Update README.md

README.md

@@ -29,6 +29,42 @@ The model was pretrained using **SimCLR with a ResNet-18 backbone** and served a
 
 Training was implemented using the [**Lightly** self-supervised learning framework](https://docs.lightly.ai/self-supervised-learning/index.html). The training images were obtained from the [`clane9/imagenet-100`](https://huggingface.co/datasets/clane9/imagenet-100) dataset on Hugging Face.
 
+### 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-100 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.
+
 ## Related Models
 
 This repository is associated with the **[Eccentricity-Constrained SimCLR Models (VEDB)](https://hf.co/collections/DM-Diaz/eccentricity-constrained-simclr-models-vedb)** collection.