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_0330)
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 | 5e-05 |
| LR Scheduler | constant |
| Epochs | 8 |
| Max Train Steps | 2664 |
| Batch Size | 64 |
| Weight Decay | 0.007 |
| Seed | 330 |
| Random Crop | True |
| Random Flip | True |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9601 |
| Val Accuracy | 0.9032 |
| Test Accuracy | 0.9000 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
elephant, mountain, road, plain, rocket, bottle, house, seal, sea, television, apple, butterfly, skunk, pickup_truck, pear, flatfish, bridge, bus, skyscraper, lawn_mower, sunflower, shrew, telephone, mushroom, wolf, aquarium_fish, cup, orange, oak_tree, tank, shark, beaver, tractor, snail, squirrel, chimpanzee, snake, clock, turtle, caterpillar, leopard, bicycle, can, train, lobster, plate, tiger, poppy, keyboard, pine_tree