Instructions to use ProbeX/Model-J__ResNet__model_idx_0241 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_0241 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_0241") 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_0241") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0241") - Notebooks
- Google Colab
- Kaggle
Model-J: ResNet Model (model_idx_0241)
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 | 0.0003 |
| LR Scheduler | constant_with_warmup |
| Epochs | 5 |
| Max Train Steps | 1665 |
| Batch Size | 64 |
| Weight Decay | 0.009 |
| Seed | 241 |
| Random Crop | True |
| Random Flip | True |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9655 |
| Val Accuracy | 0.8771 |
| Test Accuracy | 0.8708 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
sea, beetle, orange, rabbit, house, willow_tree, bicycle, cockroach, fox, telephone, hamster, streetcar, turtle, wardrobe, tank, oak_tree, dolphin, shark, cup, ray, kangaroo, lobster, mushroom, camel, rocket, poppy, forest, shrew, cattle, pear, tractor, wolf, baby, lawn_mower, squirrel, bottle, chair, girl, butterfly, porcupine, plain, bus, lamp, maple_tree, aquarium_fish, lizard, seal, tulip, train, mountain
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Model tree for ProbeX/Model-J__ResNet__model_idx_0241
Base model
microsoft/resnet-101