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_0318)
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_with_warmup |
| Epochs | 3 |
| Max Train Steps | 999 |
| Batch Size | 64 |
| Weight Decay | 0.01 |
| Seed | 318 |
| Random Crop | True |
| Random Flip | False |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.8414 |
| Val Accuracy | 0.8029 |
| Test Accuracy | 0.8066 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
tank, willow_tree, dolphin, bus, plain, camel, road, lizard, table, mountain, motorcycle, crab, butterfly, ray, lion, spider, lawn_mower, television, raccoon, bowl, cockroach, flatfish, streetcar, squirrel, girl, caterpillar, baby, orange, poppy, seal, crocodile, shark, sweet_pepper, trout, sunflower, apple, fox, plate, bicycle, palm_tree, possum, house, pickup_truck, beetle, whale, snake, aquarium_fish, oak_tree, otter, clock