metadata

base_model: openai/clip-vit-base-patch32
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: ktp-crop-clip
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: validation
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9864864864864865

ktp-crop-clip

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: 0.1223
Accuracy: 0.9865

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: 16
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	0.96	6	0.8954	0.5270
0.7112	1.92	12	0.6729	0.5405
0.7112	2.88	18	0.6407	0.7297
0.4413	4.0	25	0.1279	0.9459
0.0935	4.96	31	0.1436	0.9730
0.0935	5.92	37	0.0021	1.0
0.0697	6.88	43	0.2862	0.9459
0.161	8.0	50	0.0843	0.9595
0.161	8.96	56	0.2255	0.9459
0.0061	9.92	62	0.4678	0.9054
0.0061	10.88	68	0.3299	0.9189
0.0309	12.0	75	0.5189	0.9189
0.0025	12.96	81	0.0850	0.9865
0.0025	13.92	87	0.0720	0.9865
0.0042	14.88	93	0.0745	0.9865
0.0002	16.0	100	0.0869	0.9865
0.0002	16.96	106	0.0895	0.9865
0.0001	17.92	112	0.1127	0.9865
0.0001	18.88	118	0.1219	0.9865
0.0	19.2	120	0.1223	0.9865

Framework versions

Transformers 4.41.2
Pytorch 2.1.2
Datasets 2.19.2
Tokenizers 0.19.1