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_0529)
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 | 0.0005 |
| LR Scheduler | linear |
| Epochs | 9 |
| Max Train Steps | 2997 |
| Batch Size | 64 |
| Weight Decay | 0.03 |
| Seed | 529 |
| Random Crop | False |
| Random Flip | False |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 1.0000 |
| Val Accuracy | 0.9005 |
| Test Accuracy | 0.8928 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
tank, lamp, woman, sweet_pepper, crocodile, squirrel, mountain, porcupine, telephone, sea, train, whale, pine_tree, motorcycle, aquarium_fish, cockroach, bed, streetcar, rose, dinosaur, wolf, mushroom, maple_tree, shark, willow_tree, bus, ray, sunflower, turtle, elephant, road, forest, clock, butterfly, oak_tree, palm_tree, shrew, cattle, orchid, can, bottle, man, otter, house, couch, rabbit, crab, pear, lawn_mower, trout