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README.md

@@ -26,9 +26,61 @@ and evaluation workflows.
 
 The source code, model configuration, and usage instructions are available on
 [GitHub](https://github.com/FlorindoDev/OutfitTransformer).
-It includes the **cp_precomputed-flashionclip** (Compatibility Prediction) and
-**cir_precomputed-flashionclip** (Complementary Item Retrieval) runs, both on the Polyvore
-`nondisjoint` subset, together with train, validation, and test embeddings.
+It includes **cp_precomputed-flashionclip** (Compatibility Prediction) and
+**cir_precomputed-flashionclip** (Complementary Item Retrieval) checkpoints for
+both Polyvore subsets, `nondisjoint` and `disjoint`. FashionCLIP embeddings
+are available for both subsets, with Marqo FashionSigLIP embeddings also
+included for `nondisjoint`. Each cache has train, validation, and test splits.
+
+## Results
+
+Results on Polyvore, split `test`, with precomputed embeddings, grouped by
+`nondisjoint` and `disjoint` subsets. Metrics use a 0–1 scale, rounded to four
+decimal places.
+
+### Nondisjoint
+
+| Metric / run detail | CP |
+|---|---:|
+| Checkpoint epoch | 76 |
+| Evaluated examples | 20000 |
+| Accuracy | 0.8478 |
+| Precision | 0.9489 |
+| Recall | 0.7352 |
+| F1 | 0.8285 |
+| ROC AUC | 0.9513 |
+
+| Metric / run detail | CIR FITB |
+|---|---:|
+| Checkpoint epoch | 19 |
+| Evaluated examples | 10000 |
+| FITB accuracy | 0.6807 |
+| MRR | 0.8169 |
+| Recall@2 | 0.8782 |
+
+CIR tests were run without `category_emb`.
+
+### Disjoint
+
+| Metric / run detail | CP |
+|---|---:|
+| Checkpoint epoch | 76 |
+| Evaluated examples | 30290 |
+| Accuracy | 0.6904 |
+| Precision | 0.9816 |
+| Recall | 0.3880 |
+| F1 | 0.5561 |
+| ROC AUC | 0.9202 |
+
+| Metric / run detail | CIR FITB |
+|---|---:|
+| Checkpoint epoch | 22 |
+| Evaluated examples | 15145 |
+| FITB accuracy | 0.6640 |
+| MRR | 0.8063 |
+| Recall@2 | 0.8664 |
+
+CIR tests were run without `category_emb`.
 
 ## Contents and organization
 
@@ -40,55 +92,87 @@ individual epoch checkpoints in `epochs/`, and plots in `plots/`.
 ├── README.md
 ├── .gitattributes
 ├── checkpoints/
-│   └── nondisjoint/
+│   ├── nondisjoint/
+│   │   ├── cp_precomputed-flashionclip/
+│   │   └── cir_precomputed-flashionclip/
+│   └── disjoint/
 │       ├── cp_precomputed-flashionclip/
 │       └── cir_precomputed-flashionclip/
 └── precomputed_embeddings/
-    └── nondisjoint/
-        └── fashionCLIP/
-            ├── train/                  # manifest.json + shard-*.pt
-            ├── validation/             # manifest.json + shard-*.pt
-            └── test/                   # manifest.json + shard-*.pt
+    ├── fashionCLIP/
+    │   ├── nondisjoint/
+    │   │   ├── train/
+    │   │   ├── validation/
+    │   │   └── test/
+    │   └── disjoint/
+    │       ├── train/
+    │       ├── validation/
+    │       └── test/
+    └── fashionSigLIP/
+        └── nondisjoint/
+            ├── train/
+            ├── validation/
+            └── test/
 ```
 
 ## Parameters and hyperparameters of the published runs
 
-The following values come from the saved configurations for
-[cp_precomputed-flashionclip](checkpoints/nondisjoint/cp_precomputed-flashionclip/config.json) and
-[cir_precomputed-flashionclip](checkpoints/nondisjoint/cir_precomputed-flashionclip/config.json), compared
-with `run_config` in their respective `best.pt` files and latest available
-checkpoints. Descriptions follow the project documentation; the values shown
-are those of the published experiments. `null` indicates an unset value;
+Values come from the four saved configurations, checked against `run_config`
+in their respective `best.pt` files. Progress and validation metrics come from
+the local checkpoints; test results are listed separately above. Shared tables
+apply to both subsets unless stated otherwise. `null` indicates an unset value;
 **not recorded** indicates a field absent from the saved configuration.
 
+| Subset | CP | CIR |
+| --- | --- | --- |
+| `nondisjoint` | [config.json](checkpoints/nondisjoint/cp_precomputed-flashionclip/config.json) | [config.json](checkpoints/nondisjoint/cir_precomputed-flashionclip/config.json) |
+| `disjoint` | [config.json](checkpoints/disjoint/cp_precomputed-flashionclip/config.json) | [config.json](checkpoints/disjoint/cir_precomputed-flashionclip/config.json) |
+
 ### Checkpoints and recorded progress
 
-| Parameter | cp_precomputed-flashionclip | cir_precomputed-flashionclip |
+#### Nondisjoint
+
+| Parameter | CP | CIR |
 | --- | --- | --- |
 | Run folder | `checkpoints/nondisjoint/cp_precomputed-flashionclip/` | `checkpoints/nondisjoint/cir_precomputed-flashionclip/` |
 | Checkpoint format version (`checkpoint_schema_version`) | `2` | `1` |
 | Configured maximum epochs (`training.epochs`) | `200` | `200` |
-| Available epoch checkpoints | `81`, epochs `1–81` | `42`, epochs `1–42` |
-| Latest available checkpoint | `epochs/cp_epoch_081.pt` | `epochs/cir_epoch_042.pt` |
-| Epoch of `best.pt` | `73` | `17` |
-| Selection metric (`training.best_metric`) | `val_auc`, maximize | `val_fitb_accuracy`, maximize |
-| Metric value in the best checkpoint | AUC `0.95082516` | FITB accuracy `0.6854` (`68.54%`) |
+| Available epoch checkpoints | `77` (`1–77`) | `24` (`1–24`) |
+| Latest available checkpoint | `epochs/cp_epoch_077.pt` | `epochs/cir_epoch_024.pt` |
+| Epoch of `best.pt` | `76` | `19` |
+| Selection metric (`training.best_metric`) | `val_auc` | `val_fitb_accuracy` |
+| Validation metric in the best checkpoint | `0.95184712` | `0.6830` |
 | Training examples in the best epoch | `106612` | `53248` |
 | Validation examples in the best epoch | `10000` | `5000` |
 
-The 200 epochs and the total number of optimizer steps listed below describe
-the configured plan. Available checkpoints document training through epochs
-81 and 42 respectively; they do not establish that all 200 epochs were completed.
+#### Disjoint
+
+| Parameter | CP | CIR |
+| --- | --- | --- |
+| Run folder | `checkpoints/disjoint/cp_precomputed-flashionclip/` | `checkpoints/disjoint/cir_precomputed-flashionclip/` |
+| Checkpoint format version (`checkpoint_schema_version`) | `2` | `1` |
+| Configured maximum epochs (`training.epochs`) | `200` | `200` |
+| Available epoch checkpoints | `27` (`50–76`) | `22` (`1–22`) |
+| Latest available checkpoint | `epochs/cp_epoch_076.pt` | `epochs/cir_epoch_022.pt` |
+| Epoch of `best.pt` | `76` | `22` |
+| Selection metric (`training.best_metric`) | `val_auc` | `val_fitb_accuracy` |
+| Validation metric in the best checkpoint | `0.93390839` | `0.6723` |
+| Training examples in the best epoch | `33990` | `16960` |
+| Validation examples in the best epoch | `6000` | `3000` |
+
+Selection metrics are maximized. The 200 epochs and total optimizer steps
+describe the configured plan; available files document only the epochs
+listed in the tables.
 
 ### Dataset and features
 
-The values in this table are shared by both runs.
+The values apply to all four runs; each run uses its own subset.
 
 | Parameter | Recorded value |
 | --- | --- |
 | Dataset (`dataset.name`) | `polyvore` |
 | Dataset identifier (`dataset.id`) | `mvasil/polyvore-outfits` |
-| Subset (`dataset.subset`) | `nondisjoint` |
+| Subset (`dataset.subset`) | `nondisjoint` / `disjoint` |
 | Dataset directory during training (`dataset.dataset_root`) | `datasets/polyvore-outfits` |
 | Custom dataset cache (`dataset.cache_dir`) | `null` |
 | Embedding directory during training (`dataset.embedding_root`) | `precomputed_embeddings/patrickjohncyh-fashion-clip` |
@@ -98,10 +182,10 @@ The values in this table are shared by both runs.
 | Encoder identified in the manifests | `patrickjohncyh/fashion-clip` |
 | Trainable features (`features.trainable`) | `false`: the encoders are not trained in these runs |
 
-The recorded embedding path is the one used during training. In this repository,
-the caches are stored directly under `precomputed_embeddings/nondisjoint/fashionCLIP/<split>/`.
-For training, arrange the downloaded splits as `<embedding-root>/nondisjoint/<split>/`
-and set `--embedding-root` to that root directory.
+The recorded embedding path is the one used during training. Current caches
+are stored under `precomputed_embeddings/fashionCLIP/<subset>/<split>/`.
+Set `--embedding-root <download-dir>/precomputed_embeddings/fashionCLIP` when
+using the downloaded repository; the loader selects the subset and split.
 
 ### Architecture
 
@@ -123,7 +207,7 @@ the two modalities.
 | L2 normalization epsilon (`normalization_epsilon`) | `1e-12` | `1e-12` |
 | Embedding initialization standard deviation (`embedding_initialization_std`) | `0.02` | `0.02` |
 | Maximum number of items (`max_items`) | `16` | `16` |
-| Additional final LayerNorm, verified in the weights | Present: `cp.encoder.norm.*` | Absent: no `cir.encoder.norm.*` keys |
+| Additional final LayerNorm, verified in the weights | Absent: no `cp.encoder.norm.*` keys | Absent: no `cir.encoder.norm.*` keys |
 | Retrieval embedding dimension (`model.cir.embedding_dim`) | Not applicable | `128` |
 | L2 normalization of final retrieval embeddings (`model.cir.normalize_embeddings`) | Not applicable | `false` |
 | Category embedding (`model.use_category_embedding`) | Not applicable | `false` |
@@ -151,23 +235,27 @@ the two modalities.
 | Early stopping patience (`training.early_stopping_patience`) | `null`: disabled | `null`: disabled |
 | Minimum improvement (`training.early_stopping_min_delta`) | `0.0` | `0.0` |
 | Resume weights (`training.resume_weights`) | `null` | `null` |
-| Initialization from CP (`training.pretrained_cp_weights`) | Not applicable | `best.pt` |
-| Optimizer steps per epoch (`training.optimizer_steps_per_epoch`) | `53` | `208` |
-| Total planned optimizer steps (`training.total_optimizer_steps`) | `10600` | `41600` |
+| Initialization from CP (`training.pretrained_cp_weights`) | Not applicable | Subset-specific paths below |
+| Optimizer steps per epoch (`training.optimizer_steps_per_epoch`) | nondisjoint: `53`; disjoint: `17` | nondisjoint: `208`; disjoint: `67` |
+| Total planned optimizer steps (`training.total_optimizer_steps`) | nondisjoint: `10600`; disjoint: `3400` | nondisjoint: `41600`; disjoint: `13400` |
 
 For CIR, the target is an item chosen at random on every access to an outfit.
 As described in the training README, the loss selects the hardest negative
 among the positives from the other rows in the same microbatch: accumulating
 up to 256 examples does not expand the negative pool of the 64-example microbatch.
 
-CIR initialization records only the name `best.pt` of the source CP checkpoint,
-without its full path or hash. This alone does not uniquely identify that file
-as the `cp_precomputed-flashionclip/best.pt` published here.
+CIR configurations record these CP initialization paths:
+
+- `nondisjoint`: `checkpoints/nondisjoint/cp_precomputed/best.pt`.
+- `disjoint`: `checkpoints/disjoint/cp_fashionclip/best.pt`.
+
+These paths describe the training layout; update them to the corresponding
+local CP checkpoint when reproducing a run.
 
 ### Scheduler
 
 All fields in this table are saved under `training.scheduler` and have the same
-values in both runs.
+values in all four runs.
 
 | Parameter | Value |
 | --- | --- |
@@ -193,7 +281,7 @@ values in both runs.
 | Distributed backend (`runtime.distributed_backend`) | Not recorded | `null` |
 | Mixed precision (`runtime.mixed_precision`) | Not recorded | `false` |
 
-The tables cover all fields present in the two `config.json` files. Implicit
+The tables cover the fields present in the four `config.json` files. Implicit
 settings that were not serialized, such as AdamW betas and epsilon, and details
 such as the GPU model and software versions are not documented by the inspected
 artifacts.
@@ -213,9 +301,9 @@ resuming in this project initializes a new run from the saved weights.
 To load a checkpoint, use the loaders and commands from the source repository
 with the configuration and architecture version used during training. It is
 also useful to record the Git commit of the code used for each experiment.
-The CP checkpoint published here contains `cp.encoder.norm.*`, which is absent
-from the current CP architecture. Handling these checkpoints for CIR
-initialization is described in the
+The four checkpoints have no additional final Transformer LayerNorm
+(`cp.encoder.norm.*` or `cir.encoder.norm.*`). Initialization of CIR from CP
+is described in the
 [CIR README](https://github.com/FlorindoDev/OutfitTransformer/blob/main/training/CIR/README.md).
 
 Uploading to the Hub preserves the project's format; it does not automatically
@@ -233,51 +321,73 @@ manifest**, kept in the same folder. Shards contain `schema_version`, the
 `item_ids` identifiers, and the `embeddings` tensor; the manifest describes
 the encoder, dataset, subset, split, dimensions, precision, fingerprint, and shards.
 
-In the current procedure, each embedding concatenates the image and text
-representations, each normalized separately with L2 normalization. With
-FashionCLIP and the default configuration, each vector has 1024 components:
-512 visual and 512 textual. The actual cache values are documented in its manifest.
+Each embedding concatenates image and text representations, each normalized
+separately with L2 normalization. FashionCLIP produces 1024 components
+(512 visual + 512 textual); Marqo FashionSigLIP produces 1536 (768 + 768).
+The published checkpoints use FashionCLIP. FashionSigLIP caches require
+models configured for their 1536-dimensional inputs.
 
 ### Parameters of the published caches
 
-| Parameter | Value in the manifests |
-| --- | --- |
-| Format version (`schema_version`) | `2` |
-| Dataset / subset | `mvasil/polyvore-outfits` / `nondisjoint` |
-| Encoder model (`encoder.model_name`) | `patrickjohncyh/fashion-clip` |
-| Visual encoder (`encoder.visual_encoder`) | `FashionCLIPVisualEncoder` |
-| Text encoder (`encoder.text_encoder`) | `FashionCLIPTextEncoder` |
-| Dimension per modality (`encoder.modality_dim`) | `512` |
-| Final dimension (`embedding_dim`) | `1024` |
-| Aggregation (`encoder.aggregation`) | `concat_visual_then_text` |
-| Normalization (`encoder.normalization`) | `l2_per_modality` |
-| Saved precision (`dtype`) | `float32` |
-| Item count limit (`limit`) | `null` |
-| Visual / text encoder commit (`encoder.visual_commit`, `encoder.text_commit`) | `null` / `null` |
+| Parameter | FashionCLIP | Marqo FashionSigLIP |
+| --- | --- | --- |
+| Format version (`schema_version`) | `2` | `2` |
+| Dataset | `mvasil/polyvore-outfits` | `mvasil/polyvore-outfits` |
+| Available subsets | `nondisjoint`, `disjoint` | `nondisjoint` |
+| Encoder model (`encoder.model_name`) | `patrickjohncyh/fashion-clip` | `Marqo/marqo-fashionSigLIP` |
+| Visual encoder (`encoder.visual_encoder`) | `FashionCLIPVisualEncoder` | `MarqoFashionSigLIPVisualEncoder` |
+| Text encoder (`encoder.text_encoder`) | `FashionCLIPTextEncoder` | `MarqoFashionSigLIPTextEncoder` |
+| Dimension per modality (`encoder.modality_dim`) | `512` | `768` |
+| Final dimension (`embedding_dim`) | `1024` | `1536` |
+| Aggregation (`encoder.aggregation`) | `concat_visual_then_text` | `concat_visual_then_text` |
+| Normalization (`encoder.normalization`) | `l2_per_modality` | `l2_per_modality` |
+| Saved precision (`dtype`) | `float32` | `float32` |
+| Item count limit (`limit`) | `null` | `null` |
+| Visual / text encoder commit | `null` / `null` | `c56244cc94f92419e8369fa71efdaf403b124ce8` / `c56244cc94f92419e8369fa71efdaf403b124ce8` |
+
+#### fashionCLIP / nondisjoint
+
+| Split | Items (`count`) | Shards | Manifest |
+| --- | --- | --- | --- |
+| Train | `204679` | `21` | [train/manifest.json](precomputed_embeddings/fashionCLIP/nondisjoint/train/manifest.json) |
+| Validation | `25132` | `3` | [validation/manifest.json](precomputed_embeddings/fashionCLIP/nondisjoint/validation/manifest.json) |
+| Test | `47854` | `5` | [test/manifest.json](precomputed_embeddings/fashionCLIP/nondisjoint/test/manifest.json) |
+
+#### fashionCLIP / disjoint
 
 | Split | Items (`count`) | Shards | Manifest |
 | --- | --- | --- | --- |
-| Train | `204679` | `21` | [train/manifest.json](precomputed_embeddings/nondisjoint/fashionCLIP/train/manifest.json) |
-| Validation | `25132` | `3` | [validation/manifest.json](precomputed_embeddings/nondisjoint/fashionCLIP/validation/manifest.json) |
-| Test | `47854` | `5` | [test/manifest.json](precomputed_embeddings/nondisjoint/fashionCLIP/test/manifest.json) |
+| Train | `71967` | `8` | [train/manifest.json](precomputed_embeddings/fashionCLIP/disjoint/train/manifest.json) |
+| Validation | `14657` | `2` | [validation/manifest.json](precomputed_embeddings/fashionCLIP/disjoint/validation/manifest.json) |
+| Test | `70035` | `8` | [test/manifest.json](precomputed_embeddings/fashionCLIP/disjoint/test/manifest.json) |
 
-Each shard contains 10000 items, except the last shard in each split: 4679 for
-train, 5132 for validation, and 7854 for test. These counts refer to items with
-embeddings, not outfits or training examples.
+#### fashionSigLIP / nondisjoint
 
-Train and validation share the `model_fingerprint`
+| Split | Items (`count`) | Shards | Manifest |
+| --- | --- | --- | --- |
+| Train | `204679` | `21` | [train/manifest.json](precomputed_embeddings/fashionSigLIP/nondisjoint/train/manifest.json) |
+| Validation | `25132` | `3` | [validation/manifest.json](precomputed_embeddings/fashionSigLIP/nondisjoint/validation/manifest.json) |
+| Test | `47854` | `5` | [test/manifest.json](precomputed_embeddings/fashionSigLIP/nondisjoint/test/manifest.json) |
+
+Shards contain at most 10000 items each; the last shard may be smaller.
+Counts refer to items with embeddings.
+
+FashionCLIP disjoint manifests and nondisjoint train/validation share the
+`model_fingerprint`
 `dfcff17f9fb9335b22792d610cb7e4ba8cd73afa2954b725aa5e2d993bc10c27`.
-The test cache instead records
+FashionCLIP nondisjoint test instead records
 `b00590d05bc4af1a3a5316ecb65dabea7315fa93adf0dcbdbec0b8399ec1e788`.
-The encoder name and dimensions match, but the fingerprints differ: these
-metadata do not establish that the precomputation configuration is identical
-between test and the other splits.
-
-Use embeddings consistent with the checkpoint and the required dataset,
-subset, and split. Training workflows also verify that encoder fingerprints
-match between train and validation caches. Caches contain identifiers and
-vectors; the data needed to construct outfits, labels, and queries are still
-those required by the project.
+All three FashionSigLIP caches share
+`333a9948b0d676879fbac0938f4f8654eae108dbc00cfbf3f59552c051ac8802`.
+Matching encoder names and dimensions do not establish identical
+precomputation configurations when fingerprints differ.
+
+Use `--embedding-root <download-dir>/precomputed_embeddings/fashionCLIP` or
+`--embedding-root <download-dir>/precomputed_embeddings/fashionSigLIP`, according
+to the model. The loader reads `<embedding-root>/<subset>/<split>/`.
+Use caches matching the checkpoint, dataset, subset, and split; train and
+validation must have matching fingerprints. The data needed to construct
+outfits, labels, and queries are still those required by the project.
 
 ## References
 
@@ -301,9 +411,61 @@ training e valutazione del progetto.
 
 Il codice sorgente, la configurazione del modello e le istruzioni di utilizzo sono
 disponibili su [GitHub](https://github.com/FlorindoDev/OutfitTransformer).
-Sono inclusi i run **cp_precomputed-flashionclip** (Compatibility Prediction) e
-**cir_precomputed-flashionclip** (Complementary Item Retrieval), entrambi sul subset
-`nondisjoint` di Polyvore, insieme agli embedding di train, validation e test.
+Sono inclusi i checkpoint **cp_precomputed-flashionclip** (Compatibility Prediction)
+e **cir_precomputed-flashionclip** (Complementary Item Retrieval) per entrambi
+i subset Polyvore, `nondisjoint` e `disjoint`. Gli embedding FashionCLIP sono
+disponibili per entrambi i subset; sono inclusi anche gli embedding Marqo
+FashionSigLIP per `nondisjoint`. Ogni cache contiene train, validation e test.
+
+## Risultati
+
+Risultati su Polyvore, split `test`, con embedding precomputati, suddivisi per
+subset `nondisjoint` e `disjoint`. Metriche su scala 0–1, arrotondate a quattro
+cifre decimali.
+
+### Nondisjoint
+
+| Metrica / dettaglio esecuzione | CP |
+|---|---:|
+| Epoca del checkpoint | 76 |
+| Esempi valutati | 20000 |
+| Accuracy | 0.8478 |
+| Precision | 0.9489 |
+| Recall | 0.7352 |
+| F1 | 0.8285 |
+| ROC AUC | 0.9513 |
+
+| Metrica / dettaglio esecuzione | CIR FITB |
+|---|---:|
+| Epoca del checkpoint | 19 |
+| Esempi valutati | 10000 |
+| FITB accuracy | 0.6807 |
+| MRR | 0.8169 |
+| Recall@2 | 0.8782 |
+
+I test CIR sono stati eseguiti senza `category_emb`.
+
+### Disjoint
+
+| Metrica / dettaglio esecuzione | CP |
+|---|---:|
+| Epoca del checkpoint | 76 |
+| Esempi valutati | 30290 |
+| Accuracy | 0.6904 |
+| Precision | 0.9816 |
+| Recall | 0.3880 |
+| F1 | 0.5561 |
+| ROC AUC | 0.9202 |
+
+| Metrica / dettaglio esecuzione | CIR FITB |
+|---|---:|
+| Epoca del checkpoint | 22 |
+| Esempi valutati | 15145 |
+| FITB accuracy | 0.6640 |
+| MRR | 0.8063 |
+| Recall@2 | 0.8664 |
+
+I test CIR sono stati eseguiti senza `category_emb`.
 
 ## Contenuto e organizzazione
 
@@ -316,55 +478,88 @@ Gli artefatti sono organizzati come segue. Ogni run contiene `best.pt`,
 ├── README.md
 ├── .gitattributes
 ├── checkpoints/
-│   └── nondisjoint/
+│   ├── nondisjoint/
+│   │   ├── cp_precomputed-flashionclip/
+│   │   └── cir_precomputed-flashionclip/
+│   └── disjoint/
 │       ├── cp_precomputed-flashionclip/
 │       └── cir_precomputed-flashionclip/
 └── precomputed_embeddings/
-    └── nondisjoint/
-        └── fashionCLIP/
-            ├── train/                  # manifest.json + shard-*.pt
-            ├── validation/             # manifest.json + shard-*.pt
-            └── test/                   # manifest.json + shard-*.pt
+    ├── fashionCLIP/
+    │   ├── nondisjoint/
+    │   │   ├── train/
+    │   │   ├── validation/
+    │   │   └── test/
+    │   └── disjoint/
+    │       ├── train/
+    │       ├── validation/
+    │       └── test/
+    └── fashionSigLIP/
+        └── nondisjoint/
+            ├── train/
+            ├── validation/
+            └── test/
 ```
 
 ## Parametri e iperparametri dei run pubblicati
 
-I valori seguenti provengono dalle configurazioni salvate nei run
-[cp_precomputed-flashionclip](checkpoints/nondisjoint/cp_precomputed-flashionclip/config.json) e
-[cir_precomputed-flashionclip](checkpoints/nondisjoint/cir_precomputed-flashionclip/config.json), confrontate
-con `run_config` nei rispettivi `best.pt` e nell'ultimo checkpoint disponibile.
-Le descrizioni seguono la documentazione del progetto; i valori riportati sono
-quelli degli esperimenti pubblicati. `null` indica un valore non impostato;
-**non registrato** indica un campo assente dalla configurazione salvata.
+I valori provengono dalle quattro configurazioni salvate, verificate con
+`run_config` nei rispettivi `best.pt`. Avanzamento e metriche di validation
+provengono dai checkpoint locali; i risultati di test sono riportati sopra.
+Le tabelle comuni valgono per entrambi i subset salvo indicazione diversa.
+`null` indica un valore non impostato; **non registrato** indica un campo
+assente dalla configurazione salvata.
+
+| Subset | CP | CIR |
+| --- | --- | --- |
+| `nondisjoint` | [config.json](checkpoints/nondisjoint/cp_precomputed-flashionclip/config.json) | [config.json](checkpoints/nondisjoint/cir_precomputed-flashionclip/config.json) |
+| `disjoint` | [config.json](checkpoints/disjoint/cp_precomputed-flashionclip/config.json) | [config.json](checkpoints/disjoint/cir_precomputed-flashionclip/config.json) |
 
 ### Checkpoint e avanzamento registrato
 
-| Parametro | cp_precomputed-flashionclip | cir_precomputed-flashionclip |
+#### Nondisjoint
+
+| Parametro | CP | CIR |
 | --- | --- | --- |
 | Cartella del run | `checkpoints/nondisjoint/cp_precomputed-flashionclip/` | `checkpoints/nondisjoint/cir_precomputed-flashionclip/` |
 | Versione del formato checkpoint (`checkpoint_schema_version`) | `2` | `1` |
 | Epoche massime configurate (`training.epochs`) | `200` | `200` |
-| Checkpoint di epoca disponibili | `81`, epoche `1–81` | `42`, epoche `1–42` |
-| Ultimo checkpoint disponibile | `epochs/cp_epoch_081.pt` | `epochs/cir_epoch_042.pt` |
-| Epoca di `best.pt` | `73` | `17` |
-| Metrica di selezione (`training.best_metric`) | `val_auc`, da massimizzare | `val_fitb_accuracy`, da massimizzare |
-| Valore della metrica nel best | AUC `0.95082516` | FITB accuracy `0.6854` (`68.54%`) |
+| Checkpoint di epoca disponibili | `77` (`1–77`) | `24` (`1–24`) |
+| Ultimo checkpoint disponibile | `epochs/cp_epoch_077.pt` | `epochs/cir_epoch_024.pt` |
+| Epoca di `best.pt` | `76` | `19` |
+| Metrica di selezione (`training.best_metric`) | `val_auc` | `val_fitb_accuracy` |
+| Metrica di validation nel best | `0.95184712` | `0.6830` |
 | Esempi train nell'epoca del best | `106612` | `53248` |
 | Esempi validation nell'epoca del best | `10000` | `5000` |
 
-Le 200 epoche e il numero totale di optimizer step riportato sotto descrivono il
-piano configurato. I checkpoint disponibili documentano il training fino alle
-epoche 81 e 42 rispettivamente; non attestano il completamento delle 200 epoche.
+#### Disjoint
+
+| Parametro | CP | CIR |
+| --- | --- | --- |
+| Cartella del run | `checkpoints/disjoint/cp_precomputed-flashionclip/` | `checkpoints/disjoint/cir_precomputed-flashionclip/` |
+| Versione del formato checkpoint (`checkpoint_schema_version`) | `2` | `1` |
+| Epoche massime configurate (`training.epochs`) | `200` | `200` |
+| Checkpoint di epoca disponibili | `27` (`50–76`) | `22` (`1–22`) |
+| Ultimo checkpoint disponibile | `epochs/cp_epoch_076.pt` | `epochs/cir_epoch_022.pt` |
+| Epoca di `best.pt` | `76` | `22` |
+| Metrica di selezione (`training.best_metric`) | `val_auc` | `val_fitb_accuracy` |
+| Metrica di validation nel best | `0.93390839` | `0.6723` |
+| Esempi train nell'epoca del best | `33990` | `16960` |
+| Esempi validation nell'epoca del best | `6000` | `3000` |
+
+Le metriche di selezione sono da massimizzare. Le 200 epoche e gli optimizer
+step totali descrivono il piano configurato; i file disponibili documentano
+soltanto le epoche indicate in tabella.
 
 ### Dataset e feature
 
-I valori di questa tabella sono comuni ai due run.
+I valori valgono per tutti e quattro i run; ogni run usa il proprio subset.
 
 | Parametro | Valore registrato |
 | --- | --- |
 | Dataset (`dataset.name`) | `polyvore` |
 | Identificativo dataset (`dataset.id`) | `mvasil/polyvore-outfits` |
-| Subset (`dataset.subset`) | `nondisjoint` |
+| Subset (`dataset.subset`) | `nondisjoint` / `disjoint` |
 | Directory dataset nel training (`dataset.dataset_root`) | `datasets/polyvore-outfits` |
 | Cache dataset personalizzata (`dataset.cache_dir`) | `null` |
 | Directory embedding nel training (`dataset.embedding_root`) | `precomputed_embeddings/patrickjohncyh-fashion-clip` |
@@ -374,10 +569,10 @@ I valori di questa tabella sono comuni ai due run.
 | Encoder identificato nei manifest | `patrickjohncyh/fashion-clip` |
 | Feature allenabili (`features.trainable`) | `false`: gli encoder non vengono allenati in questi run |
 
-Il percorso degli embedding registrato è quello usato durante il training.
-In questa repository le cache si trovano direttamente in
-`precomputed_embeddings/nondisjoint/fashionCLIP/<split>/`. Per il training, disponi gli split scaricati come
-`<embedding-root>/nondisjoint/<split>/` e imposta `--embedding-root` su quella directory radice.
+Il percorso registrato è quello usato durante il training. Le cache attuali
+si trovano in `precomputed_embeddings/fashionCLIP/<subset>/<split>/`.
+Usa `--embedding-root <download-dir>/precomputed_embeddings/fashionCLIP` con la
+repository scaricata; il loader seleziona il subset e lo split.
 
 ### Architettura
 
@@ -399,7 +594,7 @@ concatenazione delle due modalità.
 | Epsilon normalizzazione L2 (`normalization_epsilon`) | `1e-12` | `1e-12` |
 | Deviazione standard inizializzazione embedding (`embedding_initialization_std`) | `0.02` | `0.02` |
 | Numero massimo di item (`max_items`) | `16` | `16` |
-| LayerNorm finale aggiuntiva, verificata nei pesi | Presente: `cp.encoder.norm.*` | Assente: nessuna chiave `cir.encoder.norm.*` |
+| LayerNorm finale aggiuntiva, verificata nei pesi | Assente: nessuna chiave `cp.encoder.norm.*` | Assente: nessuna chiave `cir.encoder.norm.*` |
 | Dimensione embedding di retrieval (`model.cir.embedding_dim`) | Non applicabile | `128` |
 | Normalizzazione L2 degli embedding finali di retrieval (`model.cir.normalize_embeddings`) | Non applicabile | `false` |
 | Embedding di categoria (`model.use_category_embedding`) | Non applicabile | `false` |
@@ -427,23 +622,27 @@ concatenazione delle due modalità.
 | Early stopping, patience (`training.early_stopping_patience`) | `null`: disabilitato | `null`: disabilitato |
 | Miglioramento minimo (`training.early_stopping_min_delta`) | `0.0` | `0.0` |
 | Pesi per resume (`training.resume_weights`) | `null` | `null` |
-| Inizializzazione da CP (`training.pretrained_cp_weights`) | Non applicabile | `best.pt` |
-| Optimizer step per epoca (`training.optimizer_steps_per_epoch`) | `53` | `208` |
-| Optimizer step totali pianificati (`training.total_optimizer_steps`) | `10600` | `41600` |
+| Inizializzazione da CP (`training.pretrained_cp_weights`) | Non applicabile | Percorsi per subset riportati sotto |
+| Optimizer step per epoca (`training.optimizer_steps_per_epoch`) | nondisjoint: `53`; disjoint: `17` | nondisjoint: `208`; disjoint: `67` |
+| Optimizer step totali pianificati (`training.total_optimizer_steps`) | nondisjoint: `10600`; disjoint: `3400` | nondisjoint: `41600`; disjoint: `13400` |
 
 Per CIR il target è un item scelto casualmente a ogni accesso all'outfit. Come
 descritto nel README del training, la loss seleziona il negativo più difficile
 tra i positivi delle altre righe dello stesso microbatch: l'accumulo fino a 256
 esempi non amplia il gruppo di negativi del microbatch da 64.
 
-L'inizializzazione CIR registra soltanto il nome `best.pt` del checkpoint CP
-sorgente, senza percorso completo o hash. Questo dato da solo non identifica
-univocamente il file come il `cp_precomputed-flashionclip/best.pt` qui pubblicato.
+Le configurazioni CIR registrano questi percorsi di inizializzazione da CP:
+
+- `nondisjoint`: `checkpoints/nondisjoint/cp_precomputed/best.pt`.
+- `disjoint`: `checkpoints/disjoint/cp_fashionclip/best.pt`.
+
+Questi percorsi descrivono il layout del training; aggiornali al checkpoint CP
+locale corrispondente quando riproduci un run.
 
 ### Scheduler
 
 Tutti i campi di questa tabella sono salvati in `training.scheduler` e hanno lo
-stesso valore nei due run.
+stesso valore nei quattro run.
 
 | Parametro | Valore |
 | --- | --- |
@@ -469,7 +668,7 @@ stesso valore nei due run.
 | Backend distribuito (`runtime.distributed_backend`) | Non registrato | `null` |
 | Mixed precision (`runtime.mixed_precision`) | Non registrata | `false` |
 
-Le tabelle coprono tutti i campi presenti nei due `config.json`. Impostazioni
+Le tabelle coprono i campi presenti nei quattro `config.json`. Impostazioni
 implicite non serializzate, come i beta e l'epsilon di AdamW, e dettagli quali
 modello di GPU e versioni software non sono documentati dagli artefatti letti.
 
@@ -489,9 +688,9 @@ del progetto inizializza un nuovo run a partire dai pesi salvati.
 Per caricare un checkpoint, usa i loader e i comandi della repository sorgente,
 con la configurazione e la versione dell'architettura usate durante il training.
 Per ciascun esperimento è utile riportare anche il commit Git del codice usato.
-Il checkpoint CP qui pubblicato contiene `cp.encoder.norm.*`, assente
-nell'architettura CP attuale. La gestione di questi checkpoint per
-l'inizializzazione di CIR è descritta nel
+I quattro checkpoint non contengono una LayerNorm finale aggiuntiva del
+Transformer (`cp.encoder.norm.*` o `cir.encoder.norm.*`). L'inizializzazione
+di CIR da CP è descritta nel
 [README CIR](https://github.com/FlorindoDev/OutfitTransformer/blob/main/training/CIR/README.md).
 
 Il caricamento sul Hub conserva il formato del progetto; non aggiunge
@@ -509,51 +708,73 @@ manifest**, mantenuti nella stessa cartella. Gli shard contengono `schema_versio
 gli identificativi `item_ids` e il tensore `embeddings`; il manifest descrive
 encoder, dataset, subset, split, dimensioni, precisione, fingerprint e shard.
 
-Nella procedura attuale, ogni embedding concatena le rappresentazioni di immagine
-e testo, normalizzate L2 separatamente. Con FashionCLIP e la configurazione
-predefinita, ogni vettore ha 1024 componenti: 512 visuali e 512 testuali.
-I valori effettivi della cache sono documentati nel relativo manifest.
+Ogni embedding concatena le rappresentazioni di immagine e testo, normalizzate
+L2 separatamente. FashionCLIP produce 1024 componenti (512 visuali + 512
+testuali); Marqo FashionSigLIP ne produce 1536 (768 + 768). I checkpoint
+pubblicati usano FashionCLIP. Le cache FashionSigLIP richiedono modelli
+configurati per input di dimensione 1536.
 
 ### Parametri delle cache pubblicate
 
-| Parametro | Valore nei manifest |
-| --- | --- |
-| Versione del formato (`schema_version`) | `2` |
-| Dataset / subset | `mvasil/polyvore-outfits` / `nondisjoint` |
-| Modello encoder (`encoder.model_name`) | `patrickjohncyh/fashion-clip` |
-| Encoder visuale (`encoder.visual_encoder`) | `FashionCLIPVisualEncoder` |
-| Encoder testuale (`encoder.text_encoder`) | `FashionCLIPTextEncoder` |
-| Dimensione per modalità (`encoder.modality_dim`) | `512` |
-| Dimensione finale (`embedding_dim`) | `1024` |
-| Aggregazione (`encoder.aggregation`) | `concat_visual_then_text` |
-| Normalizzazione (`encoder.normalization`) | `l2_per_modality` |
-| Precisione salvata (`dtype`) | `float32` |
-| Limite al numero di articoli (`limit`) | `null` |
-| Commit encoder visuale / testuale (`encoder.visual_commit`, `encoder.text_commit`) | `null` / `null` |
+| Parametro | FashionCLIP | Marqo FashionSigLIP |
+| --- | --- | --- |
+| Versione del formato (`schema_version`) | `2` | `2` |
+| Dataset | `mvasil/polyvore-outfits` | `mvasil/polyvore-outfits` |
+| Subset disponibili | `nondisjoint`, `disjoint` | `nondisjoint` |
+| Modello encoder (`encoder.model_name`) | `patrickjohncyh/fashion-clip` | `Marqo/marqo-fashionSigLIP` |
+| Encoder visuale (`encoder.visual_encoder`) | `FashionCLIPVisualEncoder` | `MarqoFashionSigLIPVisualEncoder` |
+| Encoder testuale (`encoder.text_encoder`) | `FashionCLIPTextEncoder` | `MarqoFashionSigLIPTextEncoder` |
+| Dimensione per modalità (`encoder.modality_dim`) | `512` | `768` |
+| Dimensione finale (`embedding_dim`) | `1024` | `1536` |
+| Aggregazione (`encoder.aggregation`) | `concat_visual_then_text` | `concat_visual_then_text` |
+| Normalizzazione (`encoder.normalization`) | `l2_per_modality` | `l2_per_modality` |
+| Precisione salvata (`dtype`) | `float32` | `float32` |
+| Limite al numero di articoli (`limit`) | `null` | `null` |
+| Commit encoder visuale / testuale | `null` / `null` | `c56244cc94f92419e8369fa71efdaf403b124ce8` / `c56244cc94f92419e8369fa71efdaf403b124ce8` |
+
+#### fashionCLIP / nondisjoint
+
+| Split | Articoli (`count`) | Shard | Manifest |
+| --- | --- | --- | --- |
+| Train | `204679` | `21` | [train/manifest.json](precomputed_embeddings/fashionCLIP/nondisjoint/train/manifest.json) |
+| Validation | `25132` | `3` | [validation/manifest.json](precomputed_embeddings/fashionCLIP/nondisjoint/validation/manifest.json) |
+| Test | `47854` | `5` | [test/manifest.json](precomputed_embeddings/fashionCLIP/nondisjoint/test/manifest.json) |
+
+#### fashionCLIP / disjoint
+
+| Split | Articoli (`count`) | Shard | Manifest |
+| --- | --- | --- | --- |
+| Train | `71967` | `8` | [train/manifest.json](precomputed_embeddings/fashionCLIP/disjoint/train/manifest.json) |
+| Validation | `14657` | `2` | [validation/manifest.json](precomputed_embeddings/fashionCLIP/disjoint/validation/manifest.json) |
+| Test | `70035` | `8` | [test/manifest.json](precomputed_embeddings/fashionCLIP/disjoint/test/manifest.json) |
+
+#### fashionSigLIP / nondisjoint
 
 | Split | Articoli (`count`) | Shard | Manifest |
 | --- | --- | --- | --- |
-| Train | `204679` | `21` | [train/manifest.json](precomputed_embeddings/nondisjoint/fashionCLIP/train/manifest.json) |
-| Validation | `25132` | `3` | [validation/manifest.json](precomputed_embeddings/nondisjoint/fashionCLIP/validation/manifest.json) |
-| Test | `47854` | `5` | [test/manifest.json](precomputed_embeddings/nondisjoint/fashionCLIP/test/manifest.json) |
+| Train | `204679` | `21` | [train/manifest.json](precomputed_embeddings/fashionSigLIP/nondisjoint/train/manifest.json) |
+| Validation | `25132` | `3` | [validation/manifest.json](precomputed_embeddings/fashionSigLIP/nondisjoint/validation/manifest.json) |
+| Test | `47854` | `5` | [test/manifest.json](precomputed_embeddings/fashionSigLIP/nondisjoint/test/manifest.json) |
 
-Gli shard contengono 10000 articoli ciascuno, tranne gli ultimi: 4679 per train,
-5132 per validation e 7854 per test. I conteggi indicano articoli con embedding,
-non outfit o esempi di training.
+Gli shard contengono al massimo 10000 articoli ciascuno; l’ultimo shard può
+essere più piccolo. I conteggi indicano articoli con embedding.
 
-Train e validation condividono il `model_fingerprint`
+I manifest FashionCLIP disjoint e quelli nondisjoint train/validation condividono
+il `model_fingerprint`
 `dfcff17f9fb9335b22792d610cb7e4ba8cd73afa2954b725aa5e2d993bc10c27`.
-La cache test registra invece
+FashionCLIP nondisjoint test registra invece
 `b00590d05bc4af1a3a5316ecb65dabea7315fa93adf0dcbdbec0b8399ec1e788`.
-Nome encoder e dimensioni coincidono, ma i fingerprint differiscono: questi
-metadati non attestano l'identità della configurazione di precomputazione tra
-test e gli altri split.
-
-Usa embedding coerenti con il checkpoint e con il dataset, il subset e lo split
-richiesti. I flussi di training verificano anche la corrispondenza del fingerprint
-degli encoder tra le cache di train e validation. Le cache contengono gli
-identificativi e i vettori; i dati necessari a costruire outfit, etichette e query
-restano quelli richiesti dal progetto.
+Le tre cache FashionSigLIP condividono
+`333a9948b0d676879fbac0938f4f8654eae108dbc00cfbf3f59552c051ac8802`.
+Nome encoder e dimensioni uguali non attestano una configurazione identica
+quando i fingerprint differiscono.
+
+Usa `--embedding-root <download-dir>/precomputed_embeddings/fashionCLIP` oppure
+`--embedding-root <download-dir>/precomputed_embeddings/fashionSigLIP`, secondo
+il modello. Il loader legge `<embedding-root>/<subset>/<split>/`.
+Usa cache coerenti con checkpoint, dataset, subset e split; train e validation
+devono avere fingerprint corrispondenti. I dati per costruire outfit,
+etichette e query restano quelli richiesti dal progetto.
 
 ## Riferimenti