README.md

metadata

license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: meat_calssify_fresh_crop_V_0_2
    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: 0.7677419354838709

meat_calssify_fresh_crop_V_0_2

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set:

• Loss: 0.6201
• Accuracy: 0.7677

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: 100
• eval_batch_size: 1
• 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: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.1071	1.0	7	1.1020	0.3032
1.0846	2.0	14	1.0824	0.4323
1.0559	3.0	21	1.0499	0.4774
1.0098	4.0	28	1.0008	0.4903
0.9522	5.0	35	0.9723	0.5097
0.9491	6.0	42	0.9122	0.6065
0.8659	7.0	49	0.9418	0.5290
0.8838	8.0	56	0.8785	0.5871
0.856	9.0	63	0.8893	0.5548
0.7896	10.0	70	0.8382	0.6129
0.7409	11.0	77	0.7915	0.6839
0.6992	12.0	84	0.8152	0.6581
0.6652	13.0	91	0.8571	0.6194
0.6149	14.0	98	0.7757	0.6452
0.6121	15.0	105	0.7275	0.7097
0.5469	16.0	112	0.7461	0.6968
0.4911	17.0	119	0.7415	0.7032
0.4495	18.0	126	0.8020	0.6774
0.4634	19.0	133	0.7726	0.6452
0.4244	20.0	140	0.7531	0.6645
0.4263	21.0	147	0.6650	0.7226
0.3785	22.0	154	0.6666	0.7032
0.3922	23.0	161	0.7982	0.6774
0.3366	24.0	168	0.8043	0.6710
0.3169	25.0	175	0.6897	0.7355
0.3292	26.0	182	0.7959	0.6774
0.2988	27.0	189	0.7386	0.7161
0.2965	28.0	196	0.8410	0.6581
0.3015	29.0	203	0.8074	0.6581
0.2373	30.0	210	0.6866	0.7484
0.2659	31.0	217	0.6660	0.7613
0.2421	32.0	224	0.7252	0.6968
0.2386	33.0	231	0.7942	0.6645
0.2526	34.0	238	0.7830	0.6774
0.2117	35.0	245	0.6239	0.7613
0.2409	36.0	252	0.6610	0.7484
0.2263	37.0	259	0.6636	0.7871
0.2105	38.0	266	0.7461	0.7548
0.1881	39.0	273	0.7684	0.6774
0.194	40.0	280	0.7719	0.7032
0.1734	41.0	287	0.7544	0.7226
0.1913	42.0	294	0.7721	0.7032
0.1863	43.0	301	0.7524	0.7097
0.1747	44.0	308	0.8238	0.7032
0.1871	45.0	315	0.6709	0.7613
0.2191	46.0	322	0.6103	0.7484
0.1634	47.0	329	0.6160	0.7806
0.1717	48.0	336	0.6423	0.7419
0.1868	49.0	343	0.6767	0.7484
0.2183	50.0	350	0.6201	0.7677

Framework versions

• Transformers 4.41.2
• Pytorch 2.3.0
• Datasets 2.19.2
• Tokenizers 0.19.1