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_0957)
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 | 3e-05 |
| LR Scheduler | linear |
| Epochs | 3 |
| Max Train Steps | 999 |
| Batch Size | 64 |
| Weight Decay | 0.05 |
| Seed | 957 |
| Random Crop | False |
| Random Flip | True |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.5689 |
| Val Accuracy | 0.5485 |
| Test Accuracy | 0.5500 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
wardrobe, snake, whale, possum, pickup_truck, bee, train, telephone, elephant, couch, sweet_pepper, otter, table, streetcar, tiger, bridge, hamster, trout, aquarium_fish, can, shrew, mountain, skyscraper, boy, caterpillar, television, lamp, ray, orange, kangaroo, flatfish, butterfly, raccoon, rabbit, house, fox, porcupine, cattle, castle, dolphin, poppy, mushroom, forest, wolf, skunk, spider, tank, orchid, tulip, seal