Comments (1)
I'm glad you're finding m3 useful. We welcome pull requests and optimizations. The main thing I would do is pack images into batches. There's not enough detail here on how you're using m3, but if you do all your preprocessing and downloading of the images first, m3 will batch process the inference stage.
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Related Issues (20)
- Incompatibility with Torch 1.7.0 HOT 3
- id in the results HOT 1
- Incompatibility with pytorch 1.8.0 HOT 2
- Support Different Languages Outside the EU? HOT 2
- Error fetching images will fail the infer method HOT 5
- Add streaming option to infer
- Segmentation Fault w/ transform_jsonl() HOT 12
- Commercial use HOT 1
- Predicting...0% HOT 10
- Segmentation fault for certain ids in Apple M1 computers - no problem in Apple Intel computers HOT 4
- Output file options HOT 2
- Problems with installation HOT 2
- Feature: Support v2 API data as input HOT 2
- Infer_ad and infer_username can't work well HOT 1
- Error:"profile_image_url_https" HOT 4
- Query regarding 'id' HOT 1
- "NameError: name 'torch' is not defined" but torch is installed and imported HOT 9
- ValueError: semaphore or lock released too many times HOT 2
- How to infer local Twitter JSON files?
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