README.md

metadata

license: apache-2.0
base_model: facebook/deit-base-distilled-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: deit-base-distilled-patch16-224-hasta-55-fold5
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: train
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.6388888888888888

deit-base-distilled-patch16-224-hasta-55-fold5

This model is a fine-tuned version of facebook/deit-base-distilled-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

• Loss: 0.9258
• Accuracy: 0.6389

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: 32
• eval_batch_size: 32
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 128
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	0.5714	1	1.1479	0.2778
No log	1.7143	3	1.1506	0.4444
No log	2.8571	5	1.2127	0.3889
No log	4.0	7	1.0517	0.4167
No log	4.5714	8	1.0113	0.5833
1.0752	5.7143	10	1.0014	0.4722
1.0752	6.8571	12	0.9754	0.5556
1.0752	8.0	14	0.9684	0.6111
1.0752	8.5714	15	0.9598	0.5
1.0752	9.7143	17	0.9350	0.5278
1.0752	10.8571	19	1.0848	0.5
0.8688	12.0	21	0.9479	0.5
0.8688	12.5714	22	0.9568	0.4722
0.8688	13.7143	24	0.9859	0.5833
0.8688	14.8571	26	1.0116	0.5278
0.8688	16.0	28	1.0542	0.4722
0.8688	16.5714	29	0.9993	0.5
0.7636	17.7143	31	0.9896	0.5278
0.7636	18.8571	33	0.9693	0.5278
0.7636	20.0	35	0.9705	0.5556
0.7636	20.5714	36	0.9990	0.5833
0.7636	21.7143	38	1.0442	0.5833
0.5868	22.8571	40	1.0241	0.5
0.5868	24.0	42	0.9458	0.5
0.5868	24.5714	43	0.9391	0.5556
0.5868	25.7143	45	0.9626	0.5278
0.5868	26.8571	47	0.9699	0.5278
0.5868	28.0	49	0.9504	0.5
0.4816	28.5714	50	0.9226	0.4722
0.4816	29.7143	52	0.9353	0.5278
0.4816	30.8571	54	0.9030	0.5556
0.4816	32.0	56	0.8948	0.5278
0.4816	32.5714	57	0.9272	0.5278
0.4816	33.7143	59	0.9202	0.5278
0.3909	34.8571	61	0.9052	0.5833
0.3909	36.0	63	0.9258	0.6389
0.3909	36.5714	64	0.9267	0.5833
0.3909	37.7143	66	0.9902	0.5556
0.3909	38.8571	68	1.0495	0.5278
0.3124	40.0	70	0.9900	0.5278
0.3124	40.5714	71	0.9510	0.5278
0.3124	41.7143	73	0.9531	0.5833
0.3124	42.8571	75	0.9439	0.5278
0.3124	44.0	77	0.9521	0.5278
0.3124	44.5714	78	0.9531	0.5278
0.3225	45.7143	80	0.9551	0.5
0.3225	46.8571	82	0.9520	0.5
0.3225	48.0	84	0.9464	0.5278
0.3225	48.5714	85	0.9409	0.5278
0.3225	49.7143	87	0.9471	0.5833
0.3225	50.8571	89	0.9646	0.5833
0.2829	52.0	91	0.9805	0.5833
0.2829	52.5714	92	0.9747	0.5833
0.2829	53.7143	94	0.9646	0.5833
0.2829	54.8571	96	0.9659	0.5833
0.2829	56.0	98	0.9644	0.5833
0.2829	56.5714	99	0.9646	0.5833
0.272	57.1429	100	0.9648	0.5833

Framework versions

• Transformers 4.41.0
• Pytorch 2.3.0+cu121
• Datasets 2.19.1
• Tokenizers 0.19.1