['RN50', 'RN101', 'RN50x4', 'RN50x16', 'RN50x64', 'ViT-B/32', 'ViT-B/16', 'ViT-L/14', 'ViT-L/14@336px']clip.load(name, device=..., jit=False)根据clip.available_models()返回的模型名称,返回模型(nn.model)以及模型所需的TorchVision变换。如有必要,它将下载模型。name参数也...
clip.load(name, device=..., jit=False) Returns the model and the TorchVision transform needed by the model, specified by the model name returned by clip.available_models(). It will download the model as necessary. The name argument can also be a path to a local checkpoint. The device ...
clip.load() defload(name:str, device:Union[str, torch.device] ="cuda"iftorch.cuda.is_available()else"cpu", jit:bool=False, download_root:str=None): load函数可以加载与训练好的模型,它的输入包含以下几个参数。 name: 这个name必须是在_MODELS这个字典中存在的。 device:你要使用的device。 jit:...
jit:布尔值。是否加载优化的JIT模型。 download_root:字符串。用于指定下载的模型的保存地址,默认值如下代码所示。 model, proprecess = clip.load(name="RN50", device="cpu", jit=False, download_root="~/.cache/clip") 1.
CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image - CLIP/clip/clip.py at main · Ageliss/CLIP
python tests/util_test.py --model RN50 RN101 --save_model_list models.txt --git_revision 9d31b2ec4df6d8228f370ff20c8267ec6ba39383 WARNING: This will invoke git and modify your working tree, but will reset it to the current state after data has been generated!
clip.load(name, device=..., jit=False) Returns the model and the TorchVision transform needed by the model, specified by the model name returned by clip.available_models(). It will download the model as necessary. The name argument can also be a path to a local checkpoint. The device ...
clip.load(name, device=..., jit=False) Returns the model and the TorchVision transform needed by the model, specified by the model name returned by clip.available_models(). It will download the model as necessary. The name argument can also be a path to a local checkpoint. The device ...
clip.load(name, device=..., jit=False) Returns the model and the TorchVision transform needed by the model, specified by the model name returned by clip.available_models(). It will download the model as necessary. The name argument can also be a path to a local checkpoint. The device ...
train_data.csv" \ --val-data="/path/to/validation_data.csv" \ --csv-img-key filepath \ --csv-caption-key title \ --imagenet-val=/path/to/imagenet/root/val/ \ --warmup 10000 \ --batch-size=128 \ --lr=1e-3 \ --wd=0.1 \ --epochs=30 \ --workers=8 \ --model RN50...