Instructions to use ProbeX/Model-J__ResNet__model_idx_0523 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_0523 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_0523") 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_0523") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0523") - Notebooks
- Google Colab
- Kaggle
Model-J: ResNet Model (model_idx_0523)
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 | 7e-05 |
| LR Scheduler | cosine_with_restarts |
| Epochs | 5 |
| Max Train Steps | 1665 |
| Batch Size | 64 |
| Weight Decay | 0.01 |
| Seed | 523 |
| Random Crop | True |
| Random Flip | False |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.8878 |
| Val Accuracy | 0.8605 |
| Test Accuracy | 0.8562 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
otter, lawn_mower, apple, crab, dinosaur, snail, butterfly, fox, road, bus, castle, seal, maple_tree, porcupine, snake, can, tractor, tiger, plain, poppy, wardrobe, tank, bed, lizard, wolf, cloud, table, streetcar, sweet_pepper, possum, bridge, girl, clock, chimpanzee, palm_tree, television, orange, shark, oak_tree, couch, man, bear, raccoon, aquarium_fish, pickup_truck, beetle, sunflower, whale, rocket, orchid
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Model tree for ProbeX/Model-J__ResNet__model_idx_0523
Base model
microsoft/resnet-101