fix: removed www entry

api/README.md

@@ -338,6 +338,7 @@ Code: `src/yolo/` · Defaults: `src/yolo/config.py` · Model: [`Yolo_model`](#yo
 | `--save-dir` | `saved_models` | train, sample, webcam | Root folder of checkpoints. |
 | `--model-name` | `face_detection_yolo` | train, sample, webcam | Checkpoint name. |
 | `--save` / `--no-save` | `--save` | train | Save the best checkpoint. |
+| `--save-every-epoch` / `--no-save-every-epoch` | off | train | With `--save`: also keep every epoch as `<model-name>_epochNNN` next to the best checkpoint. |
 | `--runs-dir` | `runs` | train | Run log folder. Empty disables it. |
 | `--run-name` | `""` | train | Label appended to the run folder name. |
 | `--checkpoint` | `""` | sample, webcam | Explicit weights file. Empty = `<save-dir>/<model-name>/<model-name>`. |
@@ -919,6 +920,8 @@ far slower than the single-shot YOLO model. That is why the website uses YOLO.
    - any other metric, e.g. `"acc"` (bird) or `"conf_ap"` (YOLO): higher is better.
 4. **Save on improvement:** if it improved, saves the checkpoint (`--save`)
    and the run's `best.pt`.
+   With `save_every_epoch` (YOLO: `--save-every-epoch`), every epoch is also
+   saved as `<model_name>_epochNNN`, improved or not.
 5. **Log:** prints one summary line and appends a row to the run log.
 6. **Schedule:** steps the optional `scheduler` (YOLO uses `warmup_cosine`).
 
@@ -938,6 +941,11 @@ DataLoader passes for different worker counts.
 - **Save path:** `<save_dir>/<model_name>/<model_name>`, for example
   `saved_models/face_detection_yolo/face_detection_yolo`. Only the **best**
   epoch is kept, and each improvement overwrites the file.
+- **Every epoch (YOLO, `--save-every-epoch`):** each epoch is additionally
+  saved next to it as `<model_name>_epochNNN`, e.g.
+  `saved_models/face_detection_yolo/face_detection_yolo_epoch042`. Load one with
+  `--checkpoint`. Each file is the model's full size, so 200 epochs of c32
+  take about 2.7 GB.
 - **Format:** a plain `state_dict` (`torch.save(model.state_dict())`).
 - **Loading:** `torch.load(..., weights_only=True, map_location=device)`, then
   `eval()`. Weights trained on a GPU therefore load on a CPU-only machine.

api/src/common/checkpoints.py

@@ -3,13 +3,16 @@ from pathlib import Path
 import torch
 
 
-def checkpoint_path(save_dir: str | Path, model_name: str) -> Path:
-    # Layout kept from the original code: <save_dir>/<name>/<name>
-    return Path(save_dir) / model_name / model_name
+def checkpoint_path(save_dir: str | Path, model_name: str, epoch: int | None = None) -> Path:
+    # Layout kept from the original code: <save_dir>/<name>/<name>,
+    # per-epoch checkpoints next to it as <name>_epoch007
+    file_name = model_name if epoch is None else f"{model_name}_epoch{epoch:03d}"
+    return Path(save_dir) / model_name / file_name
 
 
-def save_checkpoint(model: torch.nn.Module, save_dir: str | Path, model_name: str) -> Path:
-    path = checkpoint_path(save_dir, model_name)
+def save_checkpoint(model: torch.nn.Module, save_dir: str | Path, model_name: str,
+                    epoch: int | None = None) -> Path:
+    path = checkpoint_path(save_dir, model_name, epoch)
     path.parent.mkdir(parents=True, exist_ok=True)
     torch.save(model.state_dict(), path)
     return path

api/src/common/training.py

@@ -112,6 +112,7 @@ def fit(
     val_metrics=None,               # f(preds, targets) -> dict of extra metrics over the whole val set
     config=None,                    # the task's Config; enables the run log
     scheduler=None,                 # LR scheduler, stepped once per epoch
+    save_every_epoch: bool = False, # with save: also keep every epoch as <model_name>_epochNNN
 ):
     run = None
     if config is not None and config.runs_dir:
@@ -120,7 +121,7 @@ def fit(
     try:
         best = _fit_epochs(model, loss_fn, optimizer, train_loader, val_loader, device, epochs,
                            save_dir, model_name, save, monitor, track_accuracy, prepare_batch, val_metrics, run,
-                           scheduler)
+                           scheduler, save_every_epoch)
     except KeyboardInterrupt:
         if run:
             run.finish("interrupted")
@@ -136,7 +137,7 @@ def fit(
 
 def _fit_epochs(model, loss_fn, optimizer, train_loader, val_loader, device, epochs,
                 save_dir, model_name, save, monitor, track_accuracy, prepare_batch, val_metrics, run,
-                scheduler):
+                scheduler, save_every_epoch):
     best = math.inf if monitor == "loss" else -math.inf
 
     for epoch in range(1, epochs + 1):
@@ -153,6 +154,8 @@ def _fit_epochs(model, loss_fn, optimizer, train_loader, val_loader, device, epo
                 save_checkpoint(model, save_dir, model_name)
             if run:
                 run.save_weights(model)
+        if save and save_every_epoch:
+            save_checkpoint(model, save_dir, model_name, epoch=epoch)
         if run:
             run.log_epoch(epoch, train, val, lr=optimizer.param_groups[0]["lr"],
                           seconds=time.perf_counter() - start, improved=improved)
@@ -166,6 +169,8 @@ def _fit_epochs(model, loss_fn, optimizer, train_loader, val_loader, device, epo
         parts += [f"val {k}: {v:.3f}" for k, v in val.items() if k not in ("loss", "acc")]
         if improved and save:
             parts.append("saved")
+        if save and save_every_epoch:
+            parts.append(f"kept epoch {epoch}")
         print(" | ".join(parts))
 
     return best

api/src/yolo/config.py

@@ -45,6 +45,7 @@ class Config:
     save_dir: str = "saved_models"
     model_name: str = "face_detection_yolo"
     save: bool = True
+    save_every_epoch: bool = False    # with --save: also keep every epoch as <model_name>_epochNNN
 
     # Run log (src/common/run_log.py): one folder per training run
     runs_dir: str = "runs"            # empty = no run log

api/src/yolo/tasks.py

@@ -66,6 +66,7 @@ def train(cfg: Config):
 
     fit(model, YoloLoss(), optimizer, train_loader, val_loader, device,
         epochs=cfg.epochs, save_dir=cfg.save_dir, model_name=cfg.model_name, save=cfg.save,
+        save_every_epoch=cfg.save_every_epoch,
         monitor="conf_ap", val_metrics=detection_metrics, config=cfg, scheduler=scheduler)
 
 

caddy/Caddyfile

@@ -13,10 +13,6 @@
 	log
 }
 
-www.marvinkrausser.com {
-	redir https://marvinkrausser.com{uri} permanent
-}
-
 marvinkrausser.com {
 	import common
 	reverse_proxy cnn_website:8080