vit-base-patch16-224-in21k-FINALConcreteClassifier-VIT50epochsAUGMENTED

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: 0.0001
• Accuracy: {'accuracy': 1.0}
• F1: {'f1': 1.0}
• Precision: {'precision': 1.0}
• Recall: {'recall': 1.0}

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: 16
• eval_batch_size: 16
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 64
• 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	F1	Precision	Recall
0.8388	0.9994	407	0.7311	{'accuracy': 0.9157070474435743}	{'f1': 0.917794966875943}	{'precision': 0.921646356866348}	{'recall': 0.9170296069514275}
0.3038	1.9988	814	0.2005	{'accuracy': 0.9949332105020727}	{'f1': 0.9950743368173648}	{'precision': 0.9950553355854266}	{'recall': 0.9951352438470434}
0.1876	2.9982	1221	0.1047	{'accuracy': 0.9940119760479041}	{'f1': 0.9941331065394295}	{'precision': 0.9943533048504515}	{'recall': 0.993969038215347}
0.1113	4.0	1629	0.0507	{'accuracy': 0.9975433747888838}	{'f1': 0.9976395144195634}	{'precision': 0.9977092256032858}	{'recall': 0.997582948802461}
0.0796	4.9994	2036	0.0309	{'accuracy': 0.9967756794104099}	{'f1': 0.9968744828925905}	{'precision': 0.9969869308866188}	{'recall': 0.996780568599779}
0.083	5.9988	2443	0.0251	{'accuracy': 0.9966221403347152}	{'f1': 0.9967738849901814}	{'precision': 0.9968774505123108}	{'recall': 0.9967275638007345}
0.0571	6.9982	2850	0.0134	{'accuracy': 0.9978504529402733}	{'f1': 0.9979500771037213}	{'precision': 0.9979377245997445}	{'recall': 0.9979754620815722}
0.0422	8.0	3258	0.0114	{'accuracy': 0.9981575310916628}	{'f1': 0.998254711081091}	{'precision': 0.9982895535805023}	{'recall': 0.9982350712572632}
0.0358	8.9994	3665	0.0092	{'accuracy': 0.9978504529402733}	{'f1': 0.9979638871067233}	{'precision': 0.9979608626797065}	{'recall': 0.9979954180985109}
0.0294	9.9988	4072	0.0068	{'accuracy': 0.997389835713189}	{'f1': 0.9975489796644634}	{'precision': 0.9975481848852183}	{'recall': 0.9975538912237429}
0.047	10.9982	4479	0.0059	{'accuracy': 0.9978504529402733}	{'f1': 0.9979815213920817}	{'precision': 0.9979815455594003}	{'recall': 0.9979874356917354}
0.0195	12.0	4887	0.0031	{'accuracy': 0.9995393827729157}	{'f1': 0.9995674497361959}	{'precision': 0.9995686099728593}	{'recall': 0.9995664555320074}
0.0158	12.9994	5294	0.0023	{'accuracy': 0.9996929218486105}	{'f1': 0.9997116397752187}	{'precision': 0.999710312862109}	{'recall': 0.9997136311569301}
0.009	13.9988	5701	0.0036	{'accuracy': 0.9990787655458314}	{'f1': 0.9991197992455174}	{'precision': 0.9991467576791808}	{'recall': 0.9990975295853345}
0.0106	14.9982	6108	0.0025	{'accuracy': 0.9993858436972209}	{'f1': 0.9994156462516233}	{'precision': 0.999429874572406}	{'recall': 0.9994035847694385}
0.0044	16.0	6516	0.0032	{'accuracy': 0.9990787655458314}	{'f1': 0.9991348548711563}	{'precision': 0.9991467576791808}	{'recall': 0.9991289198606272}
0.0286	16.9994	6923	0.0012	{'accuracy': 0.9998464609243052}	{'f1': 0.9998479593939164}	{'precision': 0.9998569794050343}	{'recall': 0.9998391248391248}
0.034	17.9988	7330	0.0019	{'accuracy': 0.9993858436972209}	{'f1': 0.9994154220725433}	{'precision': 0.9994224590190075}	{'recall': 0.99940957157452}
0.0017	18.9982	7737	0.0015	{'accuracy': 0.9996929218486105}	{'f1': 0.9996958969308075}	{'precision': 0.9997142857142858}	{'recall': 0.9996782496782497}
0.0377	20.0	8145	0.0007	{'accuracy': 0.9998464609243052}	{'f1': 0.999855816578732}	{'precision': 0.9998569794050343}	{'recall': 0.9998548199767712}
0.0021	20.9994	8552	0.0008	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0138	21.9988	8959	0.0006	{'accuracy': 0.9998464609243052}	{'f1': 0.999855816578732}	{'precision': 0.9998569794050343}	{'recall': 0.9998548199767712}
0.0086	22.9982	9366	0.0039	{'accuracy': 0.9989252264701366}	{'f1': 0.9989828981253953}	{'precision': 0.998990938880296}	{'recall': 0.9989800183099152}
0.0089	24.0	9774	0.0004	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0064	24.9994	10181	0.0004	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0121	25.9988	10588	0.0013	{'accuracy': 0.9998464609243052}	{'f1': 0.9998479593939164}	{'precision': 0.9998569794050343}	{'recall': 0.9998391248391248}
0.0123	26.9982	10995	0.0003	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0129	28.0	11403	0.0012	{'accuracy': 0.9995393827729157}	{'f1': 0.9995674416794638}	{'precision': 0.9995719178082192}	{'recall': 0.9995644599303136}
0.0143	28.9994	11810	0.0003	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0029	29.9988	12217	0.0030	{'accuracy': 0.9993858436972209}	{'f1': 0.9994232496258108}	{'precision': 0.999429874572406}	{'recall': 0.9994192799070848}
0.0059	30.9982	12624	0.0020	{'accuracy': 0.9996929218486105}	{'f1': 0.999703857839045}	{'precision': 0.9997142857142858}	{'recall': 0.999693944815896}
0.0026	32.0	13032	0.0002	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0097	32.9994	13439	0.0005	{'accuracy': 0.9996929218486105}	{'f1': 0.9997028008868588}	{'precision': 0.9996941515963085}	{'recall': 0.9997116355552362}
0.0035	33.9988	13846	0.0002	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0132	34.9982	14253	0.0008	{'accuracy': 0.9996929218486105}	{'f1': 0.9997116397752187}	{'precision': 0.999710312862109}	{'recall': 0.9997136311569301}
0.0011	36.0	14661	0.0029	{'accuracy': 0.9995393827729157}	{'f1': 0.9995410021285396}	{'precision': 0.9995192307692308}	{'recall': 0.9995644599303136}
0.006	36.9994	15068	0.0002	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0038	37.9988	15475	0.0002	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0125	38.9982	15882	0.0002	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.002	40.0	16290	0.0007	{'accuracy': 0.9998464609243052}	{'f1': 0.9998479593939164}	{'precision': 0.9998569794050343}	{'recall': 0.9998391248391248}
0.0017	40.9994	16697	0.0002	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0002	41.9988	17104	0.0003	{'accuracy': 0.9998464609243052}	{'f1': 0.999855816578732}	{'precision': 0.9998569794050343}	{'recall': 0.9998548199767712}
0.0069	42.9982	17511	0.0005	{'accuracy': 0.9998464609243052}	{'f1': 0.999855816578732}	{'precision': 0.9998569794050343}	{'recall': 0.9998548199767712}
0.0008	44.0	17919	0.0001	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0129	44.9994	18326	0.0002	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0184	45.9988	18733	0.0001	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0003	46.9982	19140	0.0001	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0095	48.0	19548	0.0001	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0003	48.9994	19955	0.0001	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}
0.0002	49.9693	20350	0.0001	{'accuracy': 1.0}	{'f1': 1.0}	{'precision': 1.0}	{'recall': 1.0}

Framework versions

• Transformers 4.43.3
• Pytorch 2.3.1
• Datasets 2.20.0
• Tokenizers 0.19.1