Instructions to use ProbeX/Model-J__ResNet__model_idx_0221 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_0221 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_0221") 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_0221") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0221") - Notebooks
- Google Colab
- Kaggle
Model-J: ResNet Model (model_idx_0221)
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 |
| Epochs | 7 |
| Max Train Steps | 2331 |
| Batch Size | 64 |
| Weight Decay | 0.01 |
| Seed | 221 |
| Random Crop | True |
| Random Flip | False |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9696 |
| Val Accuracy | 0.8827 |
| Test Accuracy | 0.8764 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
boy, lobster, mushroom, bus, poppy, crocodile, sunflower, turtle, television, plate, apple, fox, bottle, caterpillar, leopard, plain, house, cockroach, maple_tree, raccoon, camel, spider, elephant, dinosaur, bed, forest, beaver, shark, porcupine, crab, road, sea, woman, cup, chimpanzee, streetcar, oak_tree, telephone, lawn_mower, pickup_truck, mountain, seal, pine_tree, bridge, lion, butterfly, baby, bear, trout, can
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Model tree for ProbeX/Model-J__ResNet__model_idx_0221
Base model
microsoft/resnet-101