1_det_instance/
-    │   ├── stage1/{1,2,3,4}/checkpoint_best_loss.pth.tar
-    │   └── stage2/{1,2,3,4}/checkpoint_best_loss.pth.tar
-    ├── s2_sem_panoptic/
-    │   ├── stage1/{1,2,3,4}/checkpoint_best_loss.pth.tar
-    │   └── stage2/{1,2,3,4}/checkpoint_best_loss.pth.tar
-    └── s3_det_pose/
-        ├── 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 λ / TIC quality levels.
-
-## Download base TIC codecs
-
-```bash
-bash scripts/download_base_codecs.sh
-# downloads into checkpoints/base_codec/
-```
-
-## After training: copy into placeholders
-
-```bash
-# 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/` — they point to these placeholder paths.
-
-Codec test (bpp / feature distortion):
-
-```bash
-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)
-
-```text
-checkpoints/task_networks/
-├── detection/model.pth
-├── instance/model.pth
-├── semantic/model.pth
-├── panoptic/model.pth
-└── pose/model.pth
-```
-
-These are **official pretrained task networks** (not codec weights).
-Required when running:
-
-```bash
-python scripts/eval_taic.py -c configs/eval/taic_detection.yaml --with-metrics
-```
-
-See `configs/task_networks/README.md` for config/checkpoint pairing.

checkpoints/base_codec/NOTE.txt

@@ -1,2 +0,0 @@
-Legacy config paths used ./checkpoints/base_codec_k.pth.tar.
-Prefer checkpoints/base_codec/base_codec_k.pth.tar after download.

checkpoints/base_codec/PLACEHOLDER_base_codec_1.pth.tar.txt

@@ -1,2 +0,0 @@
-PLACEHOLDER: replace this file with the real checkpoint (.pth.tar).
-See checkpoints/README.md for the expected filename and training copy commands.

checkpoints/base_codec/PLACEHOLDER_base_codec_2.pth.tar.txt

@@ -1,2 +0,0 @@
-PLACEHOLDER: replace this file with the real checkpoint (.pth.tar).
-See checkpoints/README.md for the expected filename and training copy commands.

checkpoints/base_codec/PLACEHOLDER_base_codec_3.pth.tar.txt

@@ -1,2 +0,0 @@
-PLACEHOLDER: replace this file with the real checkpoint (.pth.tar).
-See checkpoints/README.md for the expected filename and training copy commands.

checkpoints/base_codec/PLACEHOLDER_base_codec_4.pth.tar.txt

@@ -1,2 +0,0 @@
-PLACEHOLDER: replace this file with the real checkpoint (.pth.tar).
-See checkpoints/README.md for the expected filename and training copy commands.

checkpoints/ctaic/s1_det_instance/stage1/1/PLACEHOLDER

@@ -1,2 +0,0 @@
-PLACEHOLDER: replace this file with the real checkpoint (.pth.tar).
-See checkpoints/README.md for the expected filename and training copy commands.

checkpoints/ctaic/s1_det_instance/stage1/2/PLACEHOLDER

@@ -1,2 +0,0 @@
-PLACEHOLDER: replace this file with the real checkpoint (.pth.tar).
-See checkpoints/README.md for the expected filename and training copy commands.

checkpoints/ctaic/s1_det_instance/stage1/3/PLACEHOLDER

@@ -1,2 +0,0 @@
-PLACEHOLDER: replace this file with the real checkpoint (.pth.tar).
-See checkpoints/README.md for the expected filename and training copy commands.

checkpoints/ctaic/s1_det_instance/stage1/4/PLACEHOLDER

@@ -1,2 +0,0 @@
-PLACEHOLDER: replace this file with the real checkpoint (.pth.tar).
-See checkpoints/README.md for the expected filename and training copy commands.

checkpoints/ctaic/s1_det_instance/stage2/1/PLACEHOLDER

@@ -1,2 +0,0 @@
-PLACEHOLDER: replace this file with the real checkpoint (.pth.tar).
-See checkpoints/README.md for the expected filename and training copy commands.

checkpoints/ctaic/s1_det_instance/stage2/2/PLACEHOLDER

@@ -1,2 +0,0 @@
-PLACEHOLDER: replace this file with the real checkpoint (.pth.tar).
-See checkpoints/README.md for the expected filename and training copy commands.

checkpoints/ctaic/s1_det_instance/stage2/3/PLACEHOLDER

@@ -1,2 +0,0 @@
-PLACEHOLDER: replace this file with the real checkpoint (.pth.tar).
-See checkpoints/README.md for the expected filename and training copy commands.

checkpoints/ctaic/s1_det_instance/stage2/4/PLACEHOLDER

@@ -1,2 +0,0 @@
-PLACEHOLDER: replace this file with the real checkpoint (.pth.tar).
-See checkpoints/README.md for the expected filename and training copy commands.

checkpoints/ctaic/s2_sem_panoptic/stage1/1/PLACEHOLDER

@@ -1,2 +0,0 @@
-PLACEHOLDER: replace this file with the real checkpoint (.pth.tar).
-See checkpoints/README.md for the expected filename and training copy commands.

checkpoints/ctaic/s2_sem_panoptic/stage1/2/PLACEHOLDER

@@ -1,2 +0,0 @@
-PLACEHOLDER: replace this file with the real checkpoint (.pth.tar).
-See checkpoints/README.md for the expected filename and training copy commands.

checkpoints/ctaic/s2_sem_panoptic/stage1/3/PLACEHOLDER

@@ -1,2 +0,0 @@
-PLACEHOLDER: replace this file with the real checkpoint (.pth.tar).
-See checkpoints/README.md for the expected filename and training copy commands.

checkpoints/ctaic/s2_sem_panoptic/stage1/4/PLACEHOLDER

@@ -1,2 +0,0 @@
-PLACEHOLDER: replace this file with the real checkpoint (.pth.tar).
-See checkpoints/README.md for the expected filename and training copy commands.

checkpoints/ctaic/s2_sem_panoptic/stage2/1/PLACEHOLDER

@@ -1,2 +0,0 @@
-PLACEHOLDER: replace this file with the real checkpoint (.pth.tar).
-See checkpoints/README.md for the expected filename and training copy commands.

checkpoints/ctaic/s2_sem_panoptic/stage2/2/PLACEHOLDER

@@ -1,2 +0,0 @@
-PLACEHOLDER: replace this file with the real checkpoint (.pth.tar).
-See checkpoints/README.md for the expected filename and training copy commands.

checkpoints/ctaic/s2_sem_panoptic/stage2/3/PLACEHOLDER

@@ -1,2 +0,0 @@
-PLACEHOLDER: replace this file with the real checkpoint (.pth.tar).
-See checkpoints/README.md for the expected filename and training copy commands.

checkpoints/ctaic/s2_sem_panoptic/stage2/4/PLACEHOLDER

@@ -1,2 +0,0 @@
-PLACEHOLDER: replace this file with the real checkpoint (.pth.tar).
-See checkpoints/README.md for the expected filename and training copy commands.

checkpoints/ctaic/s3_det_pose/stage1/1/PLACEHOLDER

@@ -1,2 +0,0 @@
-PLACEHOLDER: replace this file with the real checkpoint (.pth.tar).
-See checkpoints/README.md for the expected filename and training copy commands.

checkpoints/ctaic/s3_det_pose/stage1/2/PLACEHOLDER

@@ -1,2 +0,0 @@
-PLACEHOLDER: replace this file with the real checkpoint (.pth.tar).
-See checkpoints/README.md for the expected filename and training copy commands.

checkpoints/ctaic/s3_det_pose/stage1/3/PLACEHOLDER

@@ -1,2 +0,0 @@
-PLACEHOLDER: replace this file with the real checkpoint (.pth.tar).
-See checkpoints/README.md for the expected filename and training copy commands.

checkpoints/ctaic/s3_det_pose/stage1/4/PLACEHOLDER

@@ -1,2 +0,0 @@
-PLACEHOLDER: replace this file with the real checkpoint (.pth.tar).
-See checkpoints/README.md for the expected filename and training copy commands.

checkpoints/ctaic/s3_det_pose/stage2/1/PLACEHOLDER

@@ -1,2 +0,0 @@
-PLACEHOLDER: replace this file with the real checkpoint (.pth.tar).
-See checkpoints/README.md for the expected filename and training copy commands.

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