Instructions to use ProbeX/Model-J__DINO__model_idx_0275 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ProbeX/Model-J__DINO__model_idx_0275 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ProbeX/Model-J__DINO__model_idx_0275") 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__DINO__model_idx_0275") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__DINO__model_idx_0275") - Notebooks
- Google Colab
- Kaggle
# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("ProbeX/Model-J__DINO__model_idx_0275")
model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__DINO__model_idx_0275")Model-J: DINO Model (model_idx_0275)
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 | DINO |
| Split | train |
| Base Model | facebook/dino-vitb16 |
| Dataset | CIFAR100 (50 classes) |
Training Hyperparameters
| Parameter | Value |
|---|---|
| Learning Rate | 0.0003 |
| LR Scheduler | cosine_with_restarts |
| Epochs | 7 |
| Max Train Steps | 2331 |
| Batch Size | 64 |
| Weight Decay | 0.005 |
| Seed | 275 |
| Random Crop | False |
| Random Flip | True |
Performance
| Metric | Value |
|---|---|
| Train Accuracy | 0.6224 |
| Val Accuracy | 0.4272 |
| Test Accuracy | 0.4422 |
Training Categories
The model was fine-tuned on the following 50 CIFAR100 classes:
motorcycle, road, bee, porcupine, forest, lobster, lamp, bowl, snake, trout, can, chimpanzee, kangaroo, turtle, couch, crocodile, cockroach, beetle, hamster, clock, skyscraper, dinosaur, dolphin, sea, oak_tree, possum, bed, whale, streetcar, bear, woman, bridge, orchid, palm_tree, bus, poppy, mushroom, willow_tree, otter, television, wardrobe, tank, spider, fox, girl, pine_tree, house, bicycle, cattle, plain
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Model tree for ProbeX/Model-J__DINO__model_idx_0275
Base model
facebook/dino-vitb16
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ProbeX/Model-J__DINO__model_idx_0275") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")