Instructions to use ProbeX/Model-J__ResNet__model_idx_0547 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_0547 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_0547") 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_0547") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0547") - Notebooks
- Google Colab
- Kaggle
Model-J: ResNet Model (model_idx_0547)
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 | test |
| Base Model | microsoft/resnet-101 |
| Dataset | CIFAR100 (50 classes) |
Training Hyperparameters
| Parameter | Value |
|---|---|
| Learning Rate | 0.0003 |
| LR Scheduler | cosine_with_restarts |
| Epochs | 2 |
| Max Train Steps | 666 |
| Batch Size | 64 |
| Weight Decay | 0.01 |
| Seed | 547 |
| Random Crop | True |
| Random Flip | True |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.8772 |
| Val Accuracy | 0.8325 |
| Test Accuracy | 0.8330 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
cup, ray, chimpanzee, girl, couch, crab, fox, sweet_pepper, lamp, rocket, pear, skyscraper, telephone, lawn_mower, bed, bear, clock, orchid, train, willow_tree, table, man, flatfish, aquarium_fish, castle, tulip, snake, elephant, crocodile, turtle, pine_tree, pickup_truck, sunflower, squirrel, mouse, mountain, leopard, oak_tree, orange, keyboard, bee, otter, shrew, tank, beaver, maple_tree, forest, raccoon, seal, wolf
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Model tree for ProbeX/Model-J__ResNet__model_idx_0547
Base model
microsoft/resnet-101