Instructions to use ProbeX/Model-J__ResNet__model_idx_0755 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_0755 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_0755") 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_0755") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0755") - Notebooks
- Google Colab
- Kaggle
Model-J: ResNet Model (model_idx_0755)
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 | 9e-05 |
| LR Scheduler | linear |
| Epochs | 6 |
| Max Train Steps | 1998 |
| Batch Size | 64 |
| Weight Decay | 0.007 |
| Seed | 755 |
| Random Crop | True |
| Random Flip | False |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.9044 |
| Val Accuracy | 0.8621 |
| Test Accuracy | 0.8530 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
kangaroo, tank, trout, crocodile, cloud, caterpillar, apple, bridge, turtle, man, boy, couch, ray, motorcycle, willow_tree, beaver, crab, cattle, rose, shark, lawn_mower, forest, skunk, worm, otter, skyscraper, lion, sweet_pepper, chimpanzee, house, mountain, television, cup, lamp, sunflower, castle, pine_tree, bed, mushroom, wardrobe, spider, clock, oak_tree, shrew, bus, fox, chair, pickup_truck, elephant, poppy
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Model tree for ProbeX/Model-J__ResNet__model_idx_0755
Base model
microsoft/resnet-101