README.md

metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: plant-seedlings-model-ResNet18-freeze-0-12-20ep
    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: 0.9327111984282908

plant-seedlings-model-ResNet18-freeze-0-12-20ep

This model is a fine-tuned version of microsoft/resnet-18 on the imagefolder dataset. It achieves the following results on the evaluation set:

• Loss: 0.2060
• Accuracy: 0.9327

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: 0.0002
• train_batch_size: 16
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 20
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.4528	0.25	128	0.6686	0.7878
0.4291	0.5	256	0.6259	0.7903
0.4961	0.75	384	0.5677	0.8055
0.4637	1.01	512	0.5073	0.8330
0.6897	1.26	640	0.5817	0.8060
0.5257	1.51	768	0.5118	0.8276
0.5381	1.76	896	0.4809	0.8384
0.4736	2.01	1024	0.3976	0.8595
0.4967	2.26	1152	0.4192	0.8566
0.4505	2.51	1280	0.4128	0.8590
0.4211	2.77	1408	0.4075	0.8576
0.3877	3.02	1536	0.3796	0.8738
0.3134	3.27	1664	0.3906	0.8762
0.3596	3.52	1792	0.3703	0.8846
0.3859	3.77	1920	0.3125	0.8954
0.4076	4.02	2048	0.3718	0.8615
0.3109	4.28	2176	0.3449	0.8924
0.4588	4.53	2304	0.3377	0.8875
0.2923	4.78	2432	0.3001	0.8998
0.3273	5.03	2560	0.3187	0.8880
0.2541	5.28	2688	0.3432	0.8856
0.3059	5.53	2816	0.3236	0.8988
0.2979	5.78	2944	0.3532	0.8851
0.2748	6.04	3072	0.3407	0.8885
0.3537	6.29	3200	0.2925	0.8988
0.3364	6.54	3328	0.3071	0.9047
0.2135	6.79	3456	0.2765	0.9077
0.2023	7.04	3584	0.2919	0.9037
0.1977	7.29	3712	0.2812	0.8978
0.4042	7.54	3840	0.2954	0.8998
0.3662	7.8	3968	0.2857	0.9018
0.1872	8.05	4096	0.2504	0.9140
0.3959	8.3	4224	0.2984	0.8993
0.2403	8.55	4352	0.2847	0.8998
0.3689	8.8	4480	0.2872	0.9023
0.2819	9.05	4608	0.3104	0.9008
0.1926	9.3	4736	0.2871	0.8969
0.2371	9.56	4864	0.2733	0.9082
0.2566	9.81	4992	0.2816	0.9101
0.2174	10.06	5120	0.2719	0.9160
0.2359	10.31	5248	0.2497	0.9175
0.2986	10.56	5376	0.2847	0.9096
0.2239	10.81	5504	0.2493	0.9180
0.2132	11.06	5632	0.2567	0.9121
0.1934	11.32	5760	0.2722	0.9028
0.2026	11.57	5888	0.2456	0.9229
0.2457	11.82	6016	0.2483	0.9234
0.2537	12.07	6144	0.2409	0.9165
0.193	12.32	6272	0.2215	0.9239
0.1738	12.57	6400	0.2421	0.9165
0.2925	12.83	6528	0.2499	0.9150
0.1173	13.08	6656	0.2174	0.9258
0.2147	13.33	6784	0.2917	0.9131
0.1581	13.58	6912	0.2734	0.9175
0.1349	13.83	7040	0.2485	0.9165
0.1212	14.08	7168	0.2247	0.9268
0.2178	14.33	7296	0.2289	0.9268
0.0879	14.59	7424	0.2512	0.9219
0.2006	14.84	7552	0.2321	0.9293
0.2308	15.09	7680	0.2491	0.9263
0.2137	15.34	7808	0.2270	0.9312
0.1112	15.59	7936	0.2205	0.9249
0.1477	15.84	8064	0.2328	0.9307
0.1794	16.09	8192	0.2051	0.9332
0.0596	16.35	8320	0.2234	0.9347
0.0533	16.6	8448	0.2469	0.9293
0.1096	16.85	8576	0.1871	0.9401
0.1117	17.1	8704	0.2302	0.9249
0.1349	17.35	8832	0.2084	0.9391
0.1031	17.6	8960	0.2200	0.9283
0.2428	17.85	9088	0.2201	0.9298
0.1283	18.11	9216	0.2293	0.9273
0.1688	18.36	9344	0.2120	0.9307
0.0877	18.61	9472	0.2200	0.9229
0.1508	18.86	9600	0.2204	0.9327
0.0868	19.11	9728	0.2224	0.9293
0.211	19.36	9856	0.1988	0.9401
0.1059	19.61	9984	0.2082	0.9322
0.182	19.87	10112	0.2060	0.9327

Framework versions

• Transformers 4.28.1
• Pytorch 2.0.0+cu118
• Datasets 2.11.0
• Tokenizers 0.13.3