metadata
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_0837)
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
🌐 Project | 📃 Paper | 💻 GitHub | 🤗 Dataset
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 | cosine |
| Epochs | 2 |
| Max Train Steps | 666 |
| Batch Size | 64 |
| Weight Decay | 0.007 |
| Seed | 837 |
| Random Crop | False |
| Random Flip | True |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9385 |
| Val Accuracy | 0.8997 |
| Test Accuracy | 0.8930 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
beetle, orange, bowl, tulip, sweet_pepper, table, lamp, mountain, pickup_truck, lion, squirrel, cockroach, kangaroo, rose, porcupine, hamster, turtle, lobster, cattle, caterpillar, train, palm_tree, fox, wolf, wardrobe, mouse, plain, bottle, motorcycle, beaver, shark, lizard, lawn_mower, pine_tree, tank, trout, snake, tractor, sunflower, snail, couch, crab, seal, castle, chair, elephant, raccoon, girl, bee, can