README.md

metadata

license: apache-2.0
base_model: microsoft/cvt-21-384-22k
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: cvt-21-384-22k-finetuned-LeafBack
    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: 0.974025974025974

cvt-21-384-22k-finetuned-LeafBack

This model is a fine-tuned version of microsoft/cvt-21-384-22k on the imagefolder dataset. It achieves the following results on the evaluation set:

• Loss: 0.0832
• Accuracy: 0.9740

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: 2e-05
• train_batch_size: 100
• eval_batch_size: 50
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 60

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	7	0.6551	0.6364
No log	2.0	14	0.5215	0.7532
No log	3.0	21	0.4506	0.7922
No log	4.0	28	0.4032	0.8182
No log	5.0	35	0.3416	0.8442
No log	6.0	42	0.2768	0.9091
No log	7.0	49	0.3287	0.8571
No log	8.0	56	0.2419	0.9091
No log	9.0	63	0.2510	0.9091
No log	10.0	70	0.2100	0.8961
No log	11.0	77	0.2691	0.8961
No log	12.0	84	0.2717	0.8961
No log	13.0	91	0.1412	0.9481
No log	14.0	98	0.1541	0.9351
No log	15.0	105	0.2572	0.8701
No log	16.0	112	0.1843	0.8961
No log	17.0	119	0.1376	0.9481
No log	18.0	126	0.1632	0.9351
No log	19.0	133	0.1901	0.9091
No log	20.0	140	0.1495	0.9221
No log	21.0	147	0.1710	0.9221
No log	22.0	154	0.1906	0.9351
No log	23.0	161	0.1192	0.9610
No log	24.0	168	0.2000	0.9091
No log	25.0	175	0.1611	0.9221
No log	26.0	182	0.1074	0.9610
No log	27.0	189	0.1080	0.9481
No log	28.0	196	0.1405	0.9351
No log	29.0	203	0.0890	0.9610
No log	30.0	210	0.0777	0.9740
No log	31.0	217	0.0636	0.9740
No log	32.0	224	0.0709	0.9740
No log	33.0	231	0.0671	0.9740
No log	34.0	238	0.1055	0.9610
No log	35.0	245	0.1366	0.9610
No log	36.0	252	0.1070	0.9610
No log	37.0	259	0.0890	0.9481
No log	38.0	266	0.0876	0.9481
No log	39.0	273	0.0838	0.9481
No log	40.0	280	0.1502	0.9610
No log	41.0	287	0.1241	0.9610
No log	42.0	294	0.1043	0.9740
No log	43.0	301	0.1126	0.9740
No log	44.0	308	0.1283	0.9610
No log	45.0	315	0.1123	0.9610
No log	46.0	322	0.1198	0.9610
No log	47.0	329	0.1434	0.9610
No log	48.0	336	0.1340	0.9610
No log	49.0	343	0.1073	0.9610
No log	50.0	350	0.1112	0.9610
No log	51.0	357	0.1035	0.9610
No log	52.0	364	0.0972	0.9610
No log	53.0	371	0.0960	0.9610
No log	54.0	378	0.0881	0.9610
No log	55.0	385	0.0797	0.9610
No log	56.0	392	0.0773	0.9740
No log	57.0	399	0.0736	0.9740
No log	58.0	406	0.0794	0.9740
No log	59.0	413	0.0841	0.9740
No log	60.0	420	0.0832	0.9740

Framework versions

• Transformers 4.38.1
• Pytorch 1.10.0+cu111
• Datasets 2.17.1
• Tokenizers 0.15.2