v1.0.8

版本发布时间: 2024-07-29 13:18:26

huggingface/pytorch-image-models最新发布版本:v1.0.9(2024-08-24 07:42:07)

Add mobilenet_edgetpu_v2_m weights w/ ra4 mnv4-small based recipe. 80.1% top-1 @ 224 and 80.7 @ 256.
Release 1.0.8

More MobileNet-v4 weights, ImageNet-12k pretrain w/ fine-tunes, and anti-aliased ConvLarge models

model	top1	top1_err	top5	top5_err	param_count	img_size
mobilenetv4_conv_aa_large.e230_r448_in12k_ft_in1k	84.99	15.01	97.294	2.706	32.59	544
mobilenetv4_conv_aa_large.e230_r384_in12k_ft_in1k	84.772	15.228	97.344	2.656	32.59	480
mobilenetv4_conv_aa_large.e230_r448_in12k_ft_in1k	84.64	15.36	97.114	2.886	32.59	448
mobilenetv4_conv_aa_large.e230_r384_in12k_ft_in1k	84.314	15.686	97.102	2.898	32.59	384
mobilenetv4_conv_aa_large.e600_r384_in1k	83.824	16.176	96.734	3.266	32.59	480
mobilenetv4_conv_aa_large.e600_r384_in1k	83.244	16.756	96.392	3.608	32.59	384
mobilenetv4_hybrid_medium.e200_r256_in12k_ft_in1k	82.99	17.01	96.67	3.33	11.07	320
mobilenetv4_hybrid_medium.e200_r256_in12k_ft_in1k	82.364	17.636	96.256	3.744	11.07	256

Impressive MobileNet-V1 and EfficientNet-B0 baseline challenges (https://huggingface.co/blog/rwightman/mobilenet-baselines)

model	top1	top1_err	top5	top5_err	param_count	img_size
efficientnet_b0.ra4_e3600_r224_in1k	79.364	20.636	94.754	5.246	5.29	256
efficientnet_b0.ra4_e3600_r224_in1k	78.584	21.416	94.338	5.662	5.29	224
mobilenetv1_100h.ra4_e3600_r224_in1k	76.596	23.404	93.272	6.728	5.28	256
mobilenetv1_100.ra4_e3600_r224_in1k	76.094	23.906	93.004	6.996	4.23	256
mobilenetv1_100h.ra4_e3600_r224_in1k	75.662	24.338	92.504	7.496	5.28	224
mobilenetv1_100.ra4_e3600_r224_in1k	75.382	24.618	92.312	7.688	4.23	224

Prototype of set_input_size() added to vit and swin v1/v2 models to allow changing image size, patch size, window size after model creation.
Improved support in swin for different size handling, in addition to set_input_size, always_partition and strict_img_size args have been added to __init__ to allow more flexible input size constraints
Fix out of order indices info for intermediate 'Getter' feature wrapper, check out or range indices for same.
Add several tiny < .5M param models for testing that are actually trained on ImageNet-1k

model	top1	top1_err	top5	top5_err	param_count	img_size	crop_pct
test_efficientnet.r160_in1k	47.156	52.844	71.726	28.274	0.36	192	1.0
test_byobnet.r160_in1k	46.698	53.302	71.674	28.326	0.46	192	1.0
test_efficientnet.r160_in1k	46.426	53.574	70.928	29.072	0.36	160	0.875
test_byobnet.r160_in1k	45.378	54.622	70.572	29.428	0.46	160	0.875
test_vit.r160_in1k	42.0	58.0	68.664	31.336	0.37	192	1.0
test_vit.r160_in1k	40.822	59.178	67.212	32.788	0.37	160	0.875

model	top1	top1_err	top5	top5_err	param_count	img_size
mobilenetv4_hybrid_large.ix_e600_r384_in1k	84.356	15.644	96.892	3.108	37.76	448
mobilenetv4_hybrid_large.ix_e600_r384_in1k	83.990	16.010	96.702	3.298	37.76	384
mobilenetv4_hybrid_medium.ix_e550_r384_in1k	83.394	16.606	96.760	3.240	11.07	448
mobilenetv4_hybrid_medium.ix_e550_r384_in1k	82.968	17.032	96.474	3.526	11.07	384
mobilenetv4_hybrid_medium.ix_e550_r256_in1k	82.492	17.508	96.278	3.722	11.07	320
mobilenetv4_hybrid_medium.ix_e550_r256_in1k	81.446	18.554	95.704	4.296	11.07	256