# Load model directly
from transformers import AutoProcessor, AutoModelForZeroShotImageClassification
processor = AutoProcessor.from_pretrained("turing552/clip-ROCOv2-radiology-5ep")
model = AutoModelForZeroShotImageClassification.from_pretrained("turing552/clip-ROCOv2-radiology-5ep")Quick Links
clip-ROCOv2-radiology-5ep
This model is a fine-tuned version of openai/clip-vit-base-patch32 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.4365
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.5698 | 0.6588 | 500 | 1.4979 |
| 1.0335 | 1.3175 | 1000 | 1.2915 |
| 0.9555 | 1.9763 | 1500 | 1.1798 |
| 0.644 | 2.6350 | 2000 | 1.2104 |
| 0.3687 | 3.2938 | 2500 | 1.3033 |
| 0.3659 | 3.9526 | 3000 | 1.3342 |
| 0.2289 | 4.6113 | 3500 | 1.4365 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.5.1+cu124
- Datasets 4.4.1
- Tokenizers 0.19.1
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Model tree for turing552/clip-ROCOv2-radiology-5ep
Base model
openai/clip-vit-base-patch32
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="turing552/clip-ROCOv2-radiology-5ep") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )