Update README.md

README.md

@@ -103,6 +103,42 @@ The stored epoch is zero-indexed, so `epoch = 99` corresponds to the completion
 
 The checkpoint includes training state such as optimizer and scheduler information in addition to model parameters.
 
+### Comparative Evaluation Results
+
+The table below reproduces the summary metrics reported in the associated paper across all VEDB-trained conditions and **reference models**. **Rows corresponding to this repository's ImageNet-1K checkpoint are bolded.**
+
+| Task | Condition | Val Loss | Top-1 (%) | Top-5 (%) | Best Macro-F1 (%) |
+| --- | --- | ---: | ---: | ---: | ---: |
+| SimCLR | Baseline | 0.4331 | 87.60 | — | — |
+| SimCLR | Fovea-Gaze | 0.3749 | 90.43 | — | — |
+| SimCLR | Periph-NF | 0.4548 | 90.04 | — | — |
+| SimCLR | Periph | 0.4545 | 89.26 | — | — |
+| In-Domain | Baseline | 0.9811 | — | — | 42.17 |
+| In-Domain | Fovea-Gaze | 1.2031 | — | — | 43.64 |
+| In-Domain | Periph-NF | 1.3090 | — | — | 30.93 |
+| In-Domain | Periph | 1.0623 | — | — | 36.56 |
+| In-Domain | STL-10 | 1.6666 | — | — | 25.41 |
+| In-Domain | ImageNet-100 | 1.2342 | — | — | 41.23 |
+| **In-Domain** | **ImageNet-1K** | **0.9713** | **—** | **—** | **43.33** |
+| VGGFace2 | Baseline | 7.8101 | 5.21 | 11.73 | 3.26 |
+| VGGFace2 | Fovea-Gaze | 7.9104 | 4.58 | 10.76 | 2.70 |
+| VGGFace2 | Periph-NF | 8.0232 | 3.39 | 8.17 | 1.90 |
+| VGGFace2 | Periph | 8.1681 | 2.54 | 6.39 | 1.35 |
+| VGGFace2 | STL-10 | 6.9973 | 9.55 | 18.96 | 7.43 |
+| VGGFace2 | ImageNet-100 | 6.7985 | 10.77 | 21.07 | 8.71 |
+| **VGGFace2** | **ImageNet-1K** | **6.7964** | **10.74** | **21.08** | **8.77** |
+| Places365 | Baseline | 3.9690 | 25.63 | 51.90 | 23.16 |
+| Places365 | Fovea-Gaze | 4.2347 | 21.86 | 46.21 | 19.14 |
+| Places365 | Periph-NF | 4.2621 | 20.51 | 44.58 | 17.86 |
+| Places365 | Periph | 4.2671 | 20.26 | 44.10 | 17.65 |
+| Places365 | STL-10 | 3.8281 | 26.57 | 53.47 | 24.82 |
+| Places365 | ImageNet-100 | 3.9207 | 24.99 | 51.21 | 23.32 |
+| **Places365** | **ImageNet-1K** | **3.6264** | **30.17** | **58.46** | **28.36** |
+
+**Note:** SimCLR Top-1 is computed from the self-supervised contrastive objective and is not directly comparable to downstream supervised classification accuracy. For downstream tasks, the pretrained ResNet-18 backbone was **frozen** and only a linear classifier was trained; the backbone weights were **not fine-tuned**. Classifier checkpoints were selected by best validation Macro-F1. In-domain Top-1 accuracy is omitted because label imbalance across frames can make accuracy misleading; Macro-F1 is reported as the primary class-balanced metric. STL-10, ImageNet-100, and ImageNet-1K are treated as out-of-domain baselines because they were not pretrained on VEDB.
+
+For in-domain classification, Macro-F1 was used as the primary class-balanced metric because of label imbalance across VEDB frame categories.
+
 ## Intended Use
 
 This checkpoint is provided for research and downstream applications involving self-supervised visual representations, including: