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clip-vit-base-patch32-finetuned-cars
kellyshreeve/clip-vit-base-patch32-finetuned-cars
clip-vit-base-patch32-finetuned-cars is a image classification model from kellyshreeve. Use it when you need a label for an image. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Image Classification
- Library
- transformers
- Model type
- clip
- Access
- Public
- Created
- Sep 5, 2025
- Updated
- Sep 5, 2025
- SHA
- b1bc980b
Base models
Datasets
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
clip-vit-base-patch32-finetuned-cars
This model is a fine-tuned version of openai/clip-vit-base-patch32 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.4621
- Accuracy: 1.0
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-05
- train_batch_size: 3
- eval_batch_size: 3
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 12
- optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 2 | 0.4621 | 1.0 |
| No log | 2.0 | 4 | 0.3222 | 1.0 |
| No log | 3.0 | 6 | 0.2562 | 0.8889 |
| No log | 4.0 | 8 | 0.2668 | 0.8889 |
| 0.3954 | 5.0 | 10 | 0.2213 | 0.8889 |
Framework versions
- Transformers 4.56.1
- Pytorch 2.8.0
- Datasets 4.0.0
- Tokenizers 0.22.0
- Task
- Image Classification
- Library
- transformers
- Type
- clip
- Created
- Sep 5, 2025
- Updated
- Sep 5, 2025