prepare for replicate

.dockerignore

@@ -0,0 +1,21 @@
+# generated by replicate/cog
+__pycache__
+*.pyc
+*.pyo
+*.pyd
+.Python
+env
+pip-log.txt
+pip-delete-this-directory.txt
+.tox
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.log
+.git
+.mypy_cache
+.pytest_cache
+.hypothesis

README.md

@@ -22,6 +22,20 @@ curl -o /usr/local/bin/cog -L https://github.com/replicate/cog/releases/latest/d
 chmod +x /usr/local/bin/cog
 ```
 
+## Cog
+
+build the image
+
+```bash
+cog build --separate-weights
+```
+
+push the image
+
+```bash
+cog push
+```
+
 ## Training
 
 ```bash
@@ -75,21 +89,6 @@ clip/
 - Fine-tuned vision encoder (last 3 layers)
 - Output: 4-class classifier
 
-## Performance
-
-- Training Accuracy: ~95%
-- Validation Accuracy: ~92%
-- Inference Time: ~100ms per video
-
-## Configuration
-
-Key hyperparameters can be modified in `config/default.yaml`:
-- Frame sampling: 10 frames per video
-- Image size: 224x224
-- Learning rate: 2e-6
-- Weight decay: 0.007
-- Data augmentation parameters
-
 ## License
 
 [Your License Here]

cog.yaml

@@ -14,5 +14,5 @@ build:
     - "numpy"
     - "scipy"
     - "huggingface_hub"
-
+image: "r8.im/bawolf/break-video-clip"
 predict: "predict.py:Predictor"

{scripts → script}/hyperparameter_tuning.py

@@ -4,7 +4,7 @@ import os
 import os
 import sys
 sys.path.append(os.path.dirname(os.path.dirname(__file__)))
-from scripts.train import train_and_evaluate
+from script.train import train_and_evaluate
 from src.utils.utils import create_run_directory
 
 def objective(trial, hyperparam_run_dir):

{scripts → script}/visualization/visualize.py

@@ -12,7 +12,6 @@ sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
 from src.data.dataset import VideoDataset
 from src.utils.utils import get_latest_model_path, get_latest_run_dir, get_config
 from src.models.model import load_model
-import json
 
 def plot_training_curves(log_file, output_dir):
     data = pd.read_csv(log_file)