Instructions to use ProbeX/Model-J__ResNet__model_idx_0903 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ProbeX/Model-J__ResNet__model_idx_0903 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ProbeX/Model-J__ResNet__model_idx_0903") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0903") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0903") - Notebooks
- Google Colab
- Kaggle
Model-J: ResNet Model (model_idx_0903)
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
๐ Project | ๐ Paper | ๐ป GitHub | ๐ค 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 | 3 |
| Max Train Steps | 999 |
| Batch Size | 64 |
| Weight Decay | 0.03 |
| Seed | 903 |
| Random Crop | False |
| Random Flip | False |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.8884 |
| Val Accuracy | 0.8360 |
| Test Accuracy | 0.8326 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
rabbit, bowl, cloud, motorcycle, tulip, porcupine, pine_tree, shrew, table, rose, dinosaur, baby, fox, hamster, crocodile, sweet_pepper, raccoon, man, plate, caterpillar, telephone, leopard, beaver, bottle, streetcar, couch, crab, worm, can, castle, road, tractor, chair, wardrobe, bear, aquarium_fish, house, skunk, trout, forest, rocket, cup, flatfish, pear, bed, cockroach, tank, shark, girl, lobster
- Downloads last month
- 3
Model tree for ProbeX/Model-J__ResNet__model_idx_0903
Base model
microsoft/resnet-101