ConvNextV2-large-DogBreed

This model is a fine-tuned version of facebook/convnextv2-large-22k-224 on dog breed classification dataset. It achieves the following results on the evaluation set:

• Loss: 0.5469
• Accuracy: 0.9139

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: 1e-05
• train_batch_size: 16
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 32
• total_train_batch_size: 512
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
4.8578	1.0	13	4.6940	0.0671
4.6332	1.99	26	4.4169	0.1784
4.4095	2.99	39	4.1105	0.3485
3.8841	3.98	52	3.7581	0.5198
3.5964	4.98	65	3.3647	0.6647
3.2781	5.97	78	2.9442	0.7677
2.6006	6.97	91	2.5252	0.8180
2.2638	7.96	104	2.1256	0.8467
1.9609	8.96	117	1.7626	0.8766
1.3962	9.95	130	1.4453	0.9042
1.143	10.95	143	1.1818	0.9102
0.9423	11.94	156	0.9697	0.9138
0.7674	12.94	169	0.8097	0.9174
0.5007	13.93	182	0.6922	0.9186
0.4097	14.93	195	0.5999	0.9162
0.3392	16.0	209	0.5174	0.9269
0.2285	17.0	222	0.4685	0.9257
0.184	17.99	235	0.4337	0.9210
0.1587	18.99	248	0.4058	0.9257
0.1112	19.98	261	0.3824	0.9222
0.0967	20.98	274	0.3712	0.9150
0.0838	21.97	287	0.3584	0.9186
0.0665	22.97	300	0.3468	0.9174
0.0589	23.96	313	0.3428	0.9186
0.0551	24.96	326	0.3364	0.9186
0.0512	25.95	339	0.3334	0.9162
0.0441	26.95	352	0.3278	0.9210
0.0428	27.94	365	0.3275	0.9150
0.0387	28.94	378	0.3237	0.9210
0.036	29.93	391	0.3242	0.9150
0.0337	30.93	404	0.3204	0.9186
0.0328	32.0	418	0.3176	0.9198
0.0304	33.0	431	0.3183	0.9162
0.0283	33.99	444	0.3150	0.9210
0.029	34.99	457	0.3168	0.9174
0.0264	35.98	470	0.3146	0.9174
0.0259	36.98	483	0.3162	0.9174
0.0258	37.97	496	0.3126	0.9186
0.0251	38.97	509	0.3131	0.9174
0.0239	39.96	522	0.3145	0.9186
0.0234	40.96	535	0.3120	0.9198
0.023	41.95	548	0.3102	0.9198
0.0226	42.95	561	0.3123	0.9198
0.0222	43.94	574	0.3140	0.9186
0.0225	44.94	587	0.3119	0.9186
0.0215	45.93	600	0.3106	0.9198
0.0209	46.93	613	0.3113	0.9198
0.0212	48.0	627	0.3115	0.9198
0.021	49.0	640	0.3113	0.9198
0.0212	49.76	650	0.3113	0.9198

Framework versions

• Transformers 4.34.1
• Pytorch 2.1.0+cu118
• Datasets 2.14.6
• Tokenizers 0.14.1