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

Loss: 0.2362
Accuracy: 0.9454

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: 25
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.1589	0.25	100	0.3561	0.9141
0.1629	0.49	200	0.3792	0.8932
0.1222	0.74	300	0.3749	0.8975
0.1363	0.98	400	0.3021	0.9122
0.0699	1.23	500	0.4802	0.8883
0.0759	1.47	600	0.3129	0.9214
0.2157	1.72	700	0.3362	0.9159
0.1148	1.97	800	0.2898	0.9122
0.3137	2.21	900	0.4267	0.8969
0.072	2.46	1000	0.3180	0.9141
0.0775	2.7	1100	0.3856	0.9067
0.1019	2.95	1200	0.3182	0.9153
0.1119	3.19	1300	0.4458	0.8944
0.1342	3.44	1400	0.4718	0.8889
0.1658	3.69	1500	0.3697	0.9012
0.1609	3.93	1600	0.4079	0.9024
0.1223	4.18	1700	0.3688	0.9147
0.1821	4.42	1800	0.3392	0.9116
0.0901	4.67	1900	0.3726	0.8969
0.0857	4.91	2000	0.3158	0.9196
0.1245	5.16	2100	0.3503	0.9122
0.133	5.41	2200	0.3712	0.9134
0.171	5.65	2300	0.3543	0.9067
0.1222	5.9	2400	0.3031	0.9227
0.1504	6.14	2500	0.3356	0.9085
0.0889	6.39	2600	0.3695	0.9116
0.0185	6.63	2700	0.3509	0.9141
0.1201	6.88	2800	0.3330	0.9177
0.0766	7.13	2900	0.2718	0.9251
0.0998	7.37	3000	0.3471	0.9233
0.1654	7.62	3100	0.3285	0.9196
0.0529	7.86	3200	0.3394	0.9190
0.1199	8.11	3300	0.2968	0.9294
0.0338	8.35	3400	0.2784	0.9251
0.124	8.6	3500	0.3099	0.9251
0.0581	8.85	3600	0.3372	0.9263
0.1776	9.09	3700	0.3580	0.9134
0.1598	9.34	3800	0.3158	0.9196
0.1122	9.58	3900	0.3369	0.9190
0.0808	9.83	4000	0.3259	0.9368
0.1086	10.07	4100	0.3691	0.9190
0.0197	10.32	4200	0.3101	0.9355
0.065	10.57	4300	0.3479	0.9227
0.1183	10.81	4400	0.3281	0.9319
0.044	11.06	4500	0.4357	0.9134
0.1021	11.3	4600	0.3211	0.9337
0.0615	11.55	4700	0.2947	0.9398
0.0664	11.79	4800	0.4421	0.9184
0.092	12.04	4900	0.3333	0.9202
0.1544	12.29	5000	0.3062	0.9245
0.1324	12.53	5100	0.2756	0.9294
0.1132	12.78	5200	0.2570	0.9362
0.0899	13.02	5300	0.2486	0.9386
0.0712	13.27	5400	0.2878	0.9306
0.0411	13.51	5500	0.2663	0.9368
0.0559	13.76	5600	0.2751	0.9355
0.0928	14.0	5700	0.3093	0.9269
0.0504	14.25	5800	0.2954	0.9319
0.0995	14.5	5900	0.2636	0.9337
0.1139	14.74	6000	0.2827	0.9349
0.0992	14.99	6100	0.2662	0.9368
0.1519	15.23	6200	0.2720	0.9398
0.0192	15.48	6300	0.3252	0.9269
0.0592	15.72	6400	0.3382	0.9263
0.0382	15.97	6500	0.2710	0.9349
0.0723	16.22	6600	0.2671	0.9374
0.0073	16.46	6700	0.3451	0.9263
0.1796	16.71	6800	0.3196	0.9196
0.0919	16.95	6900	0.2464	0.9337
0.0739	17.2	7000	0.2258	0.9392
0.0468	17.44	7100	0.2483	0.9411
0.145	17.69	7200	0.2639	0.9312
0.0243	17.94	7300	0.2574	0.9362
0.0648	18.18	7400	0.2554	0.9331
0.0508	18.43	7500	0.2554	0.9374
0.0475	18.67	7600	0.2915	0.9337
0.0708	18.92	7700	0.2801	0.9300
0.1476	19.16	7800	0.2479	0.9411
0.1535	19.41	7900	0.2412	0.9411
0.0873	19.66	8000	0.2544	0.9398
0.0416	19.9	8100	0.2334	0.9423
0.1157	20.15	8200	0.2059	0.9540
0.039	20.39	8300	0.2601	0.9362
0.0223	20.64	8400	0.2234	0.9484
0.0779	20.88	8500	0.2468	0.9405
0.0604	21.13	8600	0.2334	0.9374
0.1206	21.38	8700	0.2504	0.9398
0.0738	21.62	8800	0.2505	0.9398
0.0438	21.87	8900	0.2148	0.9472
0.0689	22.11	9000	0.2286	0.9435
0.0505	22.36	9100	0.1956	0.9472
0.0581	22.6	9200	0.2104	0.9484
0.1575	22.85	9300	0.2309	0.9441
0.048	23.1	9400	0.2685	0.9398
0.0784	23.34	9500	0.2329	0.9454
0.0771	23.59	9600	0.2294	0.9466
0.0545	23.83	9700	0.2037	0.9484
0.0481	24.08	9800	0.1994	0.9540
0.0663	24.32	9900	0.1993	0.9490
0.0921	24.57	10000	0.2204	0.9521
0.0939	24.82	10100	0.2362	0.9454

Framework versions

Transformers 4.28.1
Pytorch 2.0.0+cu118
Datasets 2.11.0
Tokenizers 0.13.3

carolinetfls/plant-seedlings-vit-freeze-0-6-aug-3-all-train

plant-seedlings-freeze-0-6-aug-3-all-train

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

carolinetfls/
plant-seedlings-vit-freeze-0-6-aug-3-all-train