Add model card

README.md

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+---
+base_model: microsoft/resnet-101
+library_name: transformers
+pipeline_tag: image-classification
+tags:
+  - probex
+  - model-j
+  - weight-space-learning
+---
+
+# Model-J: ResNet Model (model_idx_0292)
+
+This model is part of the **Model-J** dataset, introduced in:
+
+**Learning on Model Weights using Tree Experts** (CVPR 2025) by Eliahu Horwitz*, Bar Cavia*, Jonathan Kahana*, Yedid Hoshen
+
+<p align="center">
+    🌐 <a href="https://horwitz.ai/probex" target="_blank">Project</a> | 📃 <a href="https://arxiv.org/abs/2410.13569" target="_blank">Paper</a> | 💻 <a href="https://github.com/eliahuhorwitz/ProbeX" target="_blank">GitHub</a> | 🤗 <a href="https://huggingface.co/ProbeX" target="_blank">Dataset</a>
+</p>
+
+![ProbeX](https://raw.githubusercontent.com/eliahuhorwitz/ProbeX/main/imgs/poster.png)
+
+## Model Details
+
+| Attribute | Value |
+|---|---|
+| **Subset** | ResNet |
+| **Split** | train |
+| **Base Model** | `microsoft/resnet-101` |
+| **Dataset** | CIFAR100 (50 classes) |
+
+## Training Hyperparameters
+
+| Parameter | Value |
+|---|---|
+| Learning Rate | 0.0003 |
+| LR Scheduler | constant |
+| Epochs | 2 |
+| Max Train Steps | 666 |
+| Batch Size | 64 |
+| Weight Decay | 0.007 |
+| Seed | 292 |
+| Random Crop | True |
+| Random Flip | True |
+
+## Performance
+
+| Metric | Value |
+|---|---|
+| Train Accuracy | 0.9126 |
+| Val Accuracy | 0.8549 |
+| Test Accuracy | 0.8468 |
+
+## Training Categories
+
+The model was fine-tuned on the following 50 CIFAR100 classes:
+
+`keyboard`, `tulip`, `bowl`, `raccoon`, `lamp`, `lobster`, `orange`, `trout`, `tank`, `sea`, `lizard`, `spider`, `beetle`, `mushroom`, `cloud`, `worm`, `porcupine`, `pine_tree`, `oak_tree`, `train`, `couch`, `apple`, `caterpillar`, `poppy`, `flatfish`, `snake`, `ray`, `chair`, `pear`, `bed`, `skyscraper`, `elephant`, `wolf`, `shrew`, `baby`, `castle`, `tractor`, `tiger`, `motorcycle`, `man`, `forest`, `mountain`, `turtle`, `mouse`, `plate`, `bee`, `beaver`, `can`, `bus`, `camel`