Instructions to use ProbeX/Model-J__ResNet__model_idx_0812 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_0812 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_0812") 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_0812") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0812") - Notebooks
- Google Colab
- Kaggle
Model-J: ResNet Model (model_idx_0812)
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 | 5e-05 |
| LR Scheduler | cosine |
| Epochs | 4 |
| Max Train Steps | 1332 |
| Batch Size | 64 |
| Weight Decay | 0.05 |
| Seed | 812 |
| Random Crop | True |
| Random Flip | True |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.7512 |
| Val Accuracy | 0.7219 |
| Test Accuracy | 0.7320 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
chimpanzee, streetcar, wardrobe, spider, forest, motorcycle, ray, boy, mountain, table, plate, castle, sea, telephone, bus, lion, otter, butterfly, bear, pickup_truck, sweet_pepper, maple_tree, worm, palm_tree, shrew, pear, flatfish, sunflower, apple, bowl, pine_tree, mushroom, television, man, willow_tree, whale, skunk, kangaroo, snake, bed, orchid, plain, tank, beetle, cloud, dinosaur, can, tulip, girl, poppy
- Downloads last month
- 3
Model tree for ProbeX/Model-J__ResNet__model_idx_0812
Base model
microsoft/resnet-101