beans-resnet50-cnn

This model is a fine-tuned version of microsoft/resnet-50 on an unknown dataset. It achieves the following results on the evaluation set:

Model description

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More information needed

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 50
mixed_precision_training: Native AMP

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.0819	1.0	33	1.0757	0.5639
1.0523	2.0	66	1.0407	0.7068
1.0098	3.0	99	0.9867	0.7519
0.9469	4.0	132	0.9058	0.8195
0.8691	5.0	165	0.8213	0.8195
0.7709	6.0	198	0.7321	0.8271
0.6937	7.0	231	0.6266	0.8496
0.6053	8.0	264	0.5494	0.8722
0.5225	9.0	297	0.4623	0.8722
0.4500	10.0	330	0.3884	0.8647
0.3636	11.0	363	0.3370	0.8647
0.3231	12.0	396	0.2642	0.9323
0.2656	13.0	429	0.2415	0.9173
0.2196	14.0	462	0.2057	0.9323
0.2198	15.0	495	0.1884	0.9323
0.1882	16.0	528	0.1779	0.9398
0.1597	17.0	561	0.1615	0.9323
0.1440	18.0	594	0.1367	0.9323
0.1205	19.0	627	0.1338	0.9398
0.1178	20.0	660	0.1451	0.9398
0.1021	21.0	693	0.1026	0.9624
0.0869	22.0	726	0.1568	0.9248
0.0873	23.0	759	0.1076	0.9474
0.0871	24.0	792	0.1295	0.9474
0.0856	25.0	825	0.1243	0.9549
0.0759	26.0	858	0.0867	0.9624
0.0621	27.0	891	0.0925	0.9549
0.0630	28.0	924	0.0832	0.9624
0.0802	29.0	957	0.0971	0.9624
0.0785	30.0	990	0.0891	0.9699
0.0466	31.0	1023	0.1131	0.9549
0.0612	32.0	1056	0.0896	0.9624
0.0577	33.0	1089	0.0754	0.9699
0.0821	34.0	1122	0.0778	0.9699
0.0592	35.0	1155	0.0549	0.9774
0.0506	36.0	1188	0.0664	0.9699
0.0538	37.0	1221	0.0862	0.9549
0.0462	38.0	1254	0.0734	0.9624
0.0555	39.0	1287	0.0596	0.9699
0.0545	40.0	1320	0.0960	0.9624
0.0464	41.0	1353	0.0737	0.9624
0.0628	42.0	1386	0.0708	0.9549
0.0447	43.0	1419	0.0837	0.9624
0.0712	44.0	1452	0.0809	0.9624
0.0612	45.0	1485	0.0894	0.9624
0.0412	46.0	1518	0.0576	0.9699
0.0506	47.0	1551	0.0690	0.9699
0.0557	48.0	1584	0.0714	0.9624
0.0361	49.0	1617	0.0774	0.9624
0.0348	50.0	1650	0.0703	0.9624